P

InmemoryofAmosTversky

P

ContentsIntroduction

PartI.TwoSystems

1. The Characters of theStory

2.AttentionandEffort

3.TheLazyController

4.TheAssociativeMachine

5.CognitiveEase

6. Norms, Surprises, andCauses

7. A Machine for JumpingtoConclusions

8.HowJudgmentsHappen

9. Answering an EasierQuestion

PartII.Heuristicsand

Biases10. The Law of SmallNumbers

<5>11.Anchors

12. The Science of

Availability

13. Availability, Emotion,andRisk

14.TomW’sSpecialty

15.Linda:LessisMore

16.CausesTrumpStatistics

17.RegressiontotheMean

18. Taming IntuitivePredictions

PartIII.Overconfidence

19. The Illusion ofUnderstanding

20.TheIllusionofValidity

21.IntuitionsVs.Formulas

22. Expert Intuition:When

CanWeTrustIt?

23.TheOutsideView

24. The Engine ofCapitalism

PartIV.Choices

25.Bernoulli’sErrors

26.ProspectTheory

27.TheEndowmentEffect

28.BadEvents

29.TheFourfoldPattern

30.RareEvents

31.RiskPolicies

32.KeepingScore

33.Reversals

34.FramesandReality

PartV.TwoSelves

35.TwoSelves

36.LifeasaStory

37.ExperiencedWell-Being

38.ThinkingAboutLife

Conclusions

Appendix A:JudgmentUnderUncertainty

Appendix B: Choices,Values,andFrames

Acknowledgments

Notes

Index

P

Introduction

Every author, I suppose, hasin mind a setting in whichreaders of his or her workcould benefit from havingreadit.Mineistheproverbialoffice watercooler, whereopinions are shared andgossipisexchanged.Ihopetoenrich the vocabulary that

people use when they talkabout the judgments andchoices of others, thecompany’snewpolicies,oracolleague’s investmentdecisions.Whybeconcernedwith gossip? Because it ismuch easier, as well as farmore enjoyable, to identifyand label the mistakes ofothers than to recognize ourown. Questioning what webelieve and want is difficultat the best of times, and

especially difficult when wemostneedtodoit,butwecanbenefit from the informedopinions of others. Many ofus spontaneously anticipatehow friends and colleagueswillevaluateourchoices; thequality and content of theseanticipated judgmentstherefore matters. Theexpectation of intelligentgossip is a powerful motivefor serious self-criticism,more powerful than New

Year resolutions to improveone’s decision making atworkandathome.Tobeagooddiagnostician,

aphysicianneedstoacquirealarge set of labels fordiseases,eachofwhichbindsan idea of the illness and itssymptoms, possibleantecedents and causes,possible developments andconsequences, and possibleinterventions to cure ormitigate the illness.Learning

medicine consists in part oflearning the language ofmedicine. A deeperunderstanding of judgmentsand choices also requires aricher vocabulary than isavailable in everydaylanguage. The hope forinformed gossip is that thereare distinctive patterns in theerrors people make.Systematic errors are knownas biases, and they recurpredictably in particular

circumstances. When thehandsome and confidentspeaker bounds onto thestage, for example, you cananticipate that the audiencewill judge his commentsmore favorably than hedeserves.Theavailabilityofadiagnostic label for this bias—the halo effect—makes iteasier to anticipate,recognize,andunderstand.When you are asked what

you are thinking about, you

can normally answer. Youbelieve you know what goesoninyourmind,whichoftenconsists of one consciousthought leading inanorderlywaytoanother.Butthatisnottheonlywaythemindworks,nor indeed is that the typicalway. Most impressions andthoughts arise in yourconscious experiencewithoutyour knowing how they gotthere. You cannot tracryd>ehow you came to the belief

that there is a lamp on thedesk in front of you, or howyou detected a hint ofirritation in your spouse’svoice on the telephone, orhowyoumanaged toavoidathreaton theroadbeforeyoubecameconsciouslyawareofit. The mental work thatproduces impressions,intuitions, and manydecisions goes on in silenceinourmind.Much of the discussion in

this book is about biases ofintuition.However, the focuson error does not denigratehumanintelligence,anymorethan the attention to diseasesin medical texts denies goodhealth.Mostofusarehealthymostofthetime,andmostofourjudgmentsandactionsareappropriatemost of the time.Aswenavigateour lives,wenormally allow ourselves tobeguidedbyimpressionsandfeelings, and the confidence

we have in our intuitivebeliefs and preferences isusually justified. But notalways. We are oftenconfident even when we arewrong, and an objectiveobserver is more likely todetectourerrorsthanweare.So this is my aim for

watercooler conversations:improvetheabilitytoidentifyand understand errors ofjudgment and choice, inothers and eventually in

ourselves, by providing aricher and more preciselanguage to discuss them. Inat least some cases, anaccurate diagnosis maysuggest an intervention tolimit the damage that badjudgments and choices oftencause.

OriginsThis book presents mycurrent understanding of

judgment and decisionmaking, which has beenshaped by psychologicaldiscoveriesofrecentdecades.However, I trace the centralideastotheluckydayin1969when I asked a colleague tospeakasaguest toaseminarI was teaching in theDepartment of Psychology atthe Hebrew University ofJerusalem. Amos Tverskywasconsideredarisingstarinthe field of decision research

—indeed, in anything he did—so I knew we would havean interesting time. Manypeople who knew Amosthought he was the mostintelligent person they hadever met. He was brilliant,voluble, and charismatic. Hewas also blessed with aperfectmemoryforjokesandan exceptional ability to usethem tomake a point. Therewas never a dull momentwhen Amos was around. He

was then thirty-two; I wasthirty-five.Amos told the class about

an ongoing program ofresearch at the University ofMichigan that sought toanswer this question: Arepeople good intuitivestatisticians? We alreadyknew that people are goodintuitive grammarians: at agefour a child effortlesslyconforms to the rules ofgrammar as she speaks,

althoughshehasno idea thatsuch rules exist. Do peoplehave a similar intuitive feelfor the basic principles ofstatistics?Amosreportedthatthe answer was a qualifiedyes.Wehadalivelydebateinthe seminar and ultimatelyconcluded that aqualifiednowasabetteranswer.Amos and I enjoyed the

exchange and concluded thatintuitive statistics was aninteresting topic and that it

would be fun to explore ittogether.ThatFridaywemetfor lunch atCaféRimon, thefavorite hangout ofbohemians and professors inJerusalem, and planned astudy of the statisticalintuitions of sophisticatedresearchers. We hadconcludedintheseminarthatour own intuitions weredeficient. In spiteofyearsofteaching and using statistics,we had not developed an

intuitive sense of thereliabilityofstatisticalresultsobserved in small samples.Our subjective judgmentswerebiased:wewere far toowilling to believe researchfindings based on inadequateevidenceandpronetocollecttoo few observations in ourownresearch.Thegoalofourstudy was to examinewhether other researcherssuffered from the sameaffliction.

We prepared a survey thatincludedrealisticscenariosofstatistical issues that arise inresearch. Amos collected theresponsesofagroupofexpertparticipants in a meeting ofthe Society of MathematicalPsychology, including theauthors of two statisticaltextbooks. As expected, wefound that our expertcolleagues, like us, greatlyexaggerated the likelihoodthat the original result of an

experiment would besuccessfully replicated evenwith a small sample. Theyalsogaveverypooradvicetoa fictitious graduate studentabout the number ofobservations she needed tocollect. Even statisticianswere not good intuitivestatisticians.While writing the article

that reported these findings,Amos and I discovered thatweenjoyedworkingtogether.

Amoswasalwaysveryfunny,and inhispresence Ibecamefunny as well, so we spenthours of solid work incontinuous amusement. Thepleasurewefoundinworkingtogether made usexceptionally patient; it ismuch easier to strive forperfection when you arenever bored. Perhaps mostimportant, we checked ourcritical weapons at the door.BothAmosandIwerecritical

and argumentative, he evenmore than I, but during theyears of our collaborationneitherofuseverrejectedoutof hand anything the othersaid. Indeed,oneof thegreatjoys I found in thecollaboration was that Amosfrequently saw the point ofmy vague ideas much moreclearly than Idid.Amoswasthemorelogicalthinker,withan orientation to theory andan unfailing sense of

direction.Iwasmoreintuitiveand rooted in thepsychologyofperception,fromwhichweborrowed many ideas. Wewere sufficiently similar tounderstand each other easily,and sufficiently different tosurprise each other. Wedevelopeda routine inwhichwe spent much of ourworking days together, oftenon long walks. For the nextfourteen years ourcollaborationwasthefocusof

our lives, and the work wedid together during thoseyearswasthebesteitherofuseverdid.We quickly adopted a

practice that we maintainedformanyyears.Our researchwas a conversation, inwhichwe invented questions andjointlyexaminedourintuitiveanswers.Eachquestionwasasmall experiment, and wecarriedoutmanyexperimentsina singleday.Wewerenot

seriously looking for thecorrect answer to thestatisticalquestionsweposed.Our aim was to identify andanalyze the intuitive answer,the first one that came tomind, the one we weretempted to make even whenweknew it to bewrong.Webelieved—correctly, as ithappened—that any intuitionthat the two of us sharedwould be shared by manyotherpeopleaswell,andthat

it would be easy todemonstrate its effects onjudgments.We once discovered with

great delight that we hadidenticalsilly ideasabout thefuture professions of severaltoddlers we both knew. Wecould identify theargumentative three-year-oldlawyer, the nerdy professor,the empathetic and mildlyintrusive psychotherapist. Ofcourse thesepredictionswere

absurd, but we still foundthem appealing. It was alsoclear that our intuitionsweregoverned by the resemblanceof each child to the culturalstereotype of a profession.The amusing exercise helpedus develop a theory thatwasemerging inourmindsat thetime, about the role ofresemblance in predictions.We went on to test andelaborate that theory indozens of experiments, as in

thefollowingexample.As you consider the next

question, please assume thatStevewasselectedatrandomfromarepresentativesample:

An individual has beendescribed by a neighboras follows: “Steve isveryshyandwithdrawn,invariably helpful butwith little interest inpeopleorintheworldofreality.Ameekand tidy

soul, he has a need fororder and structurut andstre, and a passion fordetail.” Is Steve morelikelytobealibrarianorafarmer?

The resemblance of Steve’spersonality to that of astereotypical librarian strikeseveryone immediately, butequally relevant statisticalconsiderations are almostalways ignored. Did it occur

to you that there are morethan20malefarmersforeachmale librarian in the UnitedStates? Because there are somany more farmers, it isalmost certain that more“meekandtidy”soulswillbefound on tractors than atlibrary information desks.However, we found thatparticipants in ourexperiments ignored therelevant statistical facts andrelied exclusively on

resemblance. We proposedthattheyusedresemblanceasa simplifying heuristic(roughly, a rule of thumb) tomake a difficult judgment.The reliance on the heuristiccaused predictable biases(systematic errors) in theirpredictions.Onanotheroccasion,Amos

andIwonderedabouttherateof divorce among professorsinouruniversity.Wenoticedthat the question triggered a

search of memory fordivorced professorswe knewor knew about, and that wejudged the size of categoriesby the ease with whichinstances came to mind. Wecalled this reliance on theease of memory search theavailability heuristic. In oneof our studies, we askedparticipants to answer asimple question about wordsinatypicalEnglishtext:

ConsidertheletterK.Is K more likely toappear as the first letterin a word OR as thethirdletter?

As any Scrabble playerknows, it is much easier tocome up with words thatbegin with a particular letterthan to find words that havethe same letter in the thirdposition. This is true forevery letter of the alphabet.

We therefore expectedrespondentstoexaggeratethefrequencyoflettersappearingin the first position—eventhoseletters(suchasK,L,N,R, V) which in fact occurmore frequently in the thirdposition. Here again, thereliance on a heuristicproducesapredictablebiasinjudgments. For example, Irecently came to doubt mylong-held impression thatadultery is more common

amongpoliticiansthanamongphysicians or lawyers. I hadeven come up withexplanations for that “fact,”including the aphrodisiaceffect of power and thetemptationsoflifeawayfromhome. I eventually realizedthat the transgressions ofpoliticians are much morelikely tobe reported than thetransgressionsof lawyersanddoctors. My intuitiveimpression could be due

entirelytojournalists’choicesof topics and to my relianceontheavailabilityheuristic.Amos and I spent several

years studying anddocumenting biases ofintuitive thinking in varioustasks—assigningprobabilitiesto events, forecasting thefuture, assessing hypotheses,andestimatingfrequencies.Inthe fifth year of ourcollaboration, we presentedourmain findings in Science

magazine, a publication readby scholars in manydisciplines. The article(whichisreproducedinfullatthe end of this book) wastitled “Judgment UnderUncertainty: Heuristics andBiases.” It described thesimplifying shortcuts ofintuitive thinking andexplained some 20 biases asmanifestations of theseheuristics—and also asdemonstrations of the role of

heuristicsinjudgment.Historians of science have

often noted that at any giventime scholars in a particularfield tend to share basic reshareassumptionsabouttheirsubject. Social scientists areno exception; they rely on aview of human nature thatprovides the background ofmost discussions of specificbehaviors but is rarelyquestioned. Social scientistsinthe1970sbroadlyaccepted

two ideas about humannature. First, people aregenerally rational, and theirthinking is normally sound.Second, emotions such asfear, affection, and hatredexplainmostoftheoccasionsonwhich people depart fromrationality. Our articlechallenged both assumptionswithout discussing themdirectly. We documentedsystematic errors in thethinking of normal people,

andwe traced these errors tothe design of the machineryofcognitionratherthantothecorruption of thought byemotion.Our article attracted much

more attention than we hadexpected, and it remains oneof the most highly citedworksinsocialscience(morethan three hundred scholarlyarticlesreferredtoitin2010).Scholars in other disciplinesfound ituseful, and the ideas

of heuristics and biases havebeen used productively inmany fields, includingmedical diagnosis, legaljudgment, intelligenceanalysis,philosophy, finance,statistics, and militarystrategy.For example, students of

policy have noted that theavailability heuristic helpsexplain why some issues arehighly salient in the public’smind while others are

neglected. People tend toassesstherelativeimportanceof issues by the ease withwhichtheyareretrievedfrommemory—and this is largelydetermined by the extent ofcoverage in the media.Frequently mentioned topicspopulate the mind even asothers slip away fromawareness. In turn, what themedia choose to reportcorresponds to their view ofwhat is currently on the

public’s mind. It is noaccident that authoritarianregimes exert substantialpressure on independentmedia. Because publicinterestismosteasilyarousedby dramatic events and bycelebrities, media feedingfrenzies are common. Forseveral weeks after MichaelJackson’sdeath,forexample,itwasvirtually impossible tofind a television channelreportingonanothertopic.In

contrast, there is littlecoverage of critical butunexcitingissuesthatprovidelessdrama, suchasdecliningeducational standards oroverinvestment of medicalresources in the last year oflife. (As Iwrite this, Inoticethat my choice of “little-covered” examples wasguided by availability. ThetopicsIchoseasexamplesarementioned often; equallyimportant issues that are less

availabledidnotcometomymind.)Wedidnotfullyrealizeitat

thetime,butakeyreasonforthe broad appeal of“heuristics and biases”outside psychology was anincidental feature of ourwork: we almost alwaysincluded in our articles thefull text of the questions wehad asked ourselves and ourrespondents. These questionsserved as demonstrations for

the reader, allowing him torecognize how his ownthinking was tripped up bycognitive biases. I hope youhad such an experience asyou read the question aboutStevethelibrarian,whichwasintended to help youappreciate the power ofresemblance as a cue toprobability and to see howeasy it is to ignore relevantstatisticalfacts.The use of demonstrations

provided scholars fromdiverse disciplines—notablyphilosophers and economists—an unusual opportunity toobserve possible flaws intheir own thinking. Havingseen themselves fail, theybecame more likely toquestion the dogmaticassumption, prevalent at thetime, that the humanmind isrational and logical. Thechoiceofmethodwascrucial:if we had reported results of

only conventionalexperiments, the articlewould have been lessnoteworthy and lessmemorable. Furthermore,skeptical readers would havedistanced themselves fromthe results by attributingjudgment errors to thefamiliar l thefamifecklessness ofundergraduates, the typicalparticipants in psychologicalstudies.Ofcourse,wedidnot

choose demonstrations overstandardexperimentsbecausewe wanted to influencephilosophers and economists.We preferred demonstrationsbecause theyweremore fun,and we were lucky in ourchoice of method as well asin many other ways. Arecurrent theme of this bookis that luckplaysa largeroleineverystoryofsuccess;itisalmost always easy toidentifyasmallchangeinthe

story that would have turneda remarkable achievementintoamediocreoutcome.Ourstorywasnoexception.The reaction to our work

wasnotuniformlypositive.Inparticular,ourfocusonbiaseswas criticized as suggestingan unfairly negative view ofthe mind. As expected innormal science, someinvestigatorsrefinedourideasand others offered plausiblealternatives. By and large,

though, the idea that ourminds are susceptible tosystematic errors is nowgenerally accepted. Ourresearchon judgmenthadfarmoreeffecton social sciencethan we thought possiblewhenwewereworkingonit.Immediately after

completing our review ofjudgment, we switched ourattention to decision makingunder uncertainty. Our goalwas to develop a

psychological theory of howpeople make decisions aboutsimplegambles.Forexample:Would you accept a bet onthe toss of a coinwhere youwin $130 if the coin showsheads and lose $100 if itshows tails? Theseelementary choices had longbeen used to examine broadquestions about decisionmaking, such as the relativeweight that people assign tosure things and to uncertain

outcomes. Our method didnot change: we spent manydays making up choiceproblems and examiningwhether our intuitivepreferences conformed to thelogic of choice. Here again,as in judgment, we observedsystematic biases in our owndecisions, intuitivepreferences that consistentlyviolated the rules of rationalchoice. Five years after theScience article,we published

“Prospect Theory: AnAnalysis of Decision UnderRisk,”atheoryofchoicethatis by some counts moreinfluential than our work onjudgment, and is one of thefoundations of behavioraleconomics.Until geographical

separation made it toodifficulttogoon,AmosandIenjoyed the extraordinarygoodfortuneofasharedmindthat was superior to our

individual minds and of arelationship that made ourwork fun as well asproductive.Ourcollaborationon judgment and decisionmakingwasthereasonfortheNobelPrizethatIreceivedin2002, which Amos wouldhave shared had he not died,agedfifty-nine,in1996.

WherewearenowThis book is not intended as

an exposition of the earlyresearch that Amos and Iconducted together, a taskthathasbeenablycarriedoutby many authors over theyears.Mymainaimhereistopresent a view of how themind works that draws onrecent developments incognitive and socialpsychology.Oneof themoreimportant developments isthat we now understand themarvels as well as the flaws

ofintuitivethought.AmosandIdidnotaddress

accurateintuitionsbeyondthecasual statement thatjudgmentheuristics“arequiteuseful,butsometimes lead tosevereandsystematicerrors.”We focused on biases, bothbecause we found theminteresting in their own rightand because they providedevidence for theheuristicsofjudgment. We did not askourselveswhetherallintuitive

judgments under uncertaintyareproducedbytheheuristicswe studied; it is now clearthat they are not. Inparticular, the accurateintuitionsofexpertsarebetterexplained by the effects ofprolonged practice than byheuristics.We cannowdrawa richer andigha riche morebalanced picture, in whichskill and heuristics arealternative sources ofintuitive judgments and

choices.The psychologist Gary

Kleintellsthestoryofateamof firefighters that entered ahouse in which the kitchenwas on fire. Soon after theystarted hosing down thekitchen, the commanderheard himself shout, “Let’sget out of here!” withoutrealizing why. The floorcollapsedalmostimmediatelyafter the firefighters escaped.Only after the fact did the

commander realize that thefirehadbeenunusuallyquietand that his ears had beenunusuallyhot.Together,theseimpressions prompted whathe called a “sixth sense ofdanger.”Hehadnoideawhatwas wrong, but he knewsomething was wrong. Itturned out that the heart ofthe fire had not been in thekitchen but in the basementbeneath where the men hadstood.

We have all heard suchstoriesofexpertintuition:thechessmasterwhowalks pasta street game and announces“White mates in three”without stopping, or thephysician who makes acomplex diagnosis after asingle glance at a patient.Expert intuition strikes us asmagical,butitisnot.Indeed,each of us performs feats ofintuitiveexpertisemanytimeseach day. Most of us are

pitch-perfect in detectinganger in the first word of atelephone call, recognize asweenteraroomthatwewerethe subject of theconversation, and quicklyreact to subtle signs that thedriver of the car in the nextlane is dangerous. Oureveryday intuitive abilitiesarenolessmarvelousthanthestriking insights of anexperienced firefighter orphysician—only more

common.The psychology of accurate

intuition involves no magic.Perhaps the best shortstatementofitisbythegreatHerbert Simon, who studiedchess masters and showedthat after thousands of hoursof practice they come to seethe pieces on the boarddifferentlyfromtherestofus.You can feel Simon’simpatience with themythologizing of expert

intuition when he writes:“Thesituationhasprovidedacue; this cue has given theexpert access to informationstored in memory, and theinformation provides theanswer. Intuition is nothingmore and nothing less thanrecognition.”Wearenotsurprisedwhena

two-year-old looks at a dogand says “doggie!” becauseweareusedto themiracleofchildrenlearningtorecognize

and name things. Simon’spoint is that the miracles ofexpertintuitionhavethesamecharacter. Valid intuitionsdevelop when experts havelearned to recognize familiarelements in a new situationandtoactinamannerthatisappropriate to it. Goodintuitive judgments come tomind with the sameimmediacyas“doggie!”Unfortunately,

professionals’ intuitions do

not all arise from trueexpertise. Many years ago Ivisited the chief investmentofficer of a large financialfirm,whotoldmethathehadjust invested some tens ofmillions of dollars in thestock of Ford MotorCompany.WhenIaskedhowhehadmadethatdecision,hereplied that he had recentlyattended an automobile showand had been impressed.“Boy, do they know how to

make a car!” was hisexplanation.Hemade it veryclear that he trusted his gutfeelingandwassatisfiedwithhimselfandwithhisdecision.I found it remarkable that hehadapparentlynotconsideredthe one question that aneconomist would callrelevant: Is Ford stockcurrently underpriced?Instead,hehadlistenedtohisintuition;helikedthecars,heliked the company, and he

liked the idea of owning itsstock. From what we knowabout the accuracy of stockpicking, it is reasonable tobelieve that he did not knowwhathewasdoing.The specific heuristics that

Amos and I studiedproviheitudied de little helpin understanding how theexecutive came to invest inFord stock, but a broaderconception of heuristics nowexists, which offers a good

account. An importantadvance is that emotion nowlooms much larger in ourunderstanding of intuitivejudgmentsandchoicesthanitdid in the past. Theexecutive’s decision wouldtoday be described as anexample of the affectheuristic, where judgmentsand decisions are guideddirectly by feelings of likingand disliking, with littledeliberationorreasoning.

When confronted with aproblem—choosing a chessmove or decidingwhether toinvest in a stock—themachinery of intuitivethoughtdoesthebestitcan.Ifthe individual has relevantexpertise, she will recognizethesituation,andtheintuitivesolution that comes to hermind is likely to be correct.This iswhathappenswhenachess master looks at acomplex position: the few

movesthatimmediatelyoccurto him are all strong. Whenthequestionisdifficultandaskilled solution is notavailable, intuition still has ashot:ananswermaycometomindquickly—butitisnotananswer to the originalquestion. The question thatthe executive faced (should Iinvest in Ford stock?) wasdifficult,buttheanswertoaneasier and related question(do I like Ford cars?) came

readily to his mind anddeterminedhischoice.Thisisthe essence of intuitiveheuristics:whenfacedwithadifficult question, we oftenansweraneasierone instead,usually without noticing thesubstitution.The spontaneous search for

an intuitive solutionsometimes fails—neither anexpertsolutionnoraheuristicanswer comes to mind. Insuch cases we often find

ourselves switching to aslower, more deliberate andeffortful form of thinking.This is the slow thinking ofthe title. Fast thinkingincludes both variants ofintuitive thought—the expertand the heuristic—aswell asthe entirely automaticmentalactivities of perception andmemory, the operations thatenableyoutoknowthereisalamponyourdeskorretrievethe name of the capital of

Russia.Thedistinctionbetweenfast

and slow thinking has beenexplored by manypsychologists over the lasttwenty-five years. Forreasons that I explain morefully in the next chapter, Idescribe mental life by themetaphor of two agents,called System 1 and System2,whichrespectivelyproducefast and slow thinking. Ispeak of the features of

intuitive and deliberatethought as if theywere traitsand dispositions of twocharacters in your mind. Inthepicturethatemergesfromrecent research, the intuitiveSystem 1 is more influentialthan your experience tellsyou,anditisthesecretauthorof many of the choices andjudgmentsyoumake.Mostofthis book is about theworkingsofSystem1andthemutual influences between it

andSystem2.

WhatComesNextThebook isdivided intofiveparts. Part 1 presents thebasic elements of a two-systems approach tojudgment and choice. Itelaborates the distinctionbetween the automaticoperations of System 1 andthe controlled operations ofSystem 2, and shows how

associativememory, the coreof System 1, continuallyconstructs a coherentinterpretation of what isgoingon inourworldat anyinstant. I attempt to give asense of the complexity andrichnessoftheautomaticandoften unconscious processesthat underlie intuitivethinking, and of how theseautomatic processes explaintheheuristicsof judgment.Agoal is to introduce a

language for thinking andtalkingaboutthemind.Part 2 updates the study of

judgment heuristics andexploresamajorpuzzle:Whyisitsodifficultforustothinkstatistically?We easily thinkassociativelm 1associay, wethink metaphorically, wethink causally, but statisticsrequires thinkingaboutmanythings at once, which issomething that System 1 isnotdesignedtodo.

Thedifficultiesofstatisticalthinking contribute to themain theme of Part 3,whichdescribes a puzzlinglimitation of our mind: ourexcessive confidence inwhatwebelieveweknow,andourapparent inability toacknowledge the full extentof our ignorance and theuncertainty of the world welive in. We are prone tooverestimate how much weunderstand about the world

and tounderestimate the roleof chance in events.Overconfidence is fed by theillusory certainty ofhindsight. My views on thistopichavebeeninfluencedbyNassim Taleb, the author ofThe Black Swan. I hope forwatercooler conversationsthat intelligently explore thelessons that can be learnedfrom the past while resistingthe lure of hindsight and theillusionofcertainty.

The focus of part 4 is aconversation with thediscipline of economics onthenatureofdecisionmakingand on the assumption thateconomicagents are rational.This section of the bookprovides a current view,informed by the two-systemmodel,ofthekeyconceptsofprospecttheory, themodelofchoice that Amos and Ipublished in 1979.Subsequent chapters address

several ways human choicesdeviate from the rules ofrationality. I deal with theunfortunate tendency to treatproblems in isolation, andwith framing effects, wheredecisions are shaped byinconsequential features ofchoice problems. Theseobservations, which arereadily explained by thefeatures ofSystem1, presenta deep challenge to therationality assumption

favored in standardeconomics.Part 5 describes recent

researchthathasintroducedadistinction between twoselves, the experiencing selfand the remembering self,which do not have the sameinterests. For example, wecan expose people to twopainful experiences. One ofthese experiences is strictlyworsethantheother,becauseitislonger.Buttheautomatic

formation of memories—afeature of System 1—has itsrules, which we can exploitso that the worse episodeleaves a better memory.When people later choosewhichepisode to repeat, theyare,naturally,guidedbytheirremembering self andexposethemselves (theirexperiencing self) tounnecessary pain. Thedistinction between twoselves is applied to the

measurement of well-being,wherewefindagainthatwhatmakes the experiencing selfhappyisnotquitethesameaswhat satisfies theremembering self. How twoselves within a single bodycan pursue happiness raisessomedifficultquestions,bothfor individuals and forsocieties that view the well-being of the population as apolicyobjective.A concluding chapter

explores,inreverseorder,theimplications of threedistinctions drawn in thebook: between theexperiencing and therememberingselves,betweenthe conception of agents inclassical economics and inbehavioral economics (whichborrows from psychology),and between the automaticSystem 1 and the effortfulSystem 2. I return to thevirtues of educating gossip

and to what organizationsmight do to improve thequality of judgments anddecisions that are made ontheirbehalf.Two articles I wrote with

Amos are reproduced asappendixes to the book. Thefirstisthereviewofjudgmentunder uncertainty that Idescribedearlier.Thesecond,published in 1984,summarizes prospect theoryas well as our studies of

framing effects. The articlespresent the contributions thatwere cited by the Nobelcommittee—and youmay besurprisedbyhowsimpletheyare. Reading them will giveyouasenseofhowmuchweknew a long time ago, andalso of how much we havelearnedinrecentdecades.

P

Part1

P

TwoSystemsP

TheCharactersoftheStory

To observe your mind inautomaticmode,glanceattheimagebelow.

Figure1

Your experience as you lookat the woman’s faceseamlesslycombineswhatwenormally call seeing andintuitive thinking. As surelyand quickly as you saw thatthe young woman’s hair isdark,youknewshe is angry.Furthermore, what you sawextendedintothefuture.Yousensed that this woman isabout to say some veryunkind words, probably in aloud and strident voice. A

premonition ofwhat shewasgoing to do next came tomind automatically andeffortlessly. You did notintend to assess hermood orto anticipate what she mightdo, and your reaction to thepicture did not have the feelof something you did. It justhappened to you. It was aninstanceoffastthinking.Now look at the following

problem:

17×24

You knew immediately thatthis is a multiplicationproblem, and probably knewthat you could solve it, withpaper and pencil, if notwithout. You also had somevague intuitive knowledge ofthe range of possible results.You would be quick torecognize that both 12,609

and 123 are implausible.Without spending some timeontheproblem,however,youwould not be certain that theanswer is not 568.A precisesolution did not come tomind, and you felt that youcould choose whether or nottoengageinthecomputation.If you have not done so yet,you should attempt themultiplication problem now,completingatleastpartofit.You experienced slow

thinking as you proceededthrough a sequence of steps.You first retrieved frommemory the cognitiveprogram for multiplicationthat you learned in school,then you implemented it.Carryingout thecomputationwas a strain. You felt theburden of holding muchmaterial in memory, as youneededtokeeptrackofwhereyou were and of where youweregoing,whileholdingon

totheintermediateresult.Theprocess was mental work:deliberate, effortful, andorderly—a prototype of slowthinking. The computationwasnotonlyaneventinyourmind; your body was alsoinvolved. Your musclestensed up, your bloodpressure rose, and your heartrate increased. Someonelooking closely at your eyeswhile you tackled thisproblem would have seen

your pupils dilate. Yourpupils contracted back tonormal size as soon as youended yourwork—when youfound the answer (which is408,bytheway)orwhenyougaveup.

TwoSystemsPsychologists have beenintensely interested forseveral decades in the twomodagee fi Pn="cees of

thinking evoked by thepicture of the angry womanand by the multiplicationproblem, and have offeredmanylabelsforthem.Iadoptterms originally proposed bythe psychologists KeithStanovich andRichardWest,andwill refer to twosystemsin the mind, System 1 andSystem2.

System 1 operates

automatically andquickly,with littleornoeffort and no sense ofvoluntarycontrol.System 2 allocatesattention to the effortfulmental activities thatdemand it, includingcomplex computations.The operations ofSystem 2 are oftenassociated with thesubjective experience ofagency, choice, and

concentration.

The labels of System 1 andSystem 2 are widely used inpsychology, but I go furtherthanmostinthisbook,whichyou can read as apsychodrama with twocharacters.Whenwethinkofourselves,

we identify with System 2,the conscious, reasoning selfthat has beliefs, makes

choices, and decides what tothink about and what to do.Although System 2 believesitself to be where the actionis, the automaticSystem1 isthe hero of the book. Idescribe System 1 aseffortlessly originatingimpressions and feelings thatare the main sources of theexplicitbeliefsanddeliberatechoices of System 2. Theautomatic operations ofSystem 1 generate

surprisinglycomplexpatternsof ideas, but only the slowerSystem 2 can constructthoughts in an orderly seriesof steps. I also describecircumstances in whichSystem 2 takes over,overruling the freewheelingimpulses and associations ofSystem1.Youwillbeinvitedtothinkofthetwosystemsasagents with their individualabilities, limitations, andfunctions.

In rough order ofcomplexity, here are someexamples of the automaticactivitiesthatareattributedtoSystem1:

Detect thatoneobject ismore distant thananother.Orienttothesourceofasuddensound.Complete the phrase“breadand…”

Make a “disgust face”when shown a horriblepicture.Detect hostility in avoice.Answerto2+2=?Read words on largebillboards.Driveacaronanemptyroad.Find a strong move inchess(ifyouareachessmaster).Understand simple

sentences.Recognize that a “meekand tidy soul with apassion for detail”resembles anoccupationalstereotype.

All these mental eventsbelongwiththeangrywoman—they occur automaticallyandrequirelittleornoeffort.The capabilities of System 1include innate skills that we

sharewithotheranimals.Wearebornpreparedtoperceivethe world around us,recognize objects, orientattention, avoid losses, andfear spiders. Other mentalactivities become fast andautomatic through prolongedpractice. System 1 haslearned associations betweenideas(thecapitalofFrance?);ithasalsolearnedskillssuchas reading and understandingnuances of social situations.

Some skills, such as findingstrong chess moves, areacquired only by specializedexperts. Others are widelyshared. Detecting thesimilarity of a personalitysketch to an occupatioheinoccupatnal stereotyperequires broad knowledge ofthe language and the culture,which most of us possess.The knowledge is stored inmemoryandaccessedwithoutintentionandwithouteffort.

Several of the mentalactions in the list arecompletely involuntary. Youcannot refrain fromunderstanding simplesentences in your ownlanguageorfromorientingtoaloudunexpectedsound,norcan you prevent yourselffromknowing that2+2=4or from thinking of Pariswhen thecapitalofFrance ismentioned. Other activities,such as chewing, are

susceptible to voluntarycontrol but normally run onautomatic pilot. The controlof attention is shared by thetwo systems. Orienting to aloud sound is normally aninvoluntary operation ofSystem1,whichimmediatelymobilizes the voluntaryattention of System 2. Youmay be able to resist turningtoward the source of a loudand offensive comment at acrowded party, but even if

your head does not move,your attention is initiallydirected to it, at least for awhile.However,attentioncanbe moved away from anunwantedfocus,primarilybyfocusing intently on anothertarget.The highly diverse

operations of System 2 haveone feature in common: theyrequire attention and aredisrupted when attention isdrawn away. Here are some

examples:

Braceforthestarterguninarace.Focus attention on theclownsinthecircus.Focus on the voice of aparticular person in acrowded and noisyroom.Look for a womanwithwhitehair.Search memory to

identify a surprisingsound.Maintain a fasterwalking speed than isnaturalforyou.Monitor theappropriateness of yourbehavior in a socialsituation.Counttheoccurrencesofthe lettera in a page oftext.Tell someone yourphonenumber.

Park in a narrow space(for most people exceptgarageattendants).Compare two washingmachines for overallvalue.Filloutataxform.Check the validity of acomplex logicalargument.

In all these situations youmust pay attention, and you

willperformlesswell,ornotatall, ifyouarenot readyorif your attention is directedinappropriately.System2hassome ability to change theway System 1 works, byprogramming the normallyautomatic functions ofattention andmemory.Whenwaiting for a relative at abusy train station, forexample,youcansetyourselfat will to look for a white-haired woman or a bearded

man,andtherebyincreasethelikelihood of detecting yourrelative fromadistance.Youcan set your memory tosearch for capital cities thatstart with N or for Frenchexistentialist novels. Andwhen you rent a car atLondon’s Heathrow Airport,the attendant will probablyremindyouthat“wedriveonthe left side of the road overhere.” In all these cases, youare asked to do something

that doesnot comenaturally,and you will find that theconsistent maintenance of aset requires continuousexertion of at least someeffort.The often-used phrase “pay

attention” is apt:youdisposeof a limited budget ofattentionthatyoucanallocatetoactivities,andifyoutrytoi>Cyou try tgo beyond yourbudget,youwillfail.Itisthemark of effortful activities

that they interfere with eachother, which is why it isdifficult or impossible toconduct several at once.Youcould not compute theproduct of 17 × 24 whilemakinga left turn intodensetraffic, and you certainlyshould not try. You can doseveral things at once, butonly if they are easy andundemanding. You areprobably safe carrying on aconversationwithapassenger

while driving on an emptyhighway, and many parentshave discovered, perhapswithsomeguilt,thattheycanread a story to a child whilethinkingofsomethingelse.Everyone has some

awareness of the limitedcapacityofattention,andoursocial behavior makesallowances for theselimitations. When the driverofacar isovertakinga truckon a narrow road, for

example, adult passengersquite sensibly stop talking.They know that distractingthedriver isnotagood idea,and they also suspect that heis temporarily deaf and willnothearwhattheysay.Intense focusing on a task

can make people effectivelyblind, even to stimuli thatnormally attract attention.The most dramaticdemonstrationwasofferedbyChristopher Chabris and

Daniel Simons in their bookThe Invisible Gorilla. Theyconstructed a short film oftwo teams passingbasketballs,oneteamwearingwhite shirts, the otherwearing black. The viewersof the film are instructed tocount the number of passesmade by the white team,ignoring the black players.This task is difficult andcompletely absorbing.Halfway through thevideo,a

womanwearingagorillasuitappears, crosses the court,thumpsher chest, andmoveson.Thegorilla is inviewfor9seconds.Manythousandsofpeople have seen the video,andabouthalfofthemdonotnotice anythingunusual. It isthe counting task—andespecially the instruction toignoreoneoftheteams—thatcauses the blindness.No onewho watches the videowithout that taskwouldmiss

the gorilla. Seeing andorienting are automaticfunctions of System 1, buttheydependontheallocationof some attention to therelevant stimulus. Theauthors note that the mostremarkable observation oftheirstudyisthatpeoplefindits results very surprising.Indeed, the viewers who failto see thegorilla are initiallysure that it was not there—they cannot imagine missing

such a striking event. Thegorilla study illustrates twoimportant facts about ourminds:wecanbeblindtotheobvious, and we are alsoblindtoourblindness.

PlotSynopsisThe interaction of the twosystems is a recurrent themeof the book, and a briefsynopsis of the plot is inorder. In the story Iwill tell,

Systems 1 and 2 are bothactive whenever we areawake. System 1 runsautomaticallyandSystem2isnormally in a comfortablelow-effort mode, in whichonlyafractionofitscapacityis engaged. System 1continuously generatessuggestions for System 2:impressions, intuitions,intentions, and feelings. Ifendorsed by System 2,impressions and intuitions

turnintobeliefs,andimpulsesturn into voluntary actions.When all goes smoothly,which is most of the time,System 2 adopts thesuggestionsofSystem1withlittleornomodification.Yougenerally believe yourimpressions and act on yourdesires, and that is fine—usually.When System 1 runs into

difficulty,itcallsonSystem2to support more detailed and

specific processing that maysolve the problem of themoment. System 2 ismobilized when a questionarises for which System 1does not offer an answer, asprobably happened to youwhen you encountered themultiplication problem 17 ×24.Youcanalsofeelasurgeof conscious attentionwhenever you are surprised.System 2 is activ">< 2 isactated when an event is

detected that violates themodel of the world thatSystem 1 maintains. In thatworld, lamps do not jump,catsdonotbark,andgorillasdonotcrossbasketballcourts.The gorilla experimentdemonstrates that someattention is needed for thesurprising stimulus to bedetected. Surprise thenactivates and orients yourattention: youwill stare, andyouwillsearchyourmemory

for a story that makes senseof the surprising event.System2isalsocreditedwiththe continuousmonitoring ofyour own behavior—thecontrol that keeps you politewhenyouareangry,andalertwhenyouaredrivingatnight.System 2 is mobilized toincreased effort when itdetects an error about to bemade. Remember a timewhen you almost blurted outanoffensiveremarkandnote

how hard you worked torestore control. In summary,most of what you (yourSystem 2) think and dooriginates in your System 1,butSystem2takesoverwhenthings get difficult, and itnormallyhasthelastword.The division of labor

between System 1 andSystem2ishighlyefficient:itminimizes effort andoptimizes performance. Thearrangementworkswellmost

ofthetimebecauseSystem1isgenerallyverygoodatwhatitdoes:itsmodelsoffamiliarsituations are accurate, itsshort-term predictions areusually accurate aswell, andits initial reactions tochallenges are swift andgenerallyappropriate.System1 has biases, however,systematic errors that it isprone to make in specifiedcircumstances. As we shallsee, it sometimes answers

easier questions than the oneitwas asked, and it has littleunderstanding of logic andstatistics. One furtherlimitationofSystem1 is thatitcannotbeturnedoff.Ifyouare shown a word on thescreen in a language youknow, you will read it—unlessyourattentionistotallyfocusedelsewhere.

Conflict

Figure 2 is a variant of aclassic experiment thatproduces a conflict betweenthe two systems.You shouldtry the exercise beforereadingon.

Figure2

You were almost certainlysuccessful in saying thecorrect words in both tasks,and you surely discoveredthat some parts of each taskweremucheasierthanothers.When you identified upper-and lowercase, the left-handcolumn was easy and theright-hand column causedyou to slow down and

perhaps to stammer orstumble. When you namedthe position of words, theleft-hand column wasdifficult and the right-handcolumnwasmucheasier.These tasks engage System

2, because saying“upper/lower” or “right/left”is not what you routinely dowhenlookingdownacolumnof words. One of the thingsyoudidtosetyourselfforthetask was to program your

memory so that the relevantwords (upper and lower forthefirsttask)were“onthetipof your tongue.” Theprioritizing of the chosenwords is effective and themild temptation to readotherwordswasfairlyeasytoresistwhen you went through thefirst column. But the secondcolumn was different,because it contained wordsfor which you were set, andyou could not ignore them.

You were mostly able torespond correctly, butovercoming the competingresponse was a strain, and itslowed you down. Youexperienced a conflictbetween a task that youintended to carry out and anautomatic response thatinterferedwithit.Conflict between an

automatic reaction and anintention to conWhetion toctrol it is common in our

lives.Weareallfamiliarwiththeexperienceoftryingnottostare at the oddly dressedcouple at the neighboringtableinarestaurant.Wealsoknowwhat it is like to forceour attention on a boringbook, when we constantlyfindourselvesreturningtothepoint at which the readinglost its meaning. Wherewinters are hard, manydrivers have memories oftheir car skidding out of

control on the ice and of thestruggle to follow well-rehearsed instructions thatnegate what they wouldnaturally do: “Steer into theskid,andwhateveryoudo,donot touch the brakes!” Andevery human being has hadthe experience of not tellingsomeonetogotohell.Oneofthe tasks of System 2 is toovercome the impulses ofSystem 1. In other words,System2isinchargeofself-

control.

IllusionsTo appreciate the autonomyof System 1, as well as thedistinction betweenimpressions and beliefs, takeagoodlookatfigure3.This picture is

unremarkable: twohorizontallinesofdifferentlengths,withfins appended, pointing indifferent directions. The

bottom line is obviouslylonger than the one above it.That is what we all see, andwenaturallybelievewhatwesee. If you have alreadyencountered this image,however, you recognize it asthe famous Müller-Lyerillusion. As you can easilyconfirm by measuring themwith a ruler, the horizontallines are in fact identical inlength.

Figure3

Now that you have

measured the lines, you—yourSystem2, theconsciousbeing you call “I”—have anewbelief:youknowthatthelines are equally long. Ifasked about their length, youwill saywhatyouknow.Butyou still see the bottom lineaslonger.Youhavechosentobelieve themeasurement,butyoucannotpreventSystem1from doing its thing; youcannotdecidetoseethelinesas equal, although you know

they are. To resist theillusion, there is only onething you can do: you mustlearn to mistrust yourimpressions of the length oflines when fins are attachedto them. To implement thatrule, you must be able torecognize the illusory patternand recall what you knowabout it. If you can do this,you will never again befooled by the Müller-Lyerillusion.Butyouwillstillsee

one line as longer than theother.Not all illusions are visual.

Thereareillusionsofthought,which we call cognitiveillusions. As a graduatestudent, I attended somecoursesontheartandscienceofpsychotherapy.Duringoneof these lectures, our teacherimparted amorsel of clinicalwisdom.This iswhathe toldus: “You will from time totime meet a patient who

shares a disturbing tale ofmultiple mistakes in hisprevious treatment. He hasbeen seen by severalclinicians, andall failedhim.The patient can lucidlydescribe how his therapistsmisunderstood him, but hehas quickly perceived thatyou are different. You sharethe same feeling, areconvinced that youunderstand him, and will beable to help.” At this point

myteacherraisedhisvoiceashesaid,“Donoteventhinkoftakingon thispatient!Throwhim out of the office! He ismost likelyapsychopathandyou will not be able to helphim.”Many years later I learned

that the teacher had warnedus against psychopathiccharm, and the leadingauthority in the strn y in theudy of psychopathyconfirmed that the teacher’s

advice was sound. Theanalogy to the Müller-Lyerillusion is close. What wewere being taught was nothowtofeelaboutthatpatient.Our teacher took it forgrantedthatthesympathywewould feel for the patientwould not be under ourcontrol; it would arise fromSystem 1. Furthermore, wewere not being taught to begenerally suspicious of ourfeelings about patients. We

were told that a strongattraction to a patient with arepeated history of failedtreatment is a danger sign—like the fins on the parallellines. It is an illusion—acognitive illusion—and I(System2)wastaughthowtorecognize it and advised nottobelieveitoractonit.The question that is most

often asked about cognitiveillusions is whether they canbeovercome.Themessageof

these examples is notencouraging.BecauseSystem1 operates automatically andcannot be turned off at will,errorsofintuitivethoughtareoften difficult to prevent.Biases cannot always beavoided, because System 2mayhavenocluetotheerror.Even when cues to likelyerrors are available, errorscanbepreventedonlyby theenhanced monitoring andeffortfulactivityofSystem2.

As a way to live your life,however, continuousvigilance is not necessarilygood, and it is certainlyimpractical. Constantlyquestioningourownthinkingwouldbe impossibly tedious,and System 2 is much tooslow and inefficient to serveasasubstituteforSystem1inmaking routine decisions.The best we can do is acompromise: learn torecognize situations inwhich

mistakes are likely and tryharder to avoid significantmistakeswhen the stakes arehigh. The premise of thisbook is that it is easier torecognize other people’smistakesthanourown.

UsefulFictionsYou have been invited tothink of the two systems asagents within themind, withtheir individual personalities,

abilities, and limitations. Iwill often use sentences inwhich the systems are thesubjects, such as, “System 2calculatesproducts.”Theuseofsuchlanguageis

considered a sin in theprofessionalcirclesinwhichItravel, because it seems toexplain the thoughts andactions of a person by thethoughts and actions of littlepeople inside the person’shead. Grammatically the

sentence about System 2 issimilar to “The butler stealsthe petty cash.” Mycolleagues would point outthat the butler’s actionactually explains thedisappearance of the cash,and they rightly questionwhether the sentence aboutSystem 2 explains howproducts are calculated. Myansweristhatthebriefactivesentence that attributescalculation to System 2 is

intendedasadescription,notan explanation. It ismeaningful only because ofwhatyoualreadyknowaboutSystem 2. It is shorthand forthe following: “Mentalarithmetic is a voluntaryactivity that requires effort,should not be performedwhilemakinga left turn,andis associated with dilatedpupils and an acceleratedheartrate.”Similarly,thestatementthat

“highway driving underroutine conditions is left toSystem 1” means thatsteeringthecararoundabendis automatic and almosteffortless. It also implies thatan experienced driver candrive on an empty highwaywhile conducting aconversation. Finally,“System 2 prevented Jamesfromreactingfoolishlytotheinsult” means that Jameswould have been more

aggressive in his response ifhis capacity for effortfulcontrol had been disrupted(for example, if he had beendrunk).System 1 and System 2 are

socentraltothestoryItellinthisbook that Imustmake itabsolutely clear that theyare217at they a fictitiouscharacters. Systems 1 and 2are not systems in thestandardsenseofentitieswithinteracting aspects or parts.

And there is no one part ofthe brain that either of thesystems would call home.You may well ask: What isthe point of introducingfictitiouscharacterswithuglynames into a serious book?The answer is that thecharacters are useful becauseofsomequirksofourminds,yoursandmine.Asentenceisunderstood more easily if itdescribes what an agent(System 2) does than if it

describes what something is,what properties it has. Inotherwords, “System2” is abetter subject for a sentencethan“mentalarithmetic.”Themind—especiallySystem1—appears to have a specialaptitude for the constructionand interpretation of storiesaboutactiveagents,whohavepersonalities, habits, andabilities.Youquicklyformedabadopinionof the thievingbutler, you expect more bad

behavior from him, and youwill remember him for awhile. This is also my hopeforthelanguageofsystems.

Whycall themSystem1andSystem2ratherthanthemoredescriptive “automaticsystem” and “effortfulsystem”? The reason issimple: “Automatic system”takes longer to say than“System 1” and therefore

takes more space in yourworking memory. Thismatters, because anythingthat occupies your workingmemory reduces your abilityto think. You should treat“System 1” and “System 2”as nicknames, like Bob andJoe, identifying charactersthatyouwillgettoknowoverthe course of this book. Thefictitious systems make iteasier for me to think aboutjudgmentandchoice,andwill

make it easier for you tounderstandwhatIsay.

SpeakingofSystem1andSystem2

“He had an impression,but some of hisimpressions areillusions.”

“ThiswasapureSystem1 response. She reacted

to the threat before sherecognizedit.”

“This is your System 1talking. Slow down andlet your System 2 takecontrol.”

P

AttentionandEffort

In the unlikely event of thisbookbeingmade intoa film,System 2 would be asupporting character whobelieves herself to be thehero. The defining feature ofSystem2,inthisstory,isthatits operations are effortful,and one of its maincharacteristics is laziness, a

reluctance to invest moreeffort than is strictlynecessary.Asaconsequence,the thoughts and actions thatSystem 2 believes it haschosen are often guided bythefigureat thecenterof thestory, System 1. However,therearevital tasks thatonlySystem 2 can performbecause they require effortand acts of self-control inwhich the intuitions andimpulses of System 1 are

overcome.

MentalEffortIf you wish to experienceyourSystem2workingatfulltilt, the following exercisewill do; it should br"0%e caTting you to the limits ofyourcognitiveabilitieswithin5 seconds.To start,make upseveral stringsof4digits, alldifferent, and write eachstringonanindexcard.Place

a blank card on top of thedeck. The task that you willperformiscalledAdd-1.Hereishowitgoes:

Start beating a steadyrhythm(orbetteryet,seta metronome at 1/sec).Remove the blank cardand read the four digitsaloud. Wait for twobeats, then report astring in which each ofthe original digits is

incrementedby1. If thedigits on the card are5294, the correctresponse is 6305.Keeping the rhythm isimportant.

Few people can cope withmore than four digits in theAdd-1task,butifyouwantaharder challenge, please tryAdd-3.If you would like to know

what your body is doing

while your mind is hard atwork, set up two piles ofbooksonasturdytable,placea video camera on one andlean your chin on the other,getthevideogoing,andstareat the camera lenswhile youwork on Add-1 or Add-3exercises.Later,youwillfindin the changing size of yourpupils a faithful record ofhowhardyouworked.I have a long personal

history with the Add-1 task.

Early in my career I spent ayear at the University ofMichigan, as a visitor in alaboratory that studiedhypnosis.Castingabout forauseful topic of research, Ifound an article in ScientificAmerican in which thepsychologist Eckhard Hessdescribedthepupiloftheeyeas a window to the soul. Ireread it recently and againfound it inspiring. It beginswith Hess reporting that his

wife had noticed his pupilswidening as he watchedbeautiful nature pictures, andit ends with two strikingpictures of the same good-looking woman, whosomehowappearsmuchmoreattractive in one than in theother. There is only onedifference: the pupils of theeyes appear dilated in theattractive picture andconstricted in theother.Hessalso wrote of belladonna, a

pupil-dilating substance thatwas used as a cosmetic, andofbazaarshopperswhoweardark glasses in order to hidetheir level of interest frommerchants.One of Hess’s findings

especially captured myattention.Hehadnoticedthatthe pupils are sensitiveindicators of mental effort—theydilatesubstantiallywhenpeople multiply two-digitnumbers, and they dilate

moreiftheproblemsarehardthan if they are easy. Hisobservations indicated thatthe response tomental effortis distinct from emotionalarousal. Hess’s work did nothave much to do withhypnosis,butIconcludedthattheideaofavisibleindicationofmental effort had promiseas a research topic. Agraduate student in the lab,Jackson Beatty, shared myenthusiasm and we got to

work.Beatty and I developed a

setup similar to an optician’sexamination room, in whichthe experimental participantleaned her head on a chin-and-forehead rest and staredatacamerawhilelisteningtoprerecorded information andanswering questions on therecorded beats of ametronome. The beatstriggered an infrared flashevery second, causing a

picturetobetaken.Attheendofeachexperimentalsession,we would rush to have thefilm developed, project theimages of the pupil on ascreen,andgotoworkwitharuler. The method was aperfect fit for young andimpatient researchers: weknew our results almostimmediately,andtheyalwaystoldaclearstory.Beatty and I focused on

pacedtasks,suchasAdd-1,in

which we knew preciselywhat was on the subject’smind at any time. Werecorded strings of digits onbeats of the metronome andinstructed the subject torepeator transform thedigitsone indigits onby one,maintainingthesamerhythm.We soon discovered that thesize of the pupil variedsecond by second, reflectingthe changing demands of thetask. The shape of the

response was an inverted V.As you experienced it if youtried Add-1 or Add-3, effortbuilds up with every addeddigitthatyouhear,reachesanalmost intolerable peak asyou rush to produce atransformedstringduringandimmediately after the pause,and relaxes gradually as you“unload” your short-termmemory. The pupil datacorresponded precisely tosubjective experience: longer

strings reliably caused largerdilations, the transformationtask compounded the effort,and the peak of pupil sizecoincided with maximumeffort.Add-1withfourdigitscaused a larger dilation thanthe task of holding sevendigits for immediate recall.Add-3, which is much moredifficult, is the mostdemanding that I everobserved. In the first 5seconds, the pupil dilates by

about50%ofitsoriginalareaand heart rate increases byabout 7 beats per minute.This isashardaspeoplecanwork—theygiveupifmoreisasked of them. When weexposedoursubjects tomoredigits than they couldremember, their pupilsstopped dilating or actuallyshrank.We worked for some

months in a spaciousbasement suite in which we

had set up a closed-circuitsystem that projected animage of the subject’s pupilon a screen in the corridor;wealso couldhearwhatwashappening in the laboratory.Thediameteroftheprojectedpupil was about a foot;watching it dilate andcontractwhen the participantwasatworkwasafascinatingsight, quite an attraction forvisitors in our lab. Weamused ourselves and

impressed our guests by ourability to divine when theparticipantgaveuponatask.During a mentalmultiplication, the pupilnormally dilated to a largesizewithinafewsecondsandstayed large as long as theindividual kept working onthe problem; it contractedimmediately when she foundasolutionorgaveup.Aswewatched from the corridor,wewouldsometimessurprise

both the owner of the pupiland our guests by asking,“Why did you stop workingjust now?” The answer frominside the lab was often,“How did you know?” towhich we would reply, “Wehaveawindowtoyoursoul.”The casual observationswe

made from the corridorweresometimes as informative asthe formal experiments. Imade a significant discoveryas I was idly watching a

woman’spupilduringabreakbetween two tasks. She hadkept her position on the chinrest,soIcouldseetheimageofhereyewhilesheengagedin routine conversation withthe experimenter. I wassurprisedtoseethatthepupilremained small and did notnoticeablydilateasshetalkedandlistened.Unlikethetasksthat we were studying, themundane conversationapparentlydemanded littleor

no effort—no more thanretaining two or three digits.Thiswasaeurekamoment: Irealizedthatthetaskswehadchosen for study wereexceptionally effortful. Animage came to mind: mentallife—today Iwould speak ofthe life of System 2—isnormally conducted at thepace of a comfortable walk,sometimes interrupted byepisodes of jogging and onrare occasions by a frantic

sprint.TheAdd-1andAdd-3exercises are sprints, andcasualchattingisastroll.Wefoundthatpeople,when

engaged in a mental sprint,may become effectivelyblind. The authors of TheInvisible Gorilla had madethe gorilla “invisible” bykeeping the observersintensely busy countingpasses. We reported a ratherless dramatic example ofblindness during Add-1. Our

subjects were exposed to aseries of rapidly flashingletters while they worked.They were told to give thetask complete priority, butthey were also asked toreport, at theendof thedigittask,whethertheletterKhadappeared at any rored atantime during the trial. Themain finding was that theabilitytodetectandreportthetarget letter changed in thecourse of the 10 seconds of

the exercise. The observersalmostnevermissedaK thatwas shown at the beginningor near the endof theAdd-1task but they missed thetarget almost half the timewhenmentaleffortwasat itspeak, although we hadpictures of their wide-openeye staring straight at it.Failuresofdetectionfollowedthe same inverted-V patternas the dilating pupil. Thesimilaritywas reassuring: the

pupilwas agoodmeasureofthe physical arousal thataccompanies mental effort,and we could go ahead anduse it to understand how themindworks.Much like the electricity

meter outside your house orapartment,thepupilsofferanindex of the current rate atwhichmental energy isused.Theanalogygoesdeep.Youruse of electricity depends onwhat you choose to do,

whether to light a room ortoast a piece of bread.Whenyou turn on a bulb or atoaster, itdrawstheenergyitneedsbutnomore.Similarly,wedecidewhattodo,butwehave limited control over theeffort of doing it. Supposeyou are shown four digits,say, 9462, and told that yourlifedependsonholding themin memory for 10 seconds.However much you want tolive, you cannot exert as

much effort in this task asyouwouldbeforcedtoinvestto complete an Add-3transformation on the samedigits.System 2 and the electrical

circuits in your home bothhave limited capacity, butthey respond differently tothreatened overload. Abreaker trips when thedemand for current isexcessive,causingalldeviceson that circuit to lose power

at once. In contrast, theresponse to mental overloadis selective and precise:System 2 protects the mostimportant activity, so itreceives the attention itneeds; “spare capacity” isallocatedsecondbysecondtoother tasks. Inourversionofthe gorilla experiment, weinstructed the participants toassign priority to the digittask. We know that theyfollowed that instruction,

because the timing of thevisual targethadnoeffectonthe main task. If the criticalletterwaspresentedata timeof high demand, the subjectssimply did not see it. Whenthe transformation task wasless demanding, detectionperformancewasbetter.The sophisticated allocation

of attention has been honedby a long evolutionaryhistory. Orienting andresponding quickly to the

gravest threats or mostpromising opportunitiesimproved the chance ofsurvival,andthiscapabilityiscertainly not restricted tohumans. Even in modernhumans,System1 takesoverin emergencies and assignstotalprioritytoself-protectiveactions. Imagine yourself atthe wheel of a car thatunexpectedlyskidsonalargeoil slick. You will find thatyou have responded to the

threat before you becamefullyconsciousofit.Beatty and I worked

together for only a year, butour collaboration had a largeeffect on our subsequentcareers. He eventuallybecame the leading authorityon “cognitive pupillometry,”and I wrote a book titledAttention and Effort, whichwas based in large part onwhatwelearnedtogetherandonfollow-upresearchIdidat

Harvard the following year.Welearnedagreatdealaboutthe working mind—which Inow think of as System 2—from measuring pupils in awidevarietyoftasks.Asyoubecome skilled in a

task, its demand for energydiminishes. Studies of thebrain have shown that thepattern of activity associatedwith an action changes asskill increases, with fewerbrainregionsinvolved.Talent

has similar effects. Highlyintelligent individuals needless effort to solve the sameproblems, as indicated byboth pupil size and brainactivity. A general “law ofleast effort” appd t” alies tocognitive as well as physicalexertion.The lawasserts thatif there are several ways ofachieving the same goal,people will eventuallygravitate to the leastdemanding course of action.

In the economy of action,effort is a cost, and theacquisition of skill is drivenbythebalanceofbenefitsandcosts. Laziness is built deepintoournature.The tasks that we studied

varied considerably in theireffects on the pupil. Atbaseline, our subjects wereawake, aware, and ready toengageinatask—probablyata higher level of arousal andcognitive readiness than

usual. Holding one or twodigits inmemory or learningto associate a word with adigit (3 = door) producedreliableeffectsonmomentaryarousal above that baseline,but the effects wereminuscule, only 5% of theincrease in pupil diameterassociatedwithAdd-3.Ataskthat required discriminatingbetween the pitch of twotones yielded significantlylarger dilations. Recent

research has shown thatinhibiting the tendency toread distracting words (as infigure 2 of the precedingchapter) also inducesmoderate effort. Tests ofshort-termmemoryforsixorseven digits were moreeffortful. As you canexperience, the request toretrieve and say aloud yourphone number or yourspouse’s birthday alsorequiresabriefbutsignificant

effort, because the entirestring must be held inmemory as a response isorganized. Mentalmultiplication of two-digitnumbers and the Add-3 taskare near the limit of whatmostpeoplecando.Whatmakessomecognitive

operations more demandingand effortful than others?What outcomes must wepurchase in the currency ofattention?WhatcanSystem2

dothatSystem1cannot?Wenowhavetentativeanswerstothesequestions.Effort is required to

maintain simultaneously inmemory several ideas thatrequire separate actions, orthat need to be combinedaccording to a rule—rehearsing your shopping listasyouenterthesupermarket,choosing between the fishandthevealatarestaurant,orcombining a surprising result

from a survey with theinformation that the samplewas small, for example.System2is theonlyonethatcan follow rules, compareobjects on several attributes,and make deliberate choicesbetween options. Theautomatic System 1 does nothave these capabilities.System 1 detects simplerelations(“theyareallalike,”“the son is much taller thanthe father”) and excels at

integrating information aboutonething,butitdoesnotdealwith multiple distinct topicsat once, nor is it adept atusing purely statisticalinformation. System 1 willdetectthatapersondescribedas“ameekandtidysoul,withaneedfororderandstructure,and a passion for detail”resembles a caricaturelibrarian, but combining thisintuition with knowledgeabout the small number of

librarians is a task that onlySystem 2 can perform—ifSystem 2 knows how to doso, which is true of fewpeople.A crucial capability of

System 2 is the adoption of“task sets”: it can programmemory to obey aninstruction that overrideshabitual responses. Considerthe following: Count alloccurrences of the letter f inthis page. This is not a task

you have ever performedbefore and it will not comenaturally to you, but yourSystem 2 can take it on. Itwill be effortful to setyourself up for this exercise,and effortful to carry it out,though you will surelyimprove with practice.Psychologists speak of“executive control” todescribe the adoption andtermination of task sets, andneuroscientists have

identifiedthemainregionsofthe brain that serve theexecutive function. One ofthese regions is involvedwhenever a conflict must beresolved. Another is theprefrontalareaofthebrain,aregion that is substantiallymore developed in humanstht un humans an in otherprimates, and is involved inoperations that we associatewithintelligence.Nowsupposethatattheend

of the page you get anotherinstruction: count all thecommas in the next page.This will be harder, becauseyou will have to overcomethe newly acquired tendencytofocusattentionontheletterf. One of the significantdiscoveries of cognitivepsychologists in recentdecades is that switchingfrom one task to another iseffortful, especially undertime pressure. The need for

rapid switching is one of thereasons that Add-3 andmental multiplication are sodifficult.ToperformtheAdd-3task,youmustholdseveraldigits in your workingmemory at the same time,associating each with aparticular operation: somedigits are in the queue to betransformed, one is in theprocess of transformation,and others, alreadytransformed, are retained for

reporting. Modern tests ofworking memory require theindividual to switchrepeatedly between twodemanding tasks, retainingthe results of one operationwhile performing the other.Peoplewhodowell on thesetests tend to dowell on testsof general intelligence.However, the ability tocontrolattentionisnotsimplya measure of intelligence;measures of efficiency in the

control of attention predictperformance of air trafficcontrollers and of Israeli AirForce pilots beyond theeffectsofintelligence.Time pressure is another

driver of effort. As youcarried out the Add-3exercise, the rush wasimposed in part by themetronomeandinpartbytheload on memory. Like ajuggler with several balls inthe air, you cannot afford to

slowdown; the rateatwhichmaterial decays in memoryforces the pace, driving youto refresh and rehearseinformation before it is lost.Any task that requiresyou tokeepseveral ideas inmindatthe same time has the samehurriedcharacter.Unlessyouhave the good fortune of acapacious working memory,you may be forced to workuncomfortably hard. Themost effortful forms of slow

thinkingarethosethatrequireyoutothinkfast.Yousurelyobservedasyou

performed Add-3 howunusualitisforyourmindtowork so hard. Even if youthink for a living, fewof themental tasks in which youengage in the course of aworking day are asdemandingasAdd-3,orevenas demanding as storing sixdigits for immediate recall.We normally avoid mental

overload by dividing ourtasksintomultipleeasysteps,committing intermediateresults to long-term memoryor to paper rather than to aneasily overloaded workingmemory. We cover longdistances by taking our timeand conduct ourmental livesbythelawofleasteffort.

SpeakingofAttentionandEffort

“Iwon’ttrytosolvethiswhile driving. This is apupil-dilating task. Itrequiresmentaleffort!”

“The law of least effortis operating here. Hewill think as little aspossible.”

“She did not forgetabout the meeting. She

was completely focusedon something elsewhenthemeetingwas set andshejustdidn’thearyou.”

“What came quickly tomy mind was anintuitionfromSystem1.I’llhavetostartoverandsearch my memorydeliberately.”

P

TheLazyController

I spend a few months eachyear in Berkeley, and one ofmy great pleasures there is adaily four-mile walk on amarkedpathinthehills,witha fine viewofSanFranciscoBay. I usually keep track ofmy time and have learned afairamountabouteffortfromdoing so. I have found a

speed,about17minutesforamile,whichIexperienceasastroll. I certainly exertphysicaleffortandburnmorecaloriesatthatspeedthanifIsat in a recliner, but Iexperience no strain, noconflict, andnoneed topushmyself. I am also able tothinkandworkwhilewalkingat that rate. Indeed, I suspectthatthemildphysicalarousalof the walk may spill overintogreatermentalalertness.

System2 alsohas anaturalspeed. You expend somemental energy in randomthoughts and in monitoringwhat goes on around youeven when your mind doesnothing in particular, butthere is little strain. Unlessyou are in a situation thatmakesyouunusuallywaryorself-conscious, monitoringwhat happens in theenvironment or inside yourhead demands little effort.

You make many smalldecisions as you drive yourcar, absorb some informationas you read the newspaper,and conduct routineexchanges of pleasantrieswitha spouseoracolleague,all with little effort and nostrain.Justlikeastroll.It is normally easy and

actually quite pleasant towalk and think at the sametime, but at the extremesthese activities appear to

compete for the limitedresources of System 2. Youcan confirm this claim by asimple experiment. Whilewalking comfortably with afriend,askhimtocompute23×78inhishead,andtodosoimmediately. He will almostcertainly stop in his tracks.My experience is that I canthink while strolling butcannotengageinmentalworkthat imposesaheavy loadonshort-termmemory. If Imust

construct an intricateargument under timepressure, I would rather bestill, and I would prefersittingtostanding.Ofcourse,notallslowthinkingrequiresthat form of intenseconcentration and effortfulcomputation—I did the bestthinking of my life onleisurelywalkswithAmos.Accelerating beyond my

strolling speed completelychanges the experience of

walking, because thetransition to a faster walkbrings about a sharpdeterioration inmy ability tothink coherently. As I speedup, my attention is drawnwith increasing frequency totheexperienceofwalkingandto thedeliberatemaintenanceof the fasterpace.Myabilitytobringatrainofthoughttoaconclusion is impairedaccordingly. At the highestspeed I can sustain on the

hills, about 14 minutes for amile, I do not even try tothink of anything else. Inadditiontothephysicaleffortof moving my body rapidlyalongthepath,amentaleffortof self-control is needed toresist theurge to slowdown.Self-control and deliberatethought apparently draw onthe same limited budget ofeffort.Formostofus,mostof the

time, the maintenance of a

coherent trainof thoughtandtheoccasionalengagement ineffortfulthinkingalsorequireself-control. Although I havenot conducted a systematicsurvey,Isuspectthatfrequentswitching of tasks andspeeded-up mental work arenot intrinsically pleasurable,and that people avoid themwhen possible. This is howthe law of least effort comesto be a law. Even in theabsence of time pressure,

maintaining a coherent trainofthoughtrequiresdiscipline.Anobserverofthenumberoftimes I look at e-mail orinvestigate the refrigeratorduring an hour of writingcould wahene dd reasonablyinfer an urge to escape andconclude that keeping at itrequires more self-controlthanIcanreadilymuster.Fortunately, cognitive work

is not always aversive, andpeople sometimes expend

considerable effort for longperiods of time withouthaving to exert willpower.The psychologist MihalyCsikszentmihalyi(pronounced six-cent-mihaly)has done more than anyoneelse to study this state ofeffortless attending, and thenameheproposedforit,flow,has become part of thelanguage. People whoexperienceflowdescribeitas“a state of effortless

concentration so deep thatthey lose their sense of time,of themselves, of theirproblems,” and theirdescriptionsofthejoyofthatstate are so compelling thatCsikszentmihalyihascalleditan “optimal experience.”Many activities can induce asense of flow, from paintingto racing motorcycles—andfor some fortunate authors Iknow,evenwritingabookisoften an optimal experience.

Flowneatlyseparatesthetwoformsofeffort:concentrationonthetaskandthedeliberatecontrol of attention.Riding amotorcycle at 150 miles anhour and playing acompetitive game of chessarecertainlyveryeffortful.Ina state of flow, however,maintainingfocusedattentionon these absorbing activitiesrequires no exertion of self-control, thereby freeingresourcestobedirectedtothe

taskathand.

TheBusyandDepletedSystem2

It is now a well-establishedproposition that both self-control and cognitive effortare forms of mental work.Several psychological studieshave shown that people whoare simultaneouslychallenged by a demandingcognitive task and by a

temptationaremore likely toyield to the temptation.Imaginethatyouareaskedtoretainalistofsevendigitsforaminuteortwo.Youaretoldthatrememberingthedigitsisyour toppriority.Whileyourattention is focused on thedigits, you are offered achoice between two desserts:a sinfulchocolatecakeandavirtuous fruit salad. Theevidence suggests that youwouldbemorelikelytoselect

the tempting chocolate cakewhen your mind is loadedwith digits. System 1 hasmore influence on behaviorwhenSystem2isbusy,andithasasweettooth.People who are cognitivelybusy are also more likely tomake selfish choices, usesexist language, and makesuperficial judgments insocialsituations.Memorizingand repeating digits loosensthe hold of System 2 on

behavior, but of coursecognitiveloadisnot theonlycause of weakened self-control. A few drinks havethe same effect, as does asleepless night. The self-control of morning people isimpairedatnight; thereverseis true of night people. Toomuchconcernabouthowwellone is doing in a tasksometimes disruptsperformance by loadingshort-term memory with

pointless anxious thoughts.The conclusion isstraightforward: self-controlrequires attention and effort.Anotherwayofsayingthisisthat controlling thoughts andbehaviors is one of the tasksthatSystem2performs.A series of surprising

experiments by thepsychologist RoyBaumeisterandhiscolleagueshasshownconclusively that all variantsof voluntary effort—

cognitive, emotional, orphysical—drawatleastpartlyon a shared pool of mentalenergy. Their experimentsinvolvesuccessiveratherthansimultaneoustasks.Baumeister’s group has

repeatedly found that aneffortofwillorself-controlistiring; if you have had toforce yourself to dosomething, you are lesswilling or less able to exertself-control when the next

challengecomesaround.Thephenomenonhasbeennamedego depletion. In a typicaldemo thypical denstration,participants who areinstructed to stifle theiremotional reaction to anemotionallychargedfilmwilllaterperformpoorlyonatestof physical stamina—howlong they can maintain astronggriponadynamometerin spite of increasingdiscomfort. The emotional

effortinthefirstphaseoftheexperimentreducestheabilityto withstand the pain ofsustainedmuscle contraction,and ego-depleted peopletherefore succumb morequicklytotheurgetoquit.Inanother experiment, peoplearefirstdepletedbya taskinwhichtheyeatvirtuousfoodssuch as radishes and celerywhileresistingthetemptationto indulge in chocolate andrich cookies. Later, these

people will give up earlierthannormalwhenfacedwithadifficultcognitivetask.The list of situations and

tasks that are now known todeplete self-control is longand varied. All involveconflict and the need tosuppress a natural tendency.Theyinclude:

avoiding the thought ofwhitebearsinhibiting the emotional

response to a stirringfilmmakingaseriesofchoicesthatinvolveconflicttryingtoimpressothersresponding kindly to apartner’sbadbehaviorinteracting with a personof a different race (forprejudicedindividuals)

The list of indications ofdepletion is also highlydiverse:

deviatingfromone’sdietoverspending onimpulsivepurchasesreacting aggressively toprovocationpersisting less time in ahandgriptaskperforming poorly incognitive tasks andlogicaldecisionmaking

The evidence is persuasive:activities that impose highdemandsonSystem2require

self-control, and the exertionof self-control is depletingand unpleasant. Unlikecognitive load, ego depletionis at least in part a loss ofmotivation. After exertingself-control in one task, youdo not feel like making aneffort in another, althoughyou could do it if you reallyhad to. In severalexperiments, people wereable to resist the effects ofego depletion when given a

strong incentive to do so. Incontrast, increasing effort isnotanoptionwhenyoumustkeep six digits in short-termmemory while performing atask.Egodepletionisnot thesame mental state ascognitivebusyness.The most surprising

discovery made byBaumeister’sgroupshows,ashe puts it, that the idea ofmentalenergy ismore thanamere metaphor. The nervous

system consumes moreglucosethanmostotherpartsof the body, and effortfulmental activity appears to beespecially expensive in thecurrency of glucose. Whenyou are actively involved indifficult cognitive reasoningor engaged in a task thatrequires self-control, yourblood glucose level drops.The effect is analogous to arunner who draws downglucosestoredinhermuscles

during a sprint. The boldimplicationofthisideaisthatthe effects of ego depletioncouldbeundonebyingestingglucose, and Baumeister andhis colleagues haveconfirmed this hypothesis nohypothesiin severalexperiments.Volunteers in one of their

studieswatchedashortsilentfilm of a woman beinginterviewed and were askedto interpret her body

language. While they wereperforming the task, a seriesofwordscrossedthescreeninslow succession. Theparticipants were specificallyinstructed to ignore thewords,andiftheyfoundtheirattention drawn away theyhad to refocus theirconcentrationonthewoman’sbehavior. This act of self-control was known to causeego depletion. All thevolunteers drank some

lemonadebeforeparticipatingin a second task. Thelemonade was sweetenedwithglucoseforhalfof themand with Splenda for theothers. Then all participantswere given a task in whichthey needed to overcome anintuitive response to get thecorrect answer. Intuitiveerrors are normally muchmore frequent among ego-depleted people, and thedrinkers of Splenda showed

theexpecteddepletioneffect.On the other hand, theglucose drinkers were notdepleted. Restoring the levelofavailablesugarinthebrainhad prevented thedeterioration of performance.It will take some time andmuch further research toestablish whether the tasksthat cause glucose-depletionalso cause the momentaryarousal that is reflected inincreases of pupil size and

heartrate.A disturbing demonstration

of depletion effects injudgment was recentlyreported in the Proceedingsof the National Academy ofSciences. The unwittingparticipantsinthestudywereeight parole judges in Israel.They spend entire daysreviewing applications forparole. The cases arepresented in random order,and the judges spend little

timeoneachone,anaverageof 6 minutes. (The defaultdecision is denial of parole;only 35% of requests areapproved. The exact time ofeachdecisionisrecorded,andthetimesof thejudges’ threefood breaks—morning break,lunch, andafternoonbreak—duringthedayarerecordedaswell.) The authors of thestudy plotted the proportionof approved requests againstthe time since the last food

break. The proportion spikesafter each meal, when about65% of requests are granted.During the two hours or sountilthejudges’nextfeeding,the approval rate dropssteadily, to about zero justbefore the meal. As youmight expect, this is anunwelcome result and theauthors carefully checkedmany alternativeexplanations. The bestpossible account of the data

provides bad news: tired andhungry judges tend to fallback on the easier defaultposition of denying requestsfor parole. Both fatigue andhungerprobablyplayarole.

TheLazySystem2Oneof themain functionsofSystem 2 is to monitor andcontrol thoughts and actions“suggested” by System 1,allowing some to be

expresseddirectlyinbehaviorandsuppressingormodifyingothers.For an example, here is a

simple puzzle. Do not try tosolve it but listen to yourintuition:

A bat and ball cost$1.10.The bat costs one dollarmorethantheball.Howmuchdoestheballcost?

Anumbercametoyourmind.Thenumber,ofcourse,is10:10¢. The distinctive mark ofthis easy puzzle is that itevokes an answer that isintuitive, appealing, andwrong.Dothemath,andyouwillsee.Iftheballcosts10¢,then the total cost will be$1.20 (10¢ for the ball and$1.10 for thebat), not$1.10.The correct answer is 5¢.It%">5¢. is safe to assume

that the intuitive answer alsocame to the mind of thosewho ended up with thecorrect number—theysomehow managed to resisttheintuition.Shane Frederick and I

worked together on a theoryof judgment based on twosystems,andheusedthebat-and-ball puzzle to study acentralquestion:Howcloselydoes System 2 monitor thesuggestionsofSystem1?His

reasoningwasthatweknowasignificant fact about anyonewho says that the ball costs10¢: that person did notactively check whether theanswer was correct, and herSystem 2 endorsed anintuitive answer that it couldhave rejected with a smallinvestment of effort.Furthermore, we also knowthat the people who give theintuitive answer havemissedan obvious social cue; they

should have wondered whyanyone would include in aquestionnaire a puzzle withsuch an obvious answer. Afailuretocheckisremarkablebecause the cost of checkingis so low: a few seconds ofmental work (the problem ismoderately difficult), withslightly tensed muscles anddilatedpupils,couldavoidanembarrassingmistake.Peoplewho say 10¢ appear to beardentfollowersofthelawof

leasteffort.Peoplewhoavoidthat answer appear to havemoreactiveminds.Many thousands of

university students haveanswered the bat-and-ballpuzzle, and the results areshocking.More than 50% ofstudentsatHarvard,MIT,andPrinceton ton gave theintuitive—incorrect—answer.At less selective universities,the rate of demonstrablefailuretocheckwasinexcess

of 80%. The bat-and-ballproblemisourfirstencounterwith an observation that willbe a recurrent theme of thisbook: many people areoverconfident,prone toplacetoo much faith in theirintuitions. They apparentlyfind cognitive effort at leastmildly unpleasant and avoiditasmuchaspossible.Now I will show you a

logical argument—twopremises and a conclusion.

Try to determine, as quicklyasyoucan,iftheargumentislogically valid. Does theconclusion follow from thepremises?

Allrosesareflowers.Some flowers fadequickly.Therefore some rosesfadequickly.

A large majority of collegestudents endorse this

syllogismasvalid.Infacttheargument is flawed, becauseitispossiblethattherearenoroses among the flowers thatfade quickly. Just as in thebat-and-ball problem, aplausible answer comes tomind immediately.Overriding it requires hardwork—the insistent idea that“it’s true, it’s true!”makes itdifficult to check the logic,andmost people do not takethe trouble to think through

theproblem.This experiment has

discouraging implications forreasoning in everyday life. Itsuggests that when peoplebelieve a conclusion is true,they are also very likely tobelieveargumentsthatappeartosupportit,evenwhenthesearguments are unsound. IfSystem 1 is involved, theconclusion comes first andtheargumentsfollow.Next,considerthefollowing

question and answer itquicklybeforereadingon:

How many murdersoccur in the state ofMichiganinoneyear?

Thequestion,whichwasalsodevised by Shane Frederick,isagainachallengetoSystem2.The “trick” iswhether therespondent will rememberthat Detroit, a high-crime cthigh-crimeity, is in

Michigan.Collegestudentsinthe United States know thisfact and will correctlyidentifyDetroitas the largestcity in Michigan. Butknowledgeofafactisnotall-or-none. Facts that we knowdo not always come tomindwhen we need them. PeoplewhorememberthatDetroitisin Michigan give higherestimates of the murder ratein the state than people whodo not, but a majority of

Frederick’s respondents didnot think of the city whenquestioned about the state.Indeed, the average guess bypeoplewhowereaskedaboutMichigan is lower than theguesses of a similar groupwho were asked about themurderrateinDetroit.Blame fora failure to think

ofDetroitcanbelaidonbothSystem 1 and System 2.Whether the city comes tomind when the state is

mentioneddependsinpartonthe automatic function ofmemory.Peopledifferinthisrespect.Therepresentationofthe state ofMichigan is verydetailed in some people’sminds: residents of the stateare more likely to retrievemany facts about it thanpeople who live elsewhere;geography buffswill retrievemore than others whospecialize in baseballstatistics; more intelligent

individuals are more likelythan others to have richrepresentations of mostthings. Intelligence is notonlytheabilitytoreason;itisalso the ability to findrelevant material in memoryand todeployattentionwhenneeded. Memory function isan attribute of System 1.However, everyone has theoption of slowing down toconduct an active search ofmemory for all possibly

relevant facts—just as theycouldslowdowntochecktheintuitive answer in the bat-and-ball problem. The extentof deliberate checking andsearch is a characteristic ofSystem 2, which variesamongindividuals.The bat-and-ball problem,

theflowerssyllogism,andtheMichigan/Detroit problemhave something in common.Failing these minitestsappearstobe,atleasttosome

extent,amatterofinsufficientmotivation, not trying hardenough. Anyonewho can beadmittedtoagooduniversityis certainly able to reasonthrough the first twoquestionsandtoreflectaboutMichigan long enough toremember the major city inthat state and its crimeproblem. These students cansolve much more difficultproblems when they are nottempted to accept a

superficiallyplausibleanswerthat comes readily to mind.Theeasewithwhichtheyaresatisfied enough to stopthinking is rather troubling.“Lazy” is a harsh judgmentabout the self-monitoring ofthese young people and theirSystem 2, but it does notseemtobeunfair.Thosewhoavoid the sin of intellectualsloth could be called“engaged.” They are morealert, more intellectually

active, less willing to besatisfied with superficiallyattractive answers, moreskeptical about theirintuitions. The psychologistKeith Stanovich would callthemmorerational.

Intelligence,Control,Rationality

Researchers have applieddiverse methods to examinethe connection between

thinking and self-control.Some have addressed it byasking the correlationquestion: If people wereranked by their self-controland by their cognitiveaptitude, would individualshave similar positions in thetworankings?In one of the most famous

experiments in the history ofpsychology, Walter Mischeland his students exposedfour-year-old children to a

cruel dilemma. They weregiven a choice between asmall reward (one Oreo),whichtheycouldhaveatanytime,or a larger reward (twocookies) for which they hadto wait 15 minutes underdifficult conditions. Theywere to remain alone in aroom,facingadeskwithtwoobjects:asinglecookieandabell that the child could ringat any time to call in theexperimenter and receiven

oandreceitheonecookie.Asthe experiment wasdescribed: “There were notoys,books,pictures,orotherpotentially distracting itemsin the room. Theexperimenter left the roomand did not return until 15min had passed or the childhad rung the bell, eaten therewards, stood up, or shownanysignsofdistress.”The children were watched

through a one-way mirror,

and the film that shows theirbehavior during the waitingtimealwayshas theaudienceroaring in laughter. Abouthalfthechildrenmanagedthefeat of waiting for 15minutes, mainly by keepingtheir attentionaway from thetempting reward. Ten orfifteenyearslater,alargegaphad opened between thosewho had resisted temptationand those who had not. Theresistershadhighermeasures

of executive control incognitive tasks, andespecially the ability toreallocate their attentioneffectively. As young adults,they were less likely to takedrugs. A significantdifference in intellectualaptitude emerged: thechildren who had shownmore self-control as four-year-olds had substantiallyhigher scores on tests ofintelligence.

AteamofresearchersattheUniversity of Oregonexplored the link betweencognitive control andintelligence in several ways,including an attempt to raiseintelligenceby improving thecontrol of attention. Duringfive 40-minute sessions, theyexposedchildrenagedfourtosix to various computergames especially designed todemandattentionandcontrol.In one of the exercises, the

children used a joystick totrackacartooncatandmoveit to a grassy area whileavoiding a muddy area. Thegrassyareasgraduallyshrankand the muddy areaexpanded, requiringprogressively more precisecontrol.Thetestersfoundthattraining attention not onlyimproved executive control;scores on nonverbal tests ofintelligence also improvedand the improvement was

maintained for severalmonths.Otherresearchbythesamegroupidentifiedspecificgenesthatareinvolvedinthecontrol of attention, showedthatparentingtechniquesalsoaffected this ability, anddemonstrated a closeconnection between thechildren’s ability to controltheir attention and theirability to control theiremotions.ShaneFrederickconstructed

a Cognitive Reflection Test,whichconsistsofthebat-and-ball problem and two otherquestions, chosen becausethey also invite an intuitiveanswer that is bothcompelling and wrong (thequestionsareshownhere).Hewent on to study thecharacteristics of studentswho score very low on thistest—the supervisoryfunctionofSystem2isweakin these people—and found

that theyareprone toanswerquestions with the first ideathat comes to mind andunwilling to invest the effortneeded to check theirintuitions. Individuals whouncritically follow theirintuitions about puzzles arealso prone to accept othersuggestions from System 1.In particular, they areimpulsive, impatient, andkeen to receive immediategratification. For example,

63% of the intuitiverespondents say they wouldprefer to get $3,400 thismonthratherthan$3,800nextmonth. Only 37% of thosewho solve all three puzzlescorrectly have the sameshortsighted preference forreceiving a smaller amountimmediately. When askedhow much they will pay toget overnight delivery of abook they have ordered, thelow scorers on theCognitive

ReflectionTestarewillingtopaytwiceasmuchasthehighscorers. Frederick’s findingssuggest that thecharactersofour psychodrama havedifferent “personalities.”System 1 is impulsive andintuitive;System2iscapableof reasoning, and it iscautious,butatleastforsomepeople it is also lazy. Werecognize related differencesamong individuals: somepeople are more like their

System2;othersareclosertotheir System 1. This simpletesthasemergedasoneofthebetter predictors of laztestorsoflythinking.Keith Stanovich and his

longtimecollaboratorRichardWest originally introducedthe terms System 1 andSystem2(theynowprefer tospeak of Type 1 and Type 2processes).Stanovichandhiscolleagues have spentdecades studying differences

among individuals in thekindsofproblemswithwhichthis book is concerned. Theyhaveaskedonebasicquestioninmanydifferentways:Whatmakes some people moresusceptible than others tobiases of judgment?Stanovich published hisconclusions in a book titledRationalityandtheReflectiveMind,whichoffersaboldanddistinctive approach to thetopic of this chapter. He

draws a sharp distinctionbetween two parts of System2—indeed, the distinction isso sharp that he calls themseparate “minds.” One ofthese minds (he calls italgorithmic) deals with slowthinking and demandingcomputation. Some peoplearebetterthanothersinthesetasks of brain power—theyaretheindividualswhoexcelin intelligence tests and areable to switch from one task

to another quickly andefficiently. However,Stanovich argues that highintelligence does not makepeople immune to biases.Another ability is involved,which he labels rationality.Stanovich’s concept of arational person is similar towhat I earlier labeled“engaged.” The core of hisargument is that rationalityshould be distinguished fromintelligence. In his view,

superficial or “lazy” thinkingis a flaw in the reflectivemind, a failure of rationality.This is an attractive andthought-provoking idea. Insupport of it, Stanovich andhis colleagues have foundthat the bat-and-ball questionand others like it aresomewhatbetterindicatorsofoursusceptibilitytocognitiveerrors than are conventionalmeasuresofintelligence,suchas IQ tests. Time will tell

whether the distinctionbetween intelligence andrationality can lead to newdiscoveries.

SpeakingofControl

“She did not have tostruggle to stay on taskfor hours. She was in astateofflow.”

“His ego was depletedafter a long day of

meetings. So he justturned to standardoperating proceduresinstead of thinkingthroughtheproblem.”

“He didn’t bother tocheck whether what hesaid made sense. Doeshe usually have a lazySystem 2 or was heunusuallytired?”

“Unfortunately, shetends to say the firstthingthatcomesintohermind.Sheprobablyalsohas trouble delayinggratification. WeakSystem2.”

P

TheAssociativeMachine

To begin your exploration ofthe surprising workings ofSystem 1, look at thefollowingwords:

BananasVomit

A lot happened to you

duringthelastsecondortwo.You experienced someunpleasant images andmemories. Your face twistedslightly in an expression ofdisgust, and you may havepushed this bookimperceptibly farther away.Yourheartrateincreased,thehaironyourarmsrosealittle,and your sweat glands wereactivated. In short, youresponded to the disgustingword with an attenuated

version of how you wouldreact to the actual event. Allof this was completelyautomatic, beyond yourcontrol.There was no particular

reason to do so, but yourmind automatically assumeda temporal sequence and acausal connection betweenthewordsbananasandvomit,formingasketchyscenarioinwhich bananas caused thesickness.Asa result,youare

experiencing a temporaryaversion to bananas (don’tworry,itwillpass).Thestateofyourmemoryhaschangedin other ways: you are nowunusually ready to recognizeand respond to objects andconcepts associated with“vomit,” such as sick, stink,or nausea, and wordsassociated with “bananas,”suchasyellowand fruit, andperhapsappleandberries.Vomiting normally occurs

in specific contexts, such ashangovers and indigestion.Youwouldalsobeunusuallyready to recognize wordsassociated with other causesof the same unfortunateoutcome. Furthermore, yourSystem1noticedthefactthatthe juxtaposition of the twowords is uncommon; youprobablyneverencountereditbefore.Youexperiencedmildsurprise.This complex constellation

of responses occurredquickly, automatically, andeffortlessly.Youdidnotwillitandyoucouldnotstopit.ItwasanoperationofSystem1.Theevents that tookplaceasa result of your seeing thewordshappenedbyaprocesscalled associative activation:ideas that have been evokedtriggermanyotherideas,inaspreading cascade of activityin your brain. The essentialfeatureofthiscomplexsetof

mental events is itscoherence. Each element isconnected, andeach supportsand strengthens the others.The word evokes memories,whichevokeemotions,whichin turn evoke facialexpressions and otherreactions, such as a generaltensing up and an avoidancetendency. The facialexpressionand theavoidancemotion intensify the feelingstowhichtheyarelinked,and

the feelings in turn reinforcecompatible ideas. All thishappens quickly and all atonce, yielding a self-reinforcing pattern ofcognitive, emotional, andphysical responses that isbothdiverseandintegrated—it has been calledassociativelycoherent.In a second or so you

accomplished, automaticallyand unconsciously, aremarkablefeat.Startingfrom

a completely unexpectedevent,yourSystem1madeasmuchsenseaspossibleofthesituation—two simplewords,oddlyjuxtaposed—bylinkingthewordsinacausalstory;itevaluated the possible threat(mild to moderate) andcreated a context for futuredevelopments by preparingyou for events that had justbecome more likely; it alsocreated a context for thecurrent event by evaluating

how surprising it was. Youended up as informed aboutthe past and as prepared forthefutureasyoucouldbe.An odd feature of what

happenedisthatyourSystem1 treated the mereconjunction of two words asrepresentations of reality.Your body reacted in anattenuated replica of areactiontotherealthing,andthe emotional response andphysical recoil were part of

theinterpretationoftheevent.As cognitive scientists haveemphasized in recent years,cognition is embodied; youthink with your body, notonlywithyourbrain.Themechanism that causes

thesemental events has beenknown for a long time: it isthe ass12;velyociation ofideas.Weallunderstandfromexperience that ideas followeach other in our consciousmind in a fairlyorderlyway.

The British philosophers ofthe seventeenth andeighteenth centuries searchedfortherulesthatexplainsuchsequences. In An EnquiryConcerning HumanUnderstanding, published in1748, the Scottishphilosopher David Humereduced the principles ofassociation to three:resemblance, contiguity intimeandplace,andcausality.Our concept of association

has changed radically sinceHume’s days, but his threeprinciplesstillprovideagoodstart.I will adopt an expansive

viewofwhatanideais.Itcanbeconcreteorabstract,anditcan be expressed in manyways:asaverb,asanoun,asanadjective,orasaclenchedfist. Psychologists think ofideas as nodes in a vastnetwork, called associativememory, in which each idea

is linked to many others.There are different types oflinks: causes are linked totheir effects (virus cold);things to their properties(lime green); things to thecategories to which theybelong(banana fruit).Oneway we have advancedbeyond Hume is that we nolonger think of the mind asgoing through a sequence ofconscious ideas, one at atime. In the current view of

how associative memoryworks,agreatdealhappensatonce. An idea that has beenactivated does not merelyevoke one other idea. Itactivates many ideas, whichin turn activate others.Furthermore, only a few ofthe activated ideas willregister in consciousness;most of the work ofassociative thinking is silent,hidden from our consciousselves. The notion that we

have limited access to theworkings of our minds isdifficult to accept because,naturally, it is alien to ourexperience,butitistrue:youknow far less about yourselfthanyoufeelyoudo.

TheMarvelsofPriming

Asiscommoninscience,thefirst big breakthrough in ourunderstanding of the

mechanism of associationwas an improvement in amethod of measurement.Until a few decades ago, theonly way to studyassociationswastoaskmanypeople questions such as,“What is the first word thatcomes to your mind whenyou hear the word DAY?”The researchers tallied thefrequency of responses, suchas “night,” “sunny,” or“long.” In the 1980s,

psychologists discovered thatexposure to a word causesimmediate and measurablechanges in the ease withwhich many related wordscan be evoked. If you haverecently seen or heard theword EAT, you aretemporarily more likely tocomplete the word fragmentSO_P as SOUP than asSOAP. The opposite wouldhappen,ofcourse, ifyouhadjustseenWASH.Wecallthis

a priming effect and say thatthe idea of EAT primes theidea of SOUP, and thatWASHprimesSOAP.Priming effects take many

forms. If the idea of EAT iscurrently on your mind(whether or not you areconscious of it), you will bequicker than usual torecognize the word SOUPwhen it is spoken in awhisper or presented in ablurry font. And of course

you are primed not only fortheideaofsoupbutalsoforamultitude of food-relatedideas,includingfork,hungry,fat, diet, and cookie. If foryourmostrecentmealyousatat a wobbly restaurant table,you will be primed forwobblyaswell.Furthermore,the primed ideas have someability to prime other ideas,although more weakly. Likeripples on a pond, activationspreads through a small part

of the vast network ofassociated ideas. Themapping of these ripples isnowoneof themostexcitingpursuits in psychologicalresearch.

Another major advance inourunderstandingofmemorywas the discovery thatpriming is not restricted toconcepts and words. Youcannot know this fromconscious experience, of

course, but you must acceptthe alien idea that youractions and your emotionscan be primed by events ofwhich you are not evenaware. In an experiment thatbecameaninstantclassic,thepsychologist John Bargh andhis collaborators askedstudents at New YorkUniversity—most agedeighteen to twenty-two—toassemblefour-wordsentencesfroma set of fivewords (for

example, “finds he it yellowinstantly”). For one group ofstudents, half the scrambledsentences contained wordsassociated with the elderly,such as Florida, forgetful,bald,gray, orwrinkle.Whentheyhadcompleted that task,the young participants weresent out to do anotherexperimentinanofficedownthehall.That shortwalkwaswhat the experiment wasabout. The researchers

unobtrusively measured thetime it took people to getfrom one end of the corridorto the other. As Bargh hadpredicted, the young peoplewhohadfashionedasentencefrom words with an elderlytheme walked down thehallway significantly moreslowlythantheothers.

The “Florida effect”involves two stages ofpriming. First, the set of

wordsprimesthoughtsofoldage, though the word old isnever mentioned; second,these thoughts prime abehavior, walking slowly,which is associated with oldage.All thishappenswithoutany awareness. When theywere questioned afterward,noneof the students reportednoticing that the words hadhad a common theme, andthey all insisted that nothingthey did after the first

experiment could have beeninfluencedby thewords theyhadencountered.The ideaofoldagehadnotcometotheirconscious awareness, buttheir actions had changednevertheless.Thisremarkablepriming phenomenon—theinfluencing of an action bythe idea—is known as theideomotor effect. Althoughyousurelywerenotawareofit, reading this paragraphprimed you as well. If you

hadneededtostanduptogeta glass of water, you wouldhave been slightly slowerthan usual to rise from yourchair—unless you happen todislike the elderly, in whichcase research suggests thatyoumighthavebeen slightlyfasterthanusual!The ideomotor link also

works in reverse. A studyconducted in a Germanuniversity was the mirrorimageoftheearlyexperiment

thatBarghandhiscolleagueshadcarriedoutinNewYork.Students were asked to walkaround a room for 5minutesat a rate of 30 steps perminute,whichwasaboutone-thirdtheirnormalpace.Afterthis brief experience, theparticipants were muchquicker to recognize wordsrelated to old age, such asforgetful, old, and lonely.Reciprocal priming effectstend to produce a coherent

reaction: if you were primedtothinkofoldage,youwouldtendtoactold,andactingoldwould reinforce the thoughtofoldage.Reciprocal links are

common in the associativenetwork. For example, beingamused tends to make yousmile, and smiling tends tomake you feel amused. Goahead and take a pencil, andholditbetweenyourteethforafewsecondswiththeeraser

pointingtoyourrightandthepoint to your left. Now holdthe pencil so the point isaimedstraightinfrontofyou,by pursing your lips aroundthe eraser end. You wereprobablyunawarethatoneoftheseactionsforcedyourfaceinto a frown and the otherintoasmile.Collegestudentswereasked to rate thehumorof cartoons from GaryLarson’sThe Far Side whileholding a pencil in their

mouth. Those who were“smiling” (without anyawarenessofdoingso)foundthe cartoons rri221;(withfunnier than did thosewho were “frowning.” Inanother experiment, peoplewhosefacewasshapedintoafrown (by squeezing theireyebrows together) reportedan enhanced emotionalresponsetoupsettingpictures—starving children, peoplearguing, maimed accident

victims.Simple, common gestures

can also unconsciouslyinfluence our thoughts andfeelings. In onedemonstration, people wereasked to listen to messagesthrough new headphones.They were told that thepurpose of the experimentwas to test the quality of theaudio equipment and wereinstructedtomovetheirheadsrepeatedly to check for any

distortionsofsound.Half theparticipants were told to nodtheirheadupanddownwhileothers were told to shake itside to side. The messagesthey heard were radioeditorials.Thosewhonodded(a yes gesture) tended toaccept the message theyheard, but those who shooktheir head tended to reject it.Again, there was noawareness, just a habitualconnection between an

attitude of rejection oracceptance and its commonphysical expression.Youcansee why the commonadmonition to “act calm andkind regardless of how youfeel”isverygoodadvice:youare likely to be rewarded byactually feeling calm andkind.

PrimesThatGuideUs

Studies of priming effectshave yielded discoveries thatthreaten our self-image asconscious and autonomousauthorsofourjudgmentsandour choices. For instance,mostofus thinkofvotingasa deliberate act that reflectsour values and ourassessmentsofpoliciesandisnot influenced byirrelevancies.Ourvoteshouldnot be affected by the

locationofthepollingstation,forexample,butitis.Astudyofvotingpatternsinprecinctsof Arizona in 2000 showedthat the support forpropositions to increase thefunding of schools wassignificantlygreaterwhenthepolling station was in aschool thanwhen itwas in anearby location. A separateexperiment showed thatexposingpeopletoimagesofclassrooms and school

lockers also increased thetendency of participants tosupport a school initiative.Theeffectof the imageswaslarger than the differencebetween parents and othervoters! The study of priminghascomesomewayfromtheinitial demonstrations thatreminding people of old agemakes them walk moreslowly. We now know thatthe effects of priming canreachintoeverycornerofour

lives.Reminders of money

produce some troublingeffects. Participants in oneexperimentwereshownalistof five words from whichthey were required toconstruct a four-word phrasethat had a money theme(“high a salary desk paying”became “a high-payingsalary”). Other primes weremuch more subtle, includingthe presence of an irrelevant

money-related object in thebackground, such as a stackof Monopoly money on atable, or a computer with ascreen saver of dollar billsfloatinginwater.Money-primed people

become more independentthan they would be withoutthe associative trigger. Theypersevered almost twice aslongin tryingtosolveaverydifficult problem before theyasked the experimenter for

help,acrispdemonstrationofincreased self-reliance.Money-primed people arealso more selfish: they weremuch less willing to spendtime helping another studentwhopretendedtobeconfusedabout an experimental task.When an experimenterclumsily dropped a bunch ofpencils on the floor, theparticipants with money(unconsciously)ontheirmindpicked up fewer pencils. In

another experiment in theseries, participants were toldthattheywouldshortlyhaveaget-acquainted conversationwithanotherpersonandwereasked to set up two chairswhiletheexperimenterlefttoretrieve that person.Participantsprimedbymoneychose in the exto stay muchfarther apart than theirnonprimed peers (118 vs. 80centimeters). Money-primedundergraduatesalsoshoweda

greater preference for beingalone.The general theme of these

findings is that the idea ofmoney primes individualism:a reluctance to be involvedwith others, to depend onothers, or to accept demandsfromothers.Thepsychologistwhohasdonethisremarkableresearch,KathleenVohs, hasbeen laudably restrained indiscussingtheimplicationsofher findings, leaving the task

to her readers. Herexperiments are profound—her findings suggest thatliving in a culture thatsurrounds us with remindersof money may shape ourbehavior and our attitudes inways that we do not knowabout and of which we maynot be proud. Some culturesprovidefrequentremindersofrespect, others constantlyremind their members ofGod, and some societies

prime obedience by largeimages of the Dear Leader.Can there be any doubt thattheubiquitousportraitsofthenational leader in dictatorialsocietiesnotonlyconvey thefeeling that “Big Brother IsWatching”butalsoleadtoanactual reduction inspontaneous thought andindependentaction?The evidence of priming

studies suggests thatreminding people of their

mortalityincreasestheappealof authoritarian ideas, whichmaybecomereassuringinthecontextoftheterrorofdeath.Other experiments haveconfirmed Freudian insightsabouttheroleofsymbolsandmetaphors in unconsciousassociations. For example,consider theambiguouswordfragmentsW_ _H and S_ _P. People whowere recentlyaskedtothinkofanactionofwhich they are ashamed are

morelikelytocompletethosefragments as WASH andSOAP and less likely to seeWISH and SOUP.Furthermore,merely thinkingabout stabbingacoworker inthe back leaves people moreinclined to buy soap,disinfectant,ordetergentthanbatteries,juice,orcandybars.Feeling that one’s soul isstained appears to trigger adesire to cleanse one’s body,an impulse that has been

dubbed the “Lady Macbetheffect.”The cleansing is highly

specific to the body partsinvolvedinasin.Participantsin an experiment wereinduced to “lie” to animaginary person, either onthe phone or in e-mail. In asubsequent test of thedesirability of variousproducts,peoplewhohadliedon the phone preferredmouthwash over soap, and

thosewho had lied in e-mailpreferredsoaptomouthwash.When I describe priming

studies to audiences, thereaction is often disbelief.Thisisnotasurprise:System2believes that it is inchargeand that itknows thereasonsfor its choices.Questions areprobablycroppingupinyourmind as well: How is itpossible for such trivialmanipulations of the contexttohavesuchlargeeffects?Do

these experimentsdemonstrate that we arecompletely at the mercy ofwhatever primes theenvironment provides at anymoment?Of course not. Theeffects of the primes arerobust but not necessarilylarge. Among a hundredvoters, only a few whoseinitial preferences wereuncertainwillvotedifferentlyabout a school issue if theirprecinctislocatedinaschool

ratherthaninachurch—butafew percent could tip anelection.The idea you should focus

on, however, is that disbeliefis not an option. The resultsarenotmadeup,noraretheystatisticalflukes.Youhavenochoice but to accept that themajor conclusions of thesestudies are true. Moreimportant, you must acceptthattheyaretrueaboutyou.Ifyou had been exposed to a

screensaveroffloatingdollarbills, you too would likelyhavepickedupfewerpencilsto help a clumsy stranger.Youdonotbelievethattheseresults apply to you becausetheycorrespondtonothinginyour subjective experience.But your subjectiveexpefteelief. Trience consistslargely of the story that yourSystem 2 tells itself aboutwhat is going on. PrimingphenomenaariseinSystem1,

and you have no consciousaccesstothem.I conclude with a perfect

demonstration of a primingeffect, which was conductedin an office kitchen at aBritish university. For manyyearsmembers of that officehadpaidfor theteaorcoffeeto which they helpedthemselvesduringthedaybydropping money into an“honesty box.” A list ofsuggested prices was posted.

Onedayabannerposterwasdisplayedjustabovethepricelist, with no warning orexplanation. For a period often weeks a new image waspresented each week, eitherflowersoreyesthatappearedto be looking directly at theobserver.Noonecommentedon the new decorations, butthe contributions to thehonesty box changedsignificantly.Thepostersandthe amounts that people put

into the cash box (relative tothe amount they consumed)are shown in figure 4. Theydeserveacloselook.

Figure4

On the first week of theexperiment (which you cansee at the bottom of thefigure), two wide-open eyesstare at the coffee or teadrinkers, whose averagecontributionwas70penceperliter ofmilk.Onweek2, theposter shows flowers andaveragecontributionsdrop toabout 15 pence. The trend

continues. On average, theusers of the kitchencontributed almost threetimesasmuchin“eyeweeks”as they did in “flowerweeks.” Evidently, a purelysymbolic reminder of beingwatched prodded people intoimproved behavior. As weexpectatthispoint,theeffectoccurs without anyawareness. Do you nowbelieve that you would alsofallintothesamepattern?

Some years ago, thepsychologistTimothyWilsonwrote a book with theevocative title Strangers toOurselves. You have nowbeen introduced to thatstranger in you, which maybeincontrolofmuchofwhatyou do, although you rarelyhaveaglimpseofit.System1provides the impressions thatoften turn into your beliefs,and is the source of theimpulses that often become

your choices and youractions. It offers a tacitinterpretation of whathappens to you and aroundyou, linking the presentwiththe recent past and withexpectations about the nearfuture. It contains the modelof the world that instantlyevaluateseventsasnormalorsurprising. It is the source ofyour rapid and often preciseintuitive judgments. And itdoes most of this without

your conscious awareness ofitsactivities.System1isalso,as we will see in thefollowingchapters,theoriginof many of the systematicerrorsinyourintuitions.

SpeakingofPriming

“The sight of all thesepeople in uniforms doesnotprimecreativity.”

“Theworldmakesmuch

less sense than youthink. The coherencecomes mostly from thewayyourmindworks.”

“They were primed tofind flaws, and this isexactly what theyfound.”

“His System 1constructed a story, andhisSystem2believedit.

Ithappenstoallel

“I made myself smileand I’m actually feelingbetter!”

P

CognitiveEase

Wheneveryouareconscious,and perhaps even when youare not, multiplecomputationsaregoingon inyour brain, which maintainandupdatecurrentanswerstosome key questions: Isanything new going on? Isthere a threat? Are thingsgoing well? Should my

attention be redirected? Ismore effort needed for thistask? You can think of acockpit, with a set of dialsthat indicate the currentvalues of each of theseessential variables. Theassessments are carried outautomatically by System 1,and one of their functions isto determine whether extraeffort is required fromSystem2.One of the dials measures

cognitive ease, and its rangeis between “Easy” and“Strained.”Easyisasignthatthings are going well—nothreats, no major news, noneed to redirect attention ormobilize effort. Strainedindicates that a problemexists, which will requireincreased mobilization ofSystem 2. Conversely, youexperience cognitive strain.Cognitivestrainisaffectedbyboththecurrentlevelofeffort

and the presence of unmetdemands.Thesurprise is thatasingledialofcognitiveeaseis connected to a largenetworkofdiverseinputsandoutputs. Figure 5 tells thestory.The figure suggests that a

sentence that is printed in aclear font, or has beenrepeated,orhasbeenprimed,will be fluently processedwith cognitive ease. Hearinga speaker when you are in a

good mood, or even whenyou have a pencil stuckcrosswise in your mouth tomake you “smile,” alsoinduces cognitive ease.Conversely, you experiencecognitive strain when youread instructions in a poorfont, or in faint colors, orworded in complicatedlanguage,orwhenyouareina bad mood, and even whenyoufrown.

Figure5.Causes

andConsequencesofCognitiveEase

The various causes of ease

orstrainhaveinterchangeableeffects. When you are in astate of cognitive ease, youareprobablyinagoodmood,like what you see, believewhat you hear, trust yourintuitions, and feel that thecurrent situation iscomfortablyfamiliar.Youarealso likely to be relativelycasualandsuperficialinyourthinking. When you feel

strained, you aremore likelytobevigilant and suspicious,invest more effort in whatyou are doing, feel lesscomfortable, andmake fewererrors, but you also are lessintuitive and less creativethanusual.

IllusionsofRemembering

The word illusion bringsvisual illusions to mind,

because we are all familiarwith pictures that mislead.But vision is not the onlydomain of illusions; memoryisalsosusceptibletothem,asisthinkingmoregenerally.David Stenbill, Monica

Bigoutski, Sh"imight=s ispictanaTirana.Ijustmadeupthesenames.Ifyouencounterany of them within the nextfewminutesyouarelikelytoremember where you sawthem. You know, and will

know for a while, that theseare not the names of minorcelebrities.Butsupposethatafew days from now you areshown a long list of names,including some minorcelebrities and “new” namesofpeoplethatyouhaveneverheardof;your taskwillbetocheck every name of acelebrityinthelist.Thereisasubstantial probability thatyou will identify DavidStenbill as a well-known

person,althoughyouwillnot(ofcourse)knowwhetheryouencountered his name in thecontext of movies, sports, orpolitics. Larry Jacoby, thepsychologist who firstdemonstrated this memoryillusion in the laboratory,titled his article “BecomingFamous Overnight.” Howdoes this happen? Start byasking yourself how youknow whether or notsomeone is famous. In some

cases of truly famous people(or of celebrities in an areayou follow), you have amental file with richinformationaboutaperson—think Albert Einstein, Bono,HillaryClinton.But youwillhave no file of informationabout David Stenbill if youencounter his name in a fewdays. All you will have is asense of familiarity—youhave seen this namesomewhere.

Jacoby nicely stated theproblem: “The experience offamiliarity has a simple butpowerfulqualityof‘pastness’that seems to indicate that itis a direct reflection of priorexperience.” This quality ofpastness is an illusion. Thetruth is, as Jacoby andmanyfollowers have shown, thatthe nameDavid Stenbill willlookfamiliarwhenyouseeitbecause you will see it moreclearly.Words that youhave

seen before become easier tosee again—you can identifythem better than otherwordswhen they are shown verybriefly or masked by noise,andyouwillbequicker(byafew hundredths of a second)to read them than to readother words. In short, youexperience greater cognitiveeaseinperceivingawordyouhaveseenearlier,anditisthissense of ease that gives youtheimpressionoffamiliarity.

Figure 5 suggests a way totestthis.Chooseacompletelynew word, make it easier tosee,anditwillbemorelikelyto have the quality ofpastness.Indeed,anewwordis more likely to berecognizedas familiar if it isunconsciously primed byshowing it for a fewmilliseconds just before thetest, or if it is shown insharper contrast than someother words in the list. The

linkalsooperatesintheotherdirection. Imagine you areshownalistofwordsthataremore or less out of focus.Some of the words areseverely blurred, others lessso,andyourtaskistoidentifythe words that are shownmoreclearly.Awordthatyouhave seen recently willappear to be clearer thanunfamiliarwords.Asfigure5indicates,thevariouswaysofinducing cognitive ease or

strain are interchangeable;you may not know preciselywhat it is that makes thingscognitively easy or strained.This is how the illusion offamiliaritycomesabout.

IllusionsofTruth“NewYork is a large city inthe United States.” “ThemoonrevolvesaroundEarth.”“Achickenhasfourlegs.”Inall these cases, you quickly

retrieved a great deal ofrelated information, almostall pointing one way oranother.Youknewsoonafterreadingthemthatthefirsttwostatements are true and thelast one is false. Note,however, that the statement“Achickenhas three legs” ismoreobviouslyfalsethan“Achicken has four legs.”Yourassociative machinery slowsthe judgment of the lattersentence by delivering the

fact that many animals havefour legs, and perhaps alsothat supermarkets often sellchickenordblurred, legs inpackages of four. System 2was involved in sifting thatinformation, perhaps raisingthe issue of whether thequestion about New Yorkwastooeasy,orcheckingthemeaningofrevolves.Think of the last time you

took a driving test. Is it truethat you need a special

licensetodriveavehiclethatweighsmorethanthreetons?Perhapsyoustudiedseriouslyandcanrememberthesideofthepageonwhichtheanswerappeared,aswellasthelogicbehindit.Thisiscertainlynothow I passed driving testswhenImovedtoanewstate.My practice was to read thebookletof rulesquicklyonceandhopeforthebest.Iknewsomeoftheanswersfromtheexperience of driving for a

long time. But there werequestions where no goodanswer came tomind,whereall I had to go by wascognitive ease. If the answerfeltfamiliar,Iassumedthatitwas probably true. If itlooked new (or improbablyextreme), I rejected it. Theimpression of familiarity isproduced by System 1, andSystem 2 relies on thatimpression for a true/falsejudgment.

Thelessonoffigure5isthatpredictable illusionsinevitablyoccurifajudgmentis based on an impression ofcognitive ease or strain.Anythingthatmakesiteasierfortheassociativemachinetorun smoothly will also biasbeliefs. A reliable way tomake people believe infalsehoods is frequentrepetition,becausefamiliarityis not easily distinguishedfrom truth. Authoritarian

institutions and marketershave alwaysknown this fact.Butitwaspsychologistswhodiscovered that you do nothave to repeat the entirestatementof a factor idea tomake it appear true. Peoplewhowererepeatedlyexposedto the phrase “the bodytemperature of a chicken”weremorelikelytoacceptastrue the statement that “thebody temperature of achickenis144°”(oranyother

arbitrary number). Thefamiliarity of one phrase inthe statement sufficed tomake the whole statementfeel familiar, and thereforetrue. Ifyoucannot rememberthesourceofastatement,andhave no way to relate it toother things you know, youhavenooptionbuttogowiththesenseofcognitiveease.

HowtoWritea

PersuasiveMessageSuppose you must write amessage that you want therecipients to believe. Ofcourse, yourmessagewill betrue, but that is notnecessarilyenoughforpeopletobelieve that it is true. It isentirely legitimate for you toenlist cognitive ease to workin your favor, and studies oftruth illusions providespecific suggestions thatmay

helpyouachievethisgoal.Thegeneralprincipleisthat

anythingyoucandotoreducecognitive strain will help, soyou should first maximizelegibility.Comparethesetwostatements:

Adolf Hitler was bornin1892.AdolfHitlerwasbornin1887.

Both are false (Hitler was

born in 1889), butexperiments have shown thatthe first is more likely to bebelieved. More advice: ifyourmessageistobeprinted,use high-quality paper tomaximize the contrastbetween characters and theirbackground.Ifyouusecolor,you are more likely to bebelievedifyourtextisprintedin bright blue or red than inmiddling shades of green,yellow,orpaleblue.

If you care about beingthought credible andintelligent, do not usecomplex language wheresimplerlanguagewilldo.MyPrinceton ton colleagueDanny Oppenheimer refuteda myth prevalent a wo toncolmong undergraduatesabout the vocabulary thatprofessors find mostimpressive.Inanarticletitled“Consequences of EruditeVernacular Utilized

Irrespective of Necessity:Problems with Using LongWords Needlessly,” heshowed that couchingfamiliar ideas in pretentiouslanguageistakenasasignofpoor intelligence and lowcredibility.In addition to making your

message simple, try to makeitmemorable. Put your ideasinverse ifyoucan; theywillbemore likely tobe takenastruth. Participants in a much

cited experiment read dozensofunfamiliaraphorisms,suchas:

Woesunitefoes.Littlestrokeswilltumblegreatoaks.Afaultconfessedishalfredressed.

Other students read some ofthe same proverbstransformed into nonrhymingversions:

Woesuniteenemies.Littlestrokeswilltumblegreattrees.A fault admitted is halfredressed.

The aphorisms were judgedmore insightful when theyrhymed than when they didnot.Finally, if you quote a

source, choose one with aname that is easy topronounce. Participants in an

experiment were asked toevaluate the prospects offictitious Turkish companieson the basis of reports fromtwobrokeragefirms.Foreachstock,oneofthereportscamefrom an easily pronouncedname (e.g., Artan) and theotherreportcamefromafirmwith an unfortunate name(e.g., Taahhut). The reportssometimes disagreed. Thebest procedure for theobserverswouldhavebeento

average the two reports, butthis is not what they did.Theygavemuchmoreweightto thereport fromArtan thanto the report from Taahhut.Remember that System 2 islazyand thatmental effort isaversive. If possible, therecipients of your messagewant to stay away fromanythingthatremindsthemofeffort,includingasourcewithacomplicatedname.Allthisisverygoodadvice,

butweshouldnotgetcarriedaway. High-quality paper,brightcolors,andrhymingorsimple language will not bemuchhelpifyourmessageisobviouslynonsensical,orifitcontradicts facts that youraudience knows to be true.The psychologists who dothese experiments do notbelievethatpeoplearestupidor infinitely gullible. Whatpsychologists do believe isthatallofuslivemuchofour

lifeguidedbytheimpressionsof System 1—and we oftendo not know the source ofthese impressions. How doyouknow that a statement istrue? If it is strongly linkedby logic or association toother beliefs or preferencesyou hold, or comes from asourceyoutrustandlike,youwill feel a senseof cognitiveease.Thetroubleisthattheremaybeothercauses foryourfeelingofease—includingthe

quality of the font and theappealingrhythmoftheprose—and you have no simpleway of tracing your feelingsto their source. This is themessageoffigure5:thesenseofeaseor strainhasmultiplecauses, and it is difficult totease them apart. Difficult,but not impossible. Peoplecan overcome some of thesuperficial factors thatproduce illusions of truthwhen strongly motivated to

do so. On most occasions,however, the lazy System 2will adopt the suggestions ofSystem1andmarchon.

StrainandEffortThe symmetry of manyassociativeconnectionswasadominant theme in thediscussion of associativecoherence.Aswesawearlier,people who are made to“smile” or “frown” by

sticking a pencil in theirmouth or holding a ballbetweentheirfurrowedbrowsare prone to experience theemotions that frowning andsmiling normally express.The same self-reinforcingreciprocityisfoundinstudiesofcognitiveease.Ontheonehand, cognitive strain isexperienced when theeffortfuloperationsofSystem2 are engaged. On the otherhand, the experience of

cognitive strain, whatever itssource, tends to mobilizeSystem 2, shifting people’sapproach to problems from acasual intuitive mode to amore engaged and analyticmode.The bat-and-ball problem

was mentioned earlier as atest of people’s tendency toanswer questions with thefirst idea that comes to theirmind, without checking it.Shane Frederick’s Cognitive

Reflection Test consists ofthe bat-and-ball problem andtwo others, all chosenbecause they evoke animmediate intuitive answerthat is incorrect. The othertwoitemsintheCRTare:

If it takes 5machines 5minutes to make 5widgets, how longwould it take 100machines to make 100widgets?

100 minutes OR 5minutes

Inalake,thereisapatchof lily pads. Every day,thepatchdoublesinsize.Ifittakes48daysforthepatch tocover theentirelake, how longwould ittake for the patch tocoverhalfofthelake?24daysOR47days

The correct answers to both

problemsare in a footnote atthe bottom of the page.* Theexperimenters recruited 40PrincetonstudentstotaketheCRT. Half of them saw thepuzzles in a small font inwashed-out gray print. Thepuzzles were legible, but thefont inducedcognitive strain.The results tell a clear story:90%ofthestudentswhosawtheCRTinnormalfontmadeat least one mistake in the

test, but the proportiondropped to 35% when thefont was barely legible. Youread this correctly:performance was better withthebadfont.Cognitivestrain,whatever its source,mobilizesSystem2,which ismore likely to reject theintuitiveanswersuggestedbySystem1.

ThePleasureof

CognitiveEaseAn article titled “Mind atEase Puts a Smile on theFace” describes anexperiment in whichparticipants were brieflyshown pictures of objects.Some of these pictures weremade easier to recognize byshowing the outline of theobject just before thecomplete image was shown,so briefly that the contours

were never noticed.Emotional reactions weremeasured by recordingelectrical impulses fromfacial muscles, registeringchangesofexpressionthataretoo slight and toobrief to bedetectable by observers. Asexpected, people showed afaintsmileandrelaxedbrowswhenthepictureswereeasierto see. It appears to be afeature of System 1 thatcognitive ease is associated

withgoodfeelings.As expected, easily

pronounced words evoke afavorableattitude.Companieswith pronounceable namesdmisorrectlo better thanothersfor thefirstweekafterthestockisissued,thoughtheeffect disappears over time.Stocks with pronounceabletradingsymbols(likeKARorLUNMOO)outperform thosewith tongue-twisting tickerslikePXGorRDO—and they

appear to retain a smalladvantageoversome time.Astudy conducted inSwitzerland found thatinvestors believe that stockswithfluentnameslikeEmmi,Swissfirst, and Comet willearnhigherreturnsthanthosewith clunky labels likeGeberitandYpsomed.As we saw in figure 5,

repetition induces cognitiveeaseandacomfortingfeelingof familiarity. The famed

psychologist Robert Zajoncdedicatedmuch of his careerto the study of the linkbetween the repetition of anarbitrary stimulus and themild affection that peopleeventuallyhaveforit.Zajonccalled it the mere exposureeffect. A demonstrationconducted in the studentnewspapers of theUniversityofMichiganandofMichiganStateUniversityisoneofmyfavorite experiments. For a

periodofsomeweeks,anad-likeboxappearedonthefrontpage of the paper, whichcontained one of thefollowing Turkish (orTurkish-sounding) words:kadirga, saricik, biwonjni,nansoma, and iktitaf. Thefrequency with which thewords were repeated varied:one of thewordswas shownonly once, the othersappearedontwo,five,ten,ortwenty-five separate

occasions. (The words thatwere presentedmost often inone of the university paperswere the least frequent in theother.) No explanation wasoffered, and readers’ querieswere answered by thestatement that “the purchaserof the display wished foranonymity.”When themysterious series

of ads ended, theinvestigators sentquestionnaires to the

university communities,asking for impressions ofwhether each of the words“means something ‘good’ orsomething‘bad.’”Theresultswere spectacular: the wordsthat were presented morefrequently were rated muchmore favorably than thewords that had been shownonly once or twice. Thefindinghasbeenconfirmedinmany experiments, usingChinese ideographs, faces,

and randomly shapedpolygons.The mere exposure effect

does not depend on theconscious experience offamiliarity. In fact, the effectdoes not depend onconsciousnessatall:itoccurseven when the repeatedwords or pictures are shownso quickly that the observersnever become aware ofhaving seen them. They stillend up liking the words or

pictures that were presentedmore frequently. As shouldbe clear by now, System 1canrespondtoimpressionsofevents of which System 2 isunaware. Indeed, the mereexposure effect is actuallystronger for stimuli that theindividual never consciouslysees.Zajoncarguedthattheeffect

of repetition on liking is aprofoundly importantbiological fact, and that it

extends to all animals. Tosurvive in a frequentlydangerous world, anorganism should reactcautiously to a novelstimulus, with withdrawaland fear. Survival prospectsarepoorforananimal that isnot suspicious of novelty.However, it is also adaptivefor the initial caution to fadeif the stimulus is actuallysafe. The mere exposureeffectoccurs,Zajoncclaimed,

because the repeatedexposure of a stimulus isfollowed by nothing bad.Such a stimulus willeventually become a safetysignal, and safety is good.Obviously, this argument isnot restricted to humans. Tomake that point, one ofZajonc’s associates exposedtwo sets of fertile chickeneggs to different tones.Afterthey hatched, the chicksconsistently emitted fewer

distresscallswhenexposedtothetonetheyhadheardwhileinhabitingtheshell.Zajonc offered an eloquent

summary of hing ictsprogramofresearch:

The consequences ofrepeated exposuresbenefit the organism inits relations to theimmediate animate andinanimate environment.Theyallowtheorganism

to distinguish objectsandhabitatsthataresafefrom those that are not,and they are the mostprimitive basis of socialattachments. Therefore,they form the basis forsocial organization andcohesion—the basicsourcesofpsychologicalandsocialstability.

The link between positiveemotionandcognitiveeasein

System 1 has a longevolutionaryhistory.

Ease,Mood,andIntuition

Around 1960, a youngpsychologist named SarnoffMednick thought he hadidentified the essence ofcreativity. His idea was assimple as it was powerful:creativity is associativememory that works

exceptionally well. He madeup a test, called the RemoteAssociation Test (RAT),which is still often used instudiesofcreativity.For an easy example,

consider the following threewords:

cottageSwisscakeCanyou thinkofaword thatis associated with all three?Youprobablyworkedoutthattheanswerischeese.Nowtrythis:

divelightrocketThisproblemismuchharder,but it has a unique correctanswer, which every speakerof English recognizes,although less than 20% of asample of students found itwithin 15 seconds. Theanswer is sky.Of course,notevery triad of words has asolution. For example, thewords dream, ball, book donothaveasharedassociationthat everyone will recognize

asvalid.Several teams of German

psychologists that havestudied the RAT in recentyears have come up withremarkable discoveries aboutcognitive ease. One of theteams raised two questions:Canpeoplefeelthatatriadofwords has a solution beforethey know what the solutionis?Howdoesmoodinfluenceperformance in this task? Tofindout,theyfirstmadesome

of their subjects happy andotherssad,byaskingthemtothink for several minutesabout happy or sad episodesin their lives. Then theypresented these subjectswithaseriesoftriads,halfofthemlinked (such as dive, light,rocket) and half unlinked(such as dream, ball, book),and instructed them to pressoneof twokeysveryquicklyto indicate their guess aboutwhether the triadwas linked.

The time allowed for thisguess, 2 seconds, was muchtoo short for the actualsolution to come to anyone’smind.The first surprise is that

people’s guesses are muchmore accurate than theywould be by chance. I findthis astonishing. A sense ofcognitive ease is apparentlygenerated by a very faintsignal from the associativemachine,which“knows”that

the three words are coherent(share an association) longbefore the association isretrieved. The role ofcognitive ease in thejudgment was confirmedexperimentally by anotherGerman team: manipulationsthat increase cognitive ease(priming, a clear font, pre-exposing words) all increasethetendencytoseethewordsaslinked.Another remarkable

discovery is the powerfuleffect of mood on thisintuitive performance. Theexperimentershape tendecomputed an “intuitionindex” to measure accuracy.They found that putting theparticipants in a good moodbefore the test by havingthem think happy thoughtsmore than doubled accuracy.An evenmore striking resultisthatunhappysubjectswerecompletely incapable of

performing the intuitive taskaccurately;theirguesseswerenobetter thanrandom.Moodevidently affects theoperation of System 1:whenwe are uncomfortable andunhappy,we lose touchwithourintuition.These findings add to the

growing evidence that goodmood, intuition, creativity,gullibility, and increasedreliance on System 1 form acluster. At the other pole,

sadness, vigilance, suspicion,an analytic approach, andincreased effort also gotogether. A happy moodloosensthecontrolofSystem2 over performance:when inagoodmood,peoplebecomemore intuitive and morecreativebutalso lessvigilantand more prone to logicalerrors. Here again, as in themere exposure effect, theconnection makes biologicalsense. A good mood is a

signal that things aregenerally going well, theenvironment is safe, and it isall right to let one’s guarddown.A badmood indicatesthatthingsarenotgoingverywell, there may be a threat,and vigilance is required.Cognitiveeaseisbothacauseand a consequence of apleasantfeeling.The Remote Association

Testhasmoretotellusaboutthe link between cognitive

ease and positive affect.Brieflyconsidertwotriadsofwords:

sleepmailswitchsaltdeepfoam

You could not know it, ofcourse, but measurements ofelectrical activity in themuscles of your face wouldprobablyhaveshownaslightsmile when you read thesecond triad, which iscoherent(sea isthesolution).This smiling reaction to

coherenceappearsinsubjectswho are told nothing aboutcommon associates; they aremerely shown a verticallyarranged triad of words andinstructed to press the spacebar after they have read it.The impression of cognitiveease that comes with thepresentation of a coherenttriad appears to be mildlypleasurableinitself.The evidence that we have

about good feelings,

cognitive ease, and theintuition of coherence is, asscientists say, correlationalbut not necessarily causal.Cognitive ease and smilingoccur together, but do thegoodfeelingsactuallyleadtointuitions of coherence?Yes,they do. The proof comesfrom a clever experimentalapproach that has becomeincreasingly popular. Someparticipants were given acover story that provided an

alternative interpretation fortheir good feeling: theyweretold about music played intheirearphonesthat“previousresearch showed that thismusic influences theemotional reactions ofindividuals.” This storycompletely eliminates theintuition of coherence. Thefinding shows that the briefemotional response thatfollows the presentation of atriadofwords(pleasantifthe

triad is coherent, unpleasantotherwise) is actually thebasis of judgments ofcoherence. There is nothingherethatSystem1cannotdo.Emotional changes are nowexpected, and because theyare unsurprising they are notlinkedcausallytothewords.This is as good as

psychological research evergets, in its combination ofexperimental techniques andin its results, which are both

robust and extremelysurprising.Wehavelearnedagreatdealabouttheautomaticworkings of System 1 in thelast decades. Much of whatwe now know would havesounded like science fictionthirty or forty years ago. Itwas beyond imagining thatbadfontinfluencesjudgmentsof truth and improvescognitive performance, orthatanemotionalresponsetothe cognitive ease of a tri pr

that aad of words mediatesimpressions of coherence.Psychology has come a longway.

SpeakingofCognitiveEase

“Let’s not dismiss theirbusiness plan justbecause the font makesithardtoread.”

“Wemustbeinclinedtobelieve it because it hasbeen repeated so often,butlet’s thinkit throughagain.”

“Familiarity breedsliking. This is a mereexposureeffect.”

“I’m in a very goodmood today, and mySystem2isweakerthan

usual. I should be extracareful.”

P

Norms,Surprises,andCauses

The central characteristicsand functions of System 1andSystem2havenowbeenintroduced, with a moredetailed treatment of System1. Freely mixing metaphors,we have in our head aremarkably powerfulcomputer, not fast by

conventional hardwarestandards, but able torepresent the structure of ourworld by various types ofassociative links in a vastnetwork of various types ofideas. The spreading ofactivation in the associativemachine isautomatic,butwe(System2)havesomeabilityto control the search ofmemory,andalsotoprogramit so that the detection of anevent in theenvironmentcan

attract attention.We next gointo more detail of thewonders and limitation ofwhatSystem1cando.

AssessingNormalityThemainfunctionofSystem1is tomaintainandupdateamodel of your personalworld,whichrepresentswhatis normal in it. Themodel isconstructed by associationsthat link ideas of

circumstances, events,actions, and outcomes thatco-occur with someregularity, either at the sametime or within a relativelyshort interval. As these linksare formed and strengthened,thepatternofassociatedideascomes to represent thestructure of events in yourlife, and it determines yourinterpretation of the presentas well as your expectationsofthefuture.

Acapacityforsurpriseisanessentialaspectofourmentallife, and surprise itself is themost sensitive indication ofhowweunderstandourworldand what we expect from it.There are twomain varietiesof surprise. Someexpectations are active andconscious—you know youare waiting for a particularevent to happen. When thehour is near, you may beexpecting the sound of the

door as your child returnsfrom school; when the dooropens you expect the soundof a familiar voice.Youwillbe surprised if an activelyexpected event does notoccur. But there is a muchlargercategoryofevents thatyou expect passively; youdon’t wait for them, but youare not surprised when theyhappen.Theseareeventsthatare normal in a situation,though not sufficiently

probable to be activelyexpected.Asingleincidentmaymake

a recurrence less surprising.Someyearsago,mywifeandI were of dealWhennormvacationing in a smallisland resort on the GreatBarrier Reef. There are onlyforty guest rooms on theisland. When we came todinner, we were surprised tomeet an acquaintance, apsychologist named Jon. We

greeted each other warmlyand commented on thecoincidence. Jon left theresort the next day. Abouttwoweekslater,wewereinatheater in London. Alatecomersatnexttomeafterthe lights went down. Whenthe lights came up for theintermission, I saw that myneighbor was Jon. My wifeand I commented later thatwe were simultaneouslyconscious of two facts: first,

this was a more remarkablecoincidence than the firstmeeting; second, we weredistinctly less surprised tomeet Jon on the secondoccasionthanwehadbeenonthe first. Evidently, the firstmeeting had somehowchangedtheideaofJoninourminds. He was now “thepsychologist who shows upwhenwe travel abroad.”We(System 2) knew this was aludicrous idea, but our

System 1 had made it seemalmostnormaltomeetJoninstrange places. We wouldhaveexperiencedmuchmoresurprise if we had met anyacquaintance other than Jonin the next seat of a Londontheater. By any measure ofprobability, meeting Jon inthe theater was much lesslikely than meeting any oneof our hundreds ofacquaintances—yet meetingJonseemedmorenormal.

Under some conditions,passive expectations quicklyturn active, as we found inanother coincidence. On aSunday evening some yearsago, we were driving fromNewYorkCity to Princeton,as we had been doing everyweekforalongtime.Wesawanunusualsight:acaronfirebythesideoftheroad.Whenwe reached the same stretchofroadthefollowingSunday,another car was burning

there. Here again, we foundthat we were distinctly lesssurprised on the secondoccasionthanwehadbeenonthe first. This was now “theplace where cars catch fire.”Because thecircumstancesoftherecurrencewerethesame,the second incident wassufficient to create an activeexpectation: for months,perhaps for years, after theevent we were reminded ofburning cars whenever we

reached that spot of the roadand were quite prepared toseeanotherone(butofcourseweneverdid).The psychologist Dale

MillerandIwroteanessayinwhich we attempted toexplain how events come tobe perceived as normal orabnormal. I will use anexamplefromourdescriptionof “norm theory,” althoughmy interpretation of it haschangedslightly:

An observer, casuallywatchingthepatronsataneighboring table in afashionable restaurant,notices that the firstguest to taste the soupwinces,asifinpain.Thenormalityof amultitudeofeventswillbealteredby this incident. It isnowunsurprisingfortheguestwhofirsttastedthesoup to startle violentlywhen touched by a

waiter; it is alsounsurprising for anotherguesttostifleacrywhentasting soup from thesame tureen. Theseevents and many othersappearmorenormalthanthey would haveotherwise, but notnecessarily because theyconfirm advanceexpectations. Rather,they appear normalbecause they recruit the

originalepisode,retrieveitfrommemory,andareinterpreted inconjunctionwithit.

Imagine yourself the

observer at the restaurant.You were surprised by thefirst guest’s unusual reactionto the soup, and surprisedagainbythestartledresponseto the waiter’s touch.However, the secondabnormal event will retrieve

the first from memory, andboth make sense together.The two events fit into apattern, inwhich theguest isanexceptionallytenseperson.Ontheotherhand,ifthenextthing that happens after thefirst guest’s grimace is thatanother customer rejects thesoup,thesetwosurpriseswillbe linked and thehinsur soupwillsurelybeblamed.“Howmanyanimalsofeach

kind didMoses take into the

ark?” The number of peoplewho detect what is wrongwith thisquestion issosmallthat it has been dubbed the“Mosesillusion.”Mosestooknoanimalsintotheark;Noahdid. Like the incident of thewincing soup eater, theMoses illusion is readilyexplained by norm theory.The idea of animals goinginto thearksetsupabiblicalcontext, and Moses is notabnormalinthatcontext.You

didnotpositivelyexpecthim,but the mention of his nameisnotsurprising.ItalsohelpsthatMosesandNoahhavethesame vowel sound andnumber of syllables.Aswiththe triads that producecognitive ease, youunconsciously detectassociative coherencebetween “Moses” and “ark”and so quickly accept thequestion.ReplaceMoseswithGeorge W. Bush in this

sentenceandyouwillhaveapoor political joke but noillusion.When something cement

does not fit into the currentcontextofactivatedideas,thesystem detects anabnormality, as you justexperienced. You had noparticular idea of what wascoming after something, butyou knew when the wordcement came that it wasabnormal in that sentence.

Studies of brain responseshaveshownthatviolationsofnormality are detected withastonishing speed andsubtlety. In a recentexperiment, people heard thesentence “Earth revolvesaround the trouble everyyear.” A distinctive patternwasdetectedinbrainactivity,startingwithintwo-tenthsofasecondoftheonsetoftheoddword.Evenmoreremarkable,the same brain response

occurs at the same speedwhen a male voice says, “Ibelieve I am pregnantbecause I feel sick everymorning,”orwhenanupper-class voice says, “I have alarge tattoo on my back.” Avast amount of worldknowledge must instantly bebrought to bear for theincongruity tobe recognized:the voice must be identifiedas upper-class English andconfronted with the

generalization that largetattoos are uncommon in theupperclass.Weareabletocommunicate

with each other because ourknowledge of the world andour use of words are largelyshared. When I mention atable, without specifyingfurther,youunderstand that Imean a normal table. Youknow with certainty that itssurfaceisapproximatelyleveland that ithas far fewer than

25legs.Wehavenormsforavast number of categories,and these norms provide thebackgroundfortheimmediatedetection of anomalies suchaspregnantmenandtattooedaristocrats.To appreciate the role of

norms in communication,consider the sentence “Thelargemouseclimbedoverthetrunk of the very smallelephant.” I can count onyour having norms for the

size of mice and elephantsthatarenottoofarfrommine.The norms specify a typicalor average size for theseanimals,andtheyalsocontaininformation about the rangeor variability within thecategory. It is very unlikelythateitherofusgottheimageinourmind’seyeofamouselarger than an elephantstriding over an elephantsmaller than a mouse.Instead, we each separately

butjointlyvisualizedamousesmaller than a shoeclambering over an elephantlarger than a sofa. System 1,which understands language,has access to norms ofcategories,which specify therange of plausible values aswellasthemosttypicalcases.

SeeingCausesandIntentions

“Fred’s parents arrived late.

The caterers were expectedsoon. Fred was angry.” Youknow why Fred was angry,and it is not because thecaterers were expected soon.In your network ofassociationsmals in co, angerand lack of punctuality arelinked as an effect and itspossiblecause,butthereisnosuch link between anger andtheideaofexpectingcaterers.A coherent story wasinstantly constructed as you

read; you immediately knewthe cause of Fred’s anger.Finding such causalconnections is part ofunderstanding a story and isan automatic operation ofSystem 1. System 2, yourconscious self, was offeredthe causal interpretation andacceptedit.A story in Nassim Taleb’sThe Black Swan illustratesthis automatic search forcausality. He reports that

bond prices initially rose onthedayofSaddamHussein’scapture inhishidingplace inIraq. Investors wereapparently seeking saferassets that morning, and theBloomberg News serviceflashed this headline: U.S.TREASURIES RISE; HUSSEINCAPTURE MAY NOT CURBTERRORISM. Half an hourlater, bond prices fell backandtherevisedheadlineread:U.S. TREASURIES FALL; HUSSEINCAPTURE BOOSTS ALLURE OF

RISKY ASSETS. Obviously,Hussein’s capture was themajor event of the day, andbecause of the way theautomatic search for causesshapes our thinking, thatevent was destined to be theexplanation of whateverhappened in the market onthat day. The two headlineslook superficially likeexplanations of whathappenedinthemarket,butastatement that can explain

two contradictory outcomesexplains nothing at all. Infact, all the headlines do issatisfy our need forcoherence: a large event issupposed to haveconsequences, andconsequences need causes toexplain them. We havelimited information aboutwhathappenedonaday,andSystem1isadeptatfindingacoherent causal story thatlinks the fragments of

knowledgeatitsdisposal.Readthissentence:

After spending a dayexploring beautifulsights in the crowdedstreets of New York,Jane discovered that herwalletwasmissing.

When people who had readthis brief story (along withmany others) were given asurprise recall test, the word

pickpocketwasmorestronglyassociatedwiththestorythantheword sights, even thoughthe latter was actually in thesentence while the formerwas not. The rules ofassociative coherence tell uswhat happened.The event ofa lost wallet could evokemany different causes: thewalletslippedoutofapocket,wasleftintherestaurant,etc.However, when the ideas oflost wallet, New York, and

crowds are juxtaposed, theyjointly evoke the explanationthat a pickpocket caused theloss. In the story of thestartlingsoup,theoutcome—whether another customerwincing at the taste of thesoup or the first person’sextreme reaction to thewaiter’s touch—brings aboutan associatively coherentinterpretation of the initialsurprise, completing aplausiblestory.

The aristocratic Belgianpsychologist AlbertMichottepublished a book in 1945(translated into English in1963) that overturnedcenturies of thinking aboutcausality, going back at leasttoHume’sexaminationoftheassociation of ideas. Thecommonly accepted wisdomwas that we infer physicalcausality from repeatedobservations of correlationsamong events. We have had

myriad experiences in whichwe sawoneobject inmotiontouching another object,which immediately starts tomove, often (but not always)in the samedirection.This iswhathappenswhenabilliardballhitsanother,anditisalsowhat happens when youknock over a vase bybrushing against it. Michottehad a different idea: heargued thatwe see causality,just as directly as we see

color. Tomake his point, hecreated episodes in nttiowhich a black squaredrawn on paper is seen inmotion; itcomes intocontactwith another square, whichimmediately begins tomove.The observers know thatthere is no real physicalcontact,buttheyneverthelesshave a powerful “illusion ofcausality.” If the secondobjectstartsmovinginstantly,they describe it as having

been “launched” by the first.Experimentshaveshownthatsix-month-old infants see thesequence of events as acause-effect scenario, andthey indicate surprise whenthe sequence is altered. Weareevidentlyreadyfrombirthto have impressions ofcausality, which do notdepend on reasoning aboutpatterns of causation. TheyareproductsofSystem1.In 1944, at about the same

time as Michotte publishedhis demonstrations ofphysical causality, thepsychologists Fritz HeiderandMary-AnnSimmelusedamethod similar toMichotte’stodemonstratetheperceptionof intentionalcausality.Theymadeafilm,whichlastsallofoneminuteandfortyseconds,in which you see a largetriangle,asmall triangle,anda circle moving around ashape that looks like a

schematic view of a housewith an open door. Viewerssee an aggressive largetriangle bullying a smallertriangle,a terrifiedcircle, thecircle and the small trianglejoining forces to defeat thebully;theyalsoobservemuchinteractionaroundadoorandthen an explosive finale.Theperception of intention andemotion is irresistible; onlypeopleafflictedbyautismdonot experience it. All this is

entirely in your mind, ofcourse. Your mind is readyand even eager to identifyagents, assign thempersonality traits and specificintentions, and view theiractions as expressingindividual propensities. Hereagain,theevidenceisthatweare born prepared to makeintentional attributions:infants under one year oldidentify bullies and victims,and expect a pursuer to

followthemostdirectpathinattempting to catch whateveritischasing.The experience of freely

willedactionisquiteseparatefrom physical causality.Although it isyourhand thatpicks up the salt, you do notthinkoftheeventintermsofachainofphysicalcausation.You experience it as causedby a decision that adisembodied you made,because you wanted to add

salt to your food. Manypeople find it natural todescribe their soul as thesource and the cause of theiractions. The psychologistPaul Bloom, writing in TheAtlantic in 2005, presentedtheprovocativeclaimthatourinborn readiness to separatephysical and intentionalcausality explains the nearuniversality of religiousbeliefs.Heobserves that“weperceive theworldofobjects

as essentially separate fromtheworldofminds,makingitpossible for us to envisionsoulless bodies and bodilesssouls.” The two modes ofcausation that we are set toperceive make it natural forus to accept the two centralbeliefs ofmany religions: animmaterial divinity is theultimatecauseofthephysicalworld, and immortal soulstemporarily control ourbodies while we live and

leave thembehindaswedie.In Bloom’s view, the twoconcepts of causality wereshaped separately byevolutionary forces, buildingtheoriginsofreligionintothestructureofSystem1.The prominence of causal

intuitionsisarecurrentthemein this book because peopleare prone to apply causalthinking inappropriately, tosituations that requirestatistical reasoning.

Statistical thinking derivesconclusions about individualcases from properties ofcategories and ensembles.Unfortunately,System1doesnot have the capability forthis mode of reasoning;System 2 can learn to thinkstatistically, but few peoplereceive the necessarytraining.Thepsychologyofcausality

was thebasisofmydecisionto describe psycl c to

thinhological processes bymetaphors of agency, withlittleconcernforconsistency.IsometimesrefertoSystem1asanagentwithcertaintraitsand preferences, andsometimes as an associativemachine that representsreality by a complex patternof links. The system and themachine are fictions; myreason for using them is thatthey fit the way we thinkabout causes. Heider’s

triangles and circles are notreally agents—it is just veryeasy and natural to think ofthem thatway. It is amatterofmentaleconomy.Iassumethat you (like me) find iteasiertothinkaboutthemindif we describe what happensin terms of traits andintentions (the two systems)and sometimes in terms ofmechanical regularities (theassociativemachine).Idonotintend to convince you that

the systems are real, anymore than Heider intendedyou to believe that the largetriangleisreallyabully.

SpeakingofNormsandCauses

“When the secondapplicantalsoturnedoutto be an old friend ofmine, I wasn’t quite assurprised. Very littlerepetitionisneededfora

new experience to feelnormal!”

“When we survey thereaction to theseproducts,let’smakesurewe don’t focusexclusively on theaverage. We shouldconsidertheentirerangeofnormalreactions.”

“She can’t accept that

she was just unlucky;sheneedsacausalstory.Shewillendupthinkingthat someoneintentionally sabotagedherwork.”

P

AMachineforJumpingtoConclusions

The great comedian DannyKaye had a line that hasstayed with me since myadolescence. Speaking of awoman he dislikes, he says,“Her favorite position isbeside herself, and her

favorite sport is jumping toconclusions.” The line cameup, I remember, in the initialconversation with AmosTversky about the rationalityof statistical intuitions, andnowIbelieve itoffersanaptdescription of how System 1functions. Jumping toconclusions is efficient if theconclusions are likely to becorrect and the costs of anoccasional mistakeacceptable, and if the jump

saves much time and effort.Jumping to conclusions isrisky when the situation isunfamiliar, the stakes arehigh, and there is no time tocollect more information.These are the circumstancesin which intuitive errors areprobable, which may beprevented by a deliberateinterventionofSystem2.

NeglectofAmbiguity

andSuppressionofDoubt

Figure6

Whatdo the threeexhibits infigure 6 have in common?The answer is that all areambiguous. You almost

certainly read the display ontheleftasABCandtheoneon the right as121314, butthe middle items in bothdisplays are identical. YoucouldjustaswellhavereadeiomprthecvethemasA13Cor 12B 14, but you did not.Whynot?The same shape isreadasaletterinacontextofletters and as a number in acontext of numbers. Theentirecontexthelpsdeterminethe interpretation of each

element. The shape isambiguous,butyoujumptoaconclusion about its identityand do not become aware ofthe ambiguity that wasresolved.As for Ann, you probably

imagined a woman withmoney on hermind,walkingtowardabuildingwithtellersand secure vaults. But thisplausible interpretation is notthe only possible one; thesentence is ambiguous. If an

earlier sentence had been“They were floating gentlydown the river,” you wouldhave imagined an altogetherdifferent scene. When youhave just been thinking of ariver, the word bank is notassociatedwithmoney.Intheabsence of an explicitcontext,System1generatedalikelycontextonitsown.Weknow that it is System 1because you were not awareof the choice or of the

possibility of anotherinterpretation. Unless youhave been canoeing recently,you probably spend moretime going to banks thanfloating on rivers, and youresolved the ambiguityaccordingly.Whenuncertain,System 1 bets on an answer,and the bets are guided byexperience. The rules of thebetting are intelligent: recenteventsandthecurrentcontexthave the most weight in

determininganinterpretation.When no recent event comesto mind, more distantmemories govern. Amongyour earliest and mostmemorable experiences wassinging your ABCs; you didnotsingyourA13Cs.The most important aspect

of both examples is that adefinitechoicewasmade,butyoudidnotknowit.Onlyoneinterpretation came to mind,andyouwereneverawareof

theambiguity.System1doesnotkeep trackofalternativesthat it rejects, or even of thefact that there werealternatives. Conscious doubtis not in the repertoire ofSystem 1; it requiresmaintaining incompatibleinterpretations inmind at thesame time, which demandsmental effort. Uncertaintyand doubt are the domain ofSystem2.

ABiastoBelieveandConfirm

The psychologist DanielGilbert,widelyknownas theauthor of Stumbling toHappiness, once wrote anessay, titled “How MentalSystems Believe,” in whichhe developed a theory ofbelieving and unbelievingthat he traced to theseventeenth-centuryphilosopher Baruch Spinoza.

Gilbert proposed thatunderstanding a statementmustbeginwithanattempttobelieve it: you must firstknow what the idea wouldmean if it were true. Onlythen can you decide whetheror not to unbelieve it. Theinitialattempttobelieveisanautomatic operation ofSystem1,whichinvolvestheconstruction of the bestpossible interpretation of thesituation. Even a nonsensical

statement,Gilbertargues,willevoke initial belief. Try hisexample: “whitefish eatcandy.” You probably wereaware of vague impressionsof fish and candy as anautomatic process ofassociative memory searchedfor links between the twoideas that would make senseofthenonsense.Gilbert sees unbelieving as

anoperationofSystem2,andhe reported an elegant

experimenttomakehispoint.The participants sawnonsensical assertions, suchas “a dinca is a flame,”followed after a few secondsby a single word, “true” or“false.” They were latertested for their memory ofwhich sentences had beenlabeled “true.” In onecondition of the experimentsubjectswererequiredtoholddigits in memory during thetask. The disruption of

System 2 had a selectiveeffect: itmade it difficult forpeople to “unbelieve” falsesentences. In a later test ofmemory, the depleted parmuumbling toticipants endedup thinking thatmany of thefalsesentencesweretrue.Themoral is significant: whenSystem 2 is otherwiseengaged, we will believealmost anything. System1 isgullibleandbiasedtobelieve,System 2 is in charge of

doubtingandunbelieving,butSystem2 is sometimes busy,and often lazy. Indeed, thereis evidence that people aremore likely to be influencedby empty persuasivemessages, such ascommercials, when they aretiredanddepleted.The operations of

associative memorycontribute to a generalconfirmation bias. Whenasked, “Is Sam friendly?”

different instances of Sam’sbehavior will come to mindthan would if you had beenasked“IsSamunfriendly?”Adeliberate search forconfirming evidence, knownas positive test strategy, isalso how System 2 tests ahypothesis. Contrary to therules of philosophers ofscience, who advise testinghypothesesbytryingtorefutethem, people (and scientists,quiteoften)seekdatathatare

likely to be compatible withthe beliefs they currentlyhold. The confirmatory biasof System1 favors uncriticalacceptanceofsuggestionsandexaggerationofthelikelihoodof extreme and improbableevents.Ifyouareaskedaboutthe probability of a tsunamihitting California within thenext thirty years, the imagesthat come to your mind arelikely to be images oftsunamis, in the manner

Gilbertproposedfornonsensestatementssuchas“whitefisheatcandy.”Youwillbeproneto overestimate theprobabilityofadisaster.

ExaggeratedEmotionalCoherence

(HaloEffect)If you like the president’spolitics,youprobablylikehisvoice and his appearance aswell.Thetendencytolike(or

dislike) everything about aperson—including thingsyouhavenotobserved—isknownas the halo effect. The termhasbeeninuseinpsychologyfor a century, but it has notcome into wide use ineveryday language. This is apity, because the halo effectisagoodnameforacommonbias thatplaysa largerole inshaping our view of peopleandsituations.Itisoneofthewaystherepresentationofthe

worldthatSystem1generatesis simplerandmorecoherentthantherealthing.You meet a woman named

Joan at a party and find herpersonable and easy to talkto. Now her name comes upas someone who could beasked to contribute to acharity. What do you knowabout Joan’s generosity?Thecorrect answer is that youknow virtually nothing,because there is little reason

tobelievethatpeoplewhoareagreeable in social situationsarealsogenerouscontributorstocharities.ButyoulikeJoanand you will retrieve thefeeling of liking her whenyou think of her. You alsolike generosity and generouspeople. By association, youare now predisposed tobelievethatJoanisgenerous.Andnowthatyoubelievesheisgenerous,youprobablylikeJoanevenbetterthanyoudid

earlier, because you haveadded generosity to herpleasantattributes.Real evidenceofgenerosity

is missing in the story ofJoan,and thegap is filledbya guess that fits one’semotional response toher. Inother situations, evidenceaccumulates gradually andtheinterpretationisshapedbythe emotion attached to thefirst impression. In anenduring classic of

psychology, Solomon Aschpresenteddescriptionsof twopeople and asked forcomments on theirpersonality. What do youthinkofAlanandBen?

Alan: intelligent—industrious—impulsive—critical—stubborn—enviousBen: envious—The#82stubborn—critical—impulsive—

industrious—intelligentIfyouarelikemostofus,youviewed Alan much morefavorably than Ben. Theinitial traits inthelistchangetheverymeaningofthetraitsthat appear later. Thestubbornnessofan intelligentperson is seenas likely tobejustified and may actuallyevoke respect, butintelligenceinanenviousandstubborn person makes him

more dangerous. The haloeffect is also an example ofsuppressed ambiguity: likethe word bank, the adjectivestubborn is ambiguous andwill be interpreted in a waythat makes it coherent withthecontext.There have been many

variations on this researchtheme. Participants in onestudyfirstconsideredthefirstthree adjectives that describeAlan; then they considered

the last three, whichbelonged, they were told, toanother person. When theyhad imagined the twoindividuals, the participantswereaskedifitwasplausiblefor all six adjectives todescribethesameperson,andmost of them thought it wasimpossible!The sequence in which we

observe characteristics of apersonisoftendeterminedbychance. Sequence matters,

however, because the haloeffect increasestheweightoffirst impressions, sometimesto the point that subsequentinformationismostlywasted.Early in my career as aprofessor, I graded students’essay exams in theconventional way. I wouldpick up one test booklet at atime and read all thatstudent’sessaysinimmediatesuccession,gradingthemasIwent. I would then compute

thetotalandgoontothenextstudent. I eventually noticedthat my evaluations of theessays in each booklet werestrikingly homogeneous. Ibegan to suspect that mygrading exhibited a haloeffect, and that the firstquestion I scored had adisproportionateeffecton theoverall grade. Themechanism was simple: if Ihadgivenahighscore to thefirstessay,Igavethestudent

the benefit of the doubtwhenever I encountered avague or ambiguousstatement later on. Thisseemed reasonable. Surely astudentwhohaddonesowellon the first essay would notmakeafoolishmistakeinthesecond one! But there was aseriousproblemwithmywayof doing things. If a studenthad written two essays, onestrongandoneweak,Iwouldend up with different final

grades depending on whichessay I read first. I had toldthe students that the twoessays had equal weight, butthatwasnottrue:thefirstonehadamuchgreaterimpactonthe final grade than thesecond. This wasunacceptable.I adopted a new procedure.

Instead of reading thebooklets in sequence, I readand scored all the students’answers to the first question,

thenwentontothenextone.I made sure to write all thescores on the inside backpage of the booklet so that Iwould not be biased (evenunconsciously) when I readthe second essay. Soon afterswitchingtothenewmethod,I made a disconcertingobservation: my confidenceinmygradingwasnowmuchlower than it had been. Thereason was that I frequentlyexperiencedadiscomfortthat

was new tome.When Iwasdisappointedwith a student’ssecondessayandwent to theback page of the booklet toenter a poor grade, IoccasionallydiscoveredthatIhad given a top grade to thesame student’s first essay. Ialso noticed that I wastempted to reduce thediscrepancy by changing thegrade that I had not yetwritten down, and found ithardtofollowthesimplerule

of never yielding to thattemptation.Mygradesfortheessays of a single studentoften varied over aconsiderable range. The lackof coherence left meuncertainandfrustrated.I was now less happy with

and less confident in mygradesthanIhadbeenearlier,but I recognized that thassconfthiswas a good sign, anindication that the newprocedure was superior. The

consistency I had enjoyedearlier was spurious; itproduced a feeling ofcognitive ease, and mySystem2washappytolazilyaccept the final grade. Byallowing myself to bestrongly influenced by thefirst question in evaluatingsubsequent ones, I sparedmyself the dissonance offinding the same studentdoing very well on somequestions and badly on

others. The uncomfortableinconsistency that wasrevealed when I switched tothenewprocedurewasreal:itreflectedboth the inadequacyof any single question as ameasure of what the studentknew and the unreliability ofmyowngrading.The procedure I adopted to

tamethehaloeffectconformsto a general principle:decorrelate error! Tounderstandhowthisprinciple

works, imagine that a largenumber of observers areshown glass jars containingpenniesandarechallengedtoestimate the number ofpenniesineachjar.AsJamesSurowiecki explained in hisbest-selling The Wisdom ofCrowds, this is the kind oftask in which individuals dovery poorly, but pools ofindividual judgments doremarkably well. Someindividuals greatly

overestimatethetruenumber,others underestimate it, butwhen many judgments areaveraged,theaveragetendstobe quite accurate. Themechanism isstraightforward: allindividuals look at the samejar, and all their judgmentshaveacommonbasis.Ontheother hand, the errors thatindividuals make areindependent of the errorsmade by others, and (in the

absenceofa systematicbias)they tend to average to zero.However, the magic of errorreduction works well onlywhen the observations areindependent and their errorsuncorrelated. If theobserversshare a bias, the aggregationof judgmentswill not reduceit. Allowing the observers toinfluence each othereffectivelyreducesthesizeofthe sample, and with it theprecision of the group

estimate.To derive the most useful

information from multiplesources of evidence, youshould always try to makethese sources independent ofeachother.Thisruleispartofgoodpoliceprocedure.Whenthere are multiple witnessesto an event, they are notallowed to discuss it beforegiving their testimony. Thegoal is not only to preventcollusion by hostile

witnesses,itisalsotopreventunbiased witnesses frominfluencing each other.Witnesses who exchangetheir experienceswill tend tomake similar errors in theirtestimony, reducing the totalvalueof the information theyprovide. Eliminatingredundancy from yoursources of information isalwaysagoodidea.The principle of

independent judgments (and

decorrelated errors) hasimmediate applications forthe conduct of meetings, anactivity in which executivesinorganizationsspendagreatdealoftheirworkingdays.Asimple rule can help: beforean issue is discussed, allmembers of the committeeshould be asked to write avery brief summary of theirposition. This proceduremakes good use of the valueofthediversityofknowledge

andopinioninthegroup.Thestandard practice of opendiscussion gives too muchweight to the opinions ofthose who speak early andassertively, causing others tolineupbehindthem.

WhatYouSeeisAllThereis(Wysiati)

Oneofmyfavoritememoriesof theearlyyearsofworkingwith Amos is a comedy

routine he enjoyedperforming. In a perfectimpersonation of one of theprofessorswithwhomhehadstudied philosophy as anundergraduate, Amos wouldgrowlinHebrewmarkedbyathick German accent: “Youmust never forget thePrimatof the Is.” What exactly histeacher had meant by thatphraseneverbecameclear tome (or to Amos, I believe),but Amos’s jokes always

maht=cipde a point. He wasreminded of the old phrase(and eventually I was too)wheneverweencounteredtheremarkable asymmetrybetween the ways our mindtreats information that iscurrently available andinformationwedonothave.An essential design feature

of the associativemachine isthat it represents onlyactivated ideas. Informationthat is not retrieved (even

unconsciously) frommemorymight as well not exist.System 1 excels atconstructing thebestpossiblestory that incorporates ideascurrently activated, but itdoes not (cannot) allow forinformationitdoesnothave.Themeasure of success for

System1 is the coherenceofthestoryitmanagestocreate.Theamountandqualityofthedata on which the story isbased are largely irrelevant.

When information is scarce,which is a commonoccurrence, System 1operates as a machine forjumping to conclusions.Considerthefollowing:“WillMindikbeagoodleader?Sheis intelligent and strong…”An answer quickly came toyour mind, and it was yes.You picked the best answerbased on the very limitedinformation available, butyou jumped thegun.What if

the next two adjectives werecorruptandcruel?Take note of what you didnotdoasyoubriefly thoughtof Mindik as a leader. Youdidnotstartbyasking,“Whatwould Ineed toknowbeforeIformedanopinionaboutthequality of someone’sleadership?” System 1 got toworkonitsownfromthefirstadjective: intelligent is good,intelligent and strong is verygood. This is the best story

that can be constructed fromtwoadjectives, andSystem1delivered it with greatcognitiveease.Thestorywillberevisedifnewinformationcomes in (such asMindik iscorrupt), but there is nowaiting and no subjectivediscomfort. And there alsoremains a bias favoring thefirstimpression.The combination of a

coherence-seeking System 1witha lazySystem2 implies

that System 2 will endorsemany intuitive beliefs,whichclosely reflect theimpressions generated bySystem1.Of course, System2 also is capable of a moresystematic and carefulapproach to evidence, and offollowinga list ofboxes thatmust be checked beforemaking a decision—think ofbuying a home, when youdeliberately seek informationthat you don’t have.

However, System 1 isexpected to influence eventhemorecarefuldecisions.Itsinputneverceases.Jumping to conclusions on

the basis of limited evidenceis so important to anunderstanding of intuitivethinking, and comes up sooften in thisbook, that Iwilluse a cumbersomeabbreviation for it:WYSIATI, which stands forwhat you see is all there is.

System 1 is radicallyinsensitivetoboththequalityand the quantity of theinformation that gives rise toimpressionsandintuitions.Amos, with two of his

graduatestudentsatStanford,reported a study that bearsdirectly on WYSIATI, byobserving the reaction ofpeople who are given one-sided evidence and know it.The participants wereexposed to legal scenarios

suchasthefollowing:

On September 3,plaintiff DavidThornton, a forty-three-year-old union fieldrepresentative, waspresent in Thrifty DrugStore#168,performingaroutine union visit.Within ten minutes ofhis arrival, a storemanager confrontedhimandtoldhimhecouldno

longer speak with theunion employees on thefloor of the store.Instead, he would haveto see them in a backroomwhiletheywereonbreak. Such a request isallowed by the unioncontract with ThriftyDrug but had neverbefore been enforced.When Mr. Thorntonobjected, he was toldthathehadthechoiceof

contoroomwhilformingto these requirements,leaving the store, orbeing arrested. At thispoint, Mr. Thorntonindicatedtothemanagerthat he had always beenallowed to speak toemployees on the floorfor as much as tenminutes, as long as nobusiness was disrupted,andthathewouldratherbe arrested than change

the procedure of hisroutine visit. Themanager then called thepolice and had Mr.Thornton handcuffed inthestore for trespassing.Afterhewasbookedandput into a holding cellfor a brief time, allcharges were dropped.Mr. Thornton is suingThrifty Drug for falsearrest.

Inadditiontothisbackgroundmaterial, which allparticipants read, differentgroups were exposed topresentations by the lawyersforthetwoparties.Naturally,the lawyer for the unionorganizerdescribed thearrestas an intimidation attempt,whilethelawyerforthestorearguedthathavingthetalkinthe store was disruptive andthat the manager was actingproperly. Some participants,

like a jury, heard both sides.The lawyers added no usefulinformation that you couldnotinferfromthebackgroundstory.The participants were fully

awareof thesetup,andthosewho heard only one sidecould easily have generatedthe argument for the otherside. Nevertheless, thepresentation of one-sidedevidence had a verypronounced effect on

judgments. Furthermore,participants who saw one-sided evidence were moreconfident of their judgmentsthan those who saw bothsides. This is just what youwould expect if theconfidence that peopleexperience is determined bythe coherence of the storythey manage to constructfromavailableinformation.Itis the consistency of theinformationthatmattersfora

good story, not itscompleteness. Indeed, youwill often find that knowinglittle makes it easier to fiteverything you know into acoherentpattern.WY SIATI facilitates the

achievement of coherenceandofthecognitiveeasethatcauses us to accept astatement as true. It explainswhy we can think fast, andhow we are able to makesenseofpartialinformationin

a complex world. Much ofthe time, the coherent storywe put together is closeenough to reality to supportreasonableaction.However,Iwill also invoke WY SIATIto help explain a long anddiverse list of biases ofjudgment and choice,including the followingamongmanyothers:

Overconfidence: As the

WYSIATI rule implies,neither the quantity northe quality of theevidence counts formuch in subjectiveconfidence. Theconfidence thatindividuals have in theirbeliefs depends mostlyon the quality of thestory theycan tell aboutwhat they see, even ifthey see little.We oftenfail to allow for the

possibility that evidencethatshouldbecritical toour judgment ismissing—what we see is allthere is. Furthermore,our associative systemtends to settle on acoherent pattern ofactivation andsuppresses doubt andambiguity.Framing effects:Different ways ofpresenting the same

information often evokedifferent emotions. Thestatement that “the oddsof survival one monthaftersurgeryare90%”ismorereassuringthantheequivalentstatementthat“mortality within onemonth of surgery is10%.” Similarly, coldcuts described as “90%fat-free” are moreattractivethanwhentheyare described as “10%

fat.”The equivalence ofthe alternativeformulations istransparent, but anindividual normally seesonly one formulation,andwhat she sees is allthereis.Base-rateneglect:RecallSteve,themeekandtidysoul who is oftenbelieved to be alibrarian. Thepersonalitydescriptionis

salient and vivid, andalthough you surelyknowthattherearemoremalefarmmuBase-rers than malelibrarians, that statisticalfact almost certainly didnot come to your mindwhen you firstconsidered the question.What you saw was alltherewas.

SpeakingofJumpingtoConclusions

“She knows nothingabout this person’smanagement skills. Allshe is going by is thehalo effect from a goodpresentation.”

“Let’s decorrelate errorsby obtaining separatejudgments on the issue

before any discussion.We will get moreinformation fromindependentassessments.”

“They made that bigdecisiononthebasisofagood report from oneconsultant. WYSIATI—whatyouseeisall thereis.Theydidnotseemtorealize how little

informationtheyhad.”

“They didn’twantmoreinformation that mightspoil their story.WYSIATI.”

P

HowJudgmentsHappen

There is no limit to thenumberof questionsyou cananswer, whether they arequestions someone else asksorquestionsyouaskyourself.Nor is there a limit to thenumber of attributes you canevaluate. You are capable ofcounting the number of

capital letters on this page,comparing the height of thewindowsofyourhousetotheone across the street, andassessing the politicalprospectsofyoursenatoronascale from excellent todisastrous. The questions areaddressedtoSystem2,whichwill direct attention andsearch memory to find theanswers. System 2 receivesquestions or generates them:in either case it directs

attention and searchesmemory to find the answers.System1operatesdifferently.Itcontinuouslymonitorswhatisgoingonoutsideandinsidethe mind, and continuouslygenerates assessments ofvarious aspects of thesituation without specificintentionandwithlittleornoeffort. These basicassessments play animportant role in intuitivejudgment, because they are

easily substituted for moredifficultquestions—thisistheessentialideaoftheheuristicsand biases approach. Twoother features of System 1also support the substitutionof one judgment for another.One is theability to translatevalues across dimensions,whichyoudo inansweringaquestion that most peoplefind easy: “If Sam were astall as he is intelligent, howtall would he be?” Finally,

there is the mental shotgun.An intention of System 2 toanswera specificquestionorevaluate a particular attributeof the situation automaticallytriggers other computations,includingbasicassessments.

BasicAssessmentsSystem1hasbeenshapedbyevolution to provide acontinuous assessment of themain problems that an

organism must solve tosurvive: How are thingsgoing? Is there a threat or amajor opportunity? Iseverything normal? Should Iapproach or avoid? Thequestions are perhaps lessurgent for a human in a cityenvironment than for agazelle on the savannah,aalenc and e: How , but wehave inherited the neuralmechanisms that evolved toprovide ongoing assessments

ofthreat level,andtheyhavenot been turned off.Situations are constantlyevaluated as good or bad,requiringescapeorpermittingapproach. Good mood andcognitiveeaseare thehumanequivalentsofassessmentsofsafetyandfamiliarity.For a specific exampleof a

basic assessment, considerthe ability to discriminatefriend from foe at a glance.This contributes to one’s

chances of survival in adangerous world, and such aspecialized capability hasindeed evolved. AlexTodorov, my colleague atPrinceton, has explored thebiological roots of the rapidjudgmentsofhowsafeitistointeract with a stranger. Heshowed thatwe are endowedwithanabilitytoevaluate,inasingleglanceatastranger’sface, two potentially crucialfacts about that person: how

dominant (and thereforepotentially threatening)he is,and how trustworthy he is,whether his intentions aremore likely to be friendly orhostile.Theshapeofthefaceprovides the cues forassessing dominance: a“strong” square chin is onesuch cue. Facial expression(smileorfrown)providesthecues for assessing thestranger’s intentions. Thecombinationofa squarechin

with a turned-down mouthmay spell trouble. Theaccuracyoffacereadingisfarfromperfect: roundchinsarenot a reliable indicator ofmeekness, and smilescan (tosome extent) be faked. Still,even an imperfect ability toassess strangers confers asurvivaladvantage.This ancient mechanism is

put to a novel use in themodern world: it has someinfluenceonhowpeoplevote.

Todorov showed his studentspictures of men’s faces,sometimesforaslittleasone-tenth of a second, and askedthem to rate the faces onvarious attributes, includinglikability and competence.Observers agreed quite wellon those ratings. The facesthat Todorov showed werenot a random set: they werethe campaign portraits ofpoliticians competing forelective office. Todorov then

compared the results of theelectoral races to the ratingsof competence that Princetonstudents hadmade, based onbriefexposuretophotographsand without any politicalcontext. In about 70%of theraces for senator,congressman, and governor,the election winner was thecandidate whose face hadearned a higher rating ofcompetence. This strikingresultwas quickly confirmed

in national elections inFinland, in zoning boardelections in England, and invarious electoral contests inAustralia, Germany, andMexico.Surprisingly(atleasttome),ratingsofcompetencewere far more predictive ofvotingoutcomesinTodorov’sstudy than ratings oflikability.Todorov has found that

people judge competence bycombining the two

dimensions of strength andtrustworthiness. The facesthat exude competencecombineastrongchinwithaslight confident-appearingsmile. There is no evidencethat these facial featuresactually predict how wellpoliticians will perform inoffice. But studies of thebrain’s response to winningand losing candidates showthat we are biologicallypredisposed to reject

candidates who lack theattributes we value—in thisresearch, losers evokedstronger indications of(negative) emotionalresponse. This is an exampleofwhatIwillcallajudgmentheuristic in the followingchapters. Voters areattempting to form animpression of how good acandidate will be in office,and they fall back on asimpler assessment that is

made quickly andautomaticallyandisavailablewhenSystem2mustmakeitsdecision.Political scientists followed

up on Todorov’s initialresearch by identifying acategory of voters for whomthe automatic preferences ofSystem 1 are particularlylikely to play a large role.They found what they werelooking for among politicalrm="5%">Todoly uninformed

voterswhowatchagreatdealof television. As expected,the effect of facialcompetence on voting isabout three times larger forinformation-poor and TV-prone voters than for otherswho are better informed andwatch less television.Evidently, the relativeimportance of System 1 indeterminingvotingchoices isnot the same for all people.We will encounter other

examples of such individualdifferences.System 1 understands

language, of course, andunderstandingdependsonthebasic assessments that areroutinely carried out as partof the perception of eventsand the comprehension ofmessages. These assessmentsinclude computations ofsimilarity andrepresentativeness,attributions of causality, and

evaluationsoftheavailabilityof associations andexemplars. They areperformed even in theabsenceofaspecifictaskset,although the results are usedtomeettaskdemandsastheyarise.Thelistofbasicassessments

islong,butnoteverypossibleattribute is assessed. For anexample, look briefly atfigure7. A glance provides an

immediate impression ofmany features of thedisplay.Youknowthatthetwotowersare equally tall and that theyaremoresimilartoeachotherthanthetowerontheleftistothe array of blocks in themiddle.However,youdonotimmediately know that thenumber of blocks in the left-handtoweristhesameasthenumber of blocks arrayed onthe floor, and you have noimpression of the height of

thetowerthatyoucouldbuildfrom them. To confirm thatthe numbers are the same,youwould need to count thetwo sets of blocks andcompare the results, anactivity that only System 2cancarryout.

Figure7

SetsandPrototypes

For another example,consider the question: Whatis the average length of thelinesinfigure8?

Figure8

This question is easy and

System 1 answers it withoutprompting.Experimentshaveshown that a fraction of asecondissufficientforpeopleto register theaverage lengthof an array of lines withconsiderable precision.Furthermore, the accuracy ofthese judgments is notimpairedwhentheobserveriscognitively busy with amemory task. They do notnecessarily know how todescribetheaverageininches

or centimeters, but they willbe very accurate in adjustingthe length of another line tomatch the average. System 2is not needed to form animpression of the norm oflengthforanarray.System1does it, automatically andeffortlessly,justasitregistersthecolorof the lines and thefactthattheyarenotparallel.We also can form animmediate impression of thenumberofobjectsinanarray

—precisely if there are fouror fewer objects, crudely iftherearemore.Now to another question:

Whatisthetotallengthofthelines in figure 8? This is adifferent experience, becauseSystem 1 has no suggestionsto offer. The only way youcananswerthisquestionisbyactivating System 2, whichwill laboriously estimate theaverage,estimateorcountthelines, and multiply average

lengthbythenumberoflines.estimaight="0%">The failure of System 1 to

compute the total lengthof aset of lines at a glance maylook obvious to you; youneverthoughtyoucoulddoit.It is in fact an instanceof animportant limitation of thatsystem. Because System 1represents categories by aprototype or a set of typicalexemplars, it dealswellwithaverages but poorly with

sums. The size of thecategory, the number ofinstances itcontains, tends tobe ignored in judgments ofwhat I will call sum-likevariables.Participants in one of the

numerous experiments thatwere prompted by thelitigation following thedisastrous Exxon Valdez oilspill were asked theirwillingness topayfornets tocover oil ponds in which

migratory birds often drown.Different groups ofparticipants stated theirwillingness to pay to save2,000, 20,000, or 200,000birds. If saving birds is aneconomicgooditshouldbeasum-like variable: saving200,000 birds should beworthmuchmorethansaving2,000 birds. In fact, theaverage contributions of thethree groups were $80, $78,and $88 respectively. The

number of birds made verylittle difference. What theparticipants reacted to, in allthreegroups,wasaprototype—the awful image of ahelpless bird drowning, itsfeathers soaked in thick oil.The almost complete neglectofquantityinsuchemotionalcontexts has been confirmedmanytimes.

IntensityMatching

Questions about yourhappiness, the president’spopularity, the properpunishment of financialevildoers, and the futureprospectsofapoliticiansharean important characteristic:theyallrefertoanunderlyingdimension of intensity oramount, which permits theuse of the wordmore: morehappy, more popular, moresevere,ormorepowerful(fora politician). For example, a

candidate’s political futurecan range from the low of“She will be defeated in theprimary” to a high of “Shewill someday be president oftheUnitedStates.”Here we encounter a new

aptitude of System 1. Anunderlying scale of intensityallows matching acrossdiversedimensions. Ifcrimeswerecolors,murderwouldbea deeper shade of red thantheft. If crimes were

expressed as music, massmurder would be playedfortissimo whileaccumulating unpaid parkingtickets would be a faintpianissimo. And of courseyou have similar feelingsabout the intensity ofpunishments. In classicexperiments, people adjustedtheloudnessofasoundtotheseverity of crimes; otherpeople adjusted loudness tothe severity of legal

punishments. If you heardtwo notes, one for the crimeand one for the punishment,you would feel a sense ofinjustice if one tone wasmuchlouderthantheother.Consider an example that

wewillencounteragainlater:

Julie read fluentlywhenshewasfouryearsold.

Now match Julie’s readingprowess as a child to the

followingintensityscales:

Howtallisamanwhoisas tall as Julie wasprecocious?

Whatdoyou thinkof6 feet?Obviously too little. Whatabout 7 feet? Probably toomuch.You are looking for aheightthatisasremarkableastheachievementofreadingatage four. Fairly remarkable,but not extraordinary.

Reading at fifteen monthswould be extraordinary,perhaps like a man who is7'8".

What levelof incomeinyour professionmatchesJulie’s readingachievement?WhichcrimeisassevereasJuliewasprecocious?Which graduating GPAinanIvyLeaguecollegematchesJulie’sreading?

Not very hard, was it?Furthermore, you can beassured that your matcheswillbequiteclosetothoseofother people in your culturalmilieu.Wewillseethatwhenpeople are asked to predictJulie’s GPA from theinformation about the age atwhich she learned to read,they answer by translatingfromonescaletoanotherandpick thematchingGPA.Andwe will also see why this

mode of prediction bymatching is statisticallywrong—although it isperfectlynaturaltoSystem1,and for most people exceptstatisticians it is alsoacceptabletoSystem2.

TheMentalShotgunSystem 1 carries out manycomputationsatanyonetime.Some of these are routineassessments that go on

continuously.Wheneveryoureyes are open, your braincomputesathree-dimensionalrepresentation of what is inyourfieldofvision,completewith the shape of objects,their position in space, andtheir identity.No intention isneeded to trigger thisoperation or the continuousmonitoring for violatedexpectations. In contrast tothese routine assessments,other computations are

undertaken only whenneeded:youdonotmaintainacontinuousevaluationofhowhappy or wealthy you are,andevenifyouareapoliticaladdict you do notcontinuously assess thepresident’s prospects. Theoccasional judgments arevoluntary. They occur onlywhen you intend them to doso.You do not automatically

countthenumberofsyllables

of every word you read, butyou can do it if you sochoose.However, the controloverintendedcomputationsisfar from precise: we oftencomputemuchmore thanwewant or need. I call thisexcess computation themental shotgun. It isimpossible to aim at a singlepointwitha shotgunbecauseit shoots pellets that scatter,and it seems almost equallydifficult for System 1 not to

do more than System 2charges it to do. Twoexperiments that I read longagosuggestedthisimage.Participants in one

experiment listened to pairsofwords,withtheinstructionto press a key as quickly aspossible whenever theydetected that the wordsrhymed.Thewords rhyme inboththesepairs:

VOTE—NOTE

VOTE—GOAT

The difference is obvious toyoubecause you see the twopairs. VOTE and GOATrhyme, but they are spelleddifferently. The participantsonly heard the words, buttheywere also influenced bythe spelling. They weredistinctlyslower to recognizethewordsas rhyming if theirspelling was discrepant.Although the instructions

requiredonlyacomparisonofsounds, the participants alsocompared their spelling, andthe mismatch on theirrelevant dimension slowedthem down. An intention toanswer one question evokedanother, which was not onlysuperfluous but actuallydetrimentaltothemaintask.In another study, people

listened to a series ofsentences, with theinstructiontopressonekeyas

quickly as post="lly desibletoindicateifthesentencewasliterallytrue,andanotherkeyif the sentence was notliterally true. What are thecorrect responses for thefollowingsentences?

Someroadsaresnakes.Somejobsaresnakes.Somejobsarejails.

All three sentences areliterally false. However, you

probably noticed that thesecond sentence is moreobviouslyfalsethantheothertwo—the reaction timescollected in the experimentconfirmed a substantialdifference.Thereasonforthedifference is that the twodifficult sentences can bemetaphorically true. Hereagain, the intention toperform one computationevoked another. And hereagain, the correct answer

prevailed in the conflict, buttheconflictwiththeirrelevantanswer disruptedperformance. In the nextchapter we will see that thecombination of a mentalshotgun with intensitymatching explains why wehave intuitive judgmentsabout many things that weknowlittleabout.

Speakingof

Judgment

“Evaluating people asattractive or not is abasic assessment. Youdo that automaticallywhetherornotyouwantto, and it influencesyou.”

“Therearecircuitsinthebrain that evaluatedominance from the

shape of the face. Helooks the part for aleadershiprole.”

“The punishment won’tfeel just unless itsintensity matches thecrime. Just like you canmatch the loudness of asound to the brightnessofalight.”

“This was a clear

instance of a mentalshotgun. He was askedwhether he thought thecompanywasfinanciallysound, but he couldn’tforget that he likes theirproduct.”

P

AnsweringanEasierQuestion

A remarkable aspect of yourmental life is that you arerarely stumped. True, youoccasionally face a questionsuchas17×24=?towhichno answer comesimmediately to mind, butthese dumbfoundedmomentsare rare. The normal state of

your mind is that you haveintuitive feelings andopinions about almosteverything that comes yourway. You like or dislikepeoplelongbeforeyouknowmuchaboutthem;youtrustordistrust strangers withoutknowing why; you feel thatan enterprise is bound tosucceedwithout analyzing it.Whether you state them ornot, you often have answersto questions that you do not

completely understand,relying on evidence that youcan neither explain nordefend.

SubstitutingQuestions

Iproposeasimpleaccountofhow we generate intuitiveopinionsoncomplexmatters.If a satisfactory answer to ahard question isebr ques Dnot found quickly, System 1

will find a related questionthat iseasierandwillanswerit. I call the operation ofanswering one question inplace of another substitution.I also adopt the followingterms:

The target question is theassessment you intend toproduce.Theheuristicquestionisthe

simpler question that you

answerinstead.

The technical definition ofheuristic is a simpleprocedure that helps findadequate, though oftenimperfect,answerstodifficultquestions. The word comesfromthesamerootaseureka.The idea of substitution

came up early in my workwith Amos, and it was thecore of what became the

heuristics and biasesapproach. We askedourselveshowpeoplemanageto make judgments ofprobability without knowingpreciselywhat probability is.We concluded that peoplemust somehow simplify thatimpossible task, and we setout to find how they do it.Our answer was that whencalled upon to judgeprobability, people actuallyjudge something else and

believe they have judgedprobability. System 1 oftenmakes thismovewhen facedwithdifficulttargetquestions,if theanswer toarelatedandeasier heuristic questioncomesreadilytomind.Substituting one question

for another can be a goodstrategy for solving difficultproblems, and George Pólyaincluded substitution in hisclassic How to Solve It: “Ifyou can’t solve a problem,

then there is an easierproblem you can solve: findit.” Pólya’s heuristics arestrategic procedures that aredeliberately implemented bySystem 2. But the heuristicsthat I discuss in this chapterare not chosen; they are aconsequence of the mentalshotgun, the imprecisecontrol we have overtargeting our responses toquestions.Consider the questions

listedintheleft-handcolumnoftable1.Thesearedifficultquestions,andbeforeyoucanproduceareasonedanswertoany of them you must dealwith other difficult issues.What is the meaning ofhappiness? What are thelikely political developmentsinthenextsixmonths?Whatarethestandardsentencesforother financial crimes? Howstrong is thecompetition thatthe candidate faces? What

other environmental or othercauses shouldbeconsidered?Dealing with these questionsseriously is completelyimpractical. But you are notlimited to perfectly reasonedanswerstoquestions.Thereisa heuristic alternative tocareful reasoning, whichsometimes works fairly welland sometimes leads toseriouserrors.

TargetQuestion

HeuristicQuestion

Howmuchwouldyoucontributetosaveanendangeredspecies?

HowmuchemotiondoIfeelwhenIthinkofdyingdolphins?

How

happyareyou Whatismymoodright

withyourlifethesedays?

now?

Howpopularisthepresidentrightnow?

Howpopularwillthepresidentbesixmonthsfromnow?

Howshouldfinancialadviserswhopreyonthe

HowmuchangerdoIfeelwhenIthinkof

elderlybepunished?

financialpredators?

Thiswomanisrunningfortheprimary.Howfarwillshegoinpolitics?

Doesthiswomanlooklikeapoliticalwinner?

Table1 The mental shotgun makes

it easy to generate quickanswers todifficultquestions

without imposing much hardworkonyour lazySystem2.Theright-handcounterpartofeach of the left-handquestions is very likely tobeevoked and very easilyanswered. Your feelingsabout dolphins and financialcrooks, your current mood,your impressions of thepolitical skill of the primarycandidate, or the currentstandingof thepresidentwillreadily come to mind. The

heuristicquestionsprovideanoff-the-shelf answer to eachof the difficult targetquestions.Something is still missing

from this story: the answersneed to be fitted to theoriginal questions. Forexample, my feelings aboutdying dolphins must beexpressed indollars.Anothercapability of System 1,intensity matching, isavailable to solve that

problem. Recall that bothfeelings and contributiondollars are intensity scales. Icanfeelmoreorlessstronglyaboutdolphins and there is acontribution thatmatches theintensity ofmy feelings. Thedollar amount thatwill cometo my mind is the matchingamount. Similar intensitymatches are possible for allthe questions. For example,the political skills of acandidate can range from

pathetic to extraordinarilyimpressive, and the scale ofpolitical success can rangefrom the lowof“Shewillbedefeated in theprimary” to ahighof“Shewillsomedaybepresident of the UnitedStates.”The automatic processes of

the mental shotgun andintensity matching oftenmake available one or moreanswerstoeasyquestionsthatcould be mapped onto the

target question. On someoccasions, substitution willoccur and a heuristic answerwillbeendorsedbySystem2.Of course, System 2 has theopportunity to reject thisintuitiveanswer,ortomodifyit by incorporating otherinformation.However, a lazySystem 2 often follows thepath of least effort andendorses a heuristic answerwithout much scrutiny ofwhether it is truly

appropriate. You will not bestumped,youwillnothavetoworkveryherрwheard, andyoumaynotevennotice thatyou did not answer thequestion you were asked.Furthermore, you may notrealizethatthetargetquestionwas difficult, because anintuitive answer to it camereadilytomind.

The3-DHeuristic

Havea lookat thepictureofthethreemenandanswerthequestionthatfollows.

Figure9

As printed on the page,is thefigureontherightlarger than thefigureontheleft?

The obvious answer comes

quicklytomind:thefigureontherightislarger.Ifyoutakea ruler to the two figures,however, you will discoverthat in fact the figures are

exactly the same size. Yourimpression of their relativesize is dominated by apowerful illusion, whichneatly illustrates the processofsubstitution.The corridor in which the

figures are seen is drawn inperspectiveandappearstogointo the depth plane. Yourperceptual systemautomatically interprets thepictureasathree-dimensionalscene,notasanimageprinted

onaflatpapersurface.Inthe3-Dinterpretation,thepersonon the right is both muchfartherawayandmuchlargerthan the person on the left.For most of us, thisimpression of 3-D size isoverwhelming. Only visualartists and experiencedphotographers havedeveloped the skill of seeingthe drawing as an object onthe page. For the rest of us,substitution occurs: the

dominant impression of 3-Dsize dictates the judgment of2-D size. The illusion is duetoa3-Dheuristic.Whathappenshereisatrue

illusion, not amisunderstanding of thequestion. You knew that thequestionwasaboutthesizeofthe figures in the picture, asprinted on the page. If youhad been asked to estimatethe size of the figures, weknow from experiments that

youranswerwouldhavebeenin inches,not feet.Youwerenot confused about thequestion, but you wereinfluencedbytheanswertoaquestion that you were notasked:“Howtallarethethreepeople?”The essential step in the

heuristic—the substitution ofthree-dimensional for two-dimensional size—occurredautomatically. The picturecontains cues that suggest a

3-D interpretation. Thesecuesareirrelevanttothetaskathand—thejudgmentofsizeof the figure on the page—andyou shouldhave ignoredthem,butyoucouldnot.Thebias associated with theheuristic is that objects thatappeartobemoredistantalsoappear to be larger on thepage. As this exampleillustrates, a judgment that isbased on substitution willinevitably be biased in

predictableways.Inthiscase,it happens so deep in theperceptual system that yousimplycannothelpit.

TheMoodHeuristicforHappiness

AsurveyofGermanstudentsisoneofthebestexamplesofsubstitution. The survey thatthe young participantscompleted included thefollowingtwoquestions:

How happy are youthesedays?Howmanydatesdidyouhavelastmonth?

< stрr to a p height="0%"width="0%">Theexperimenterswereinterestedinthecorrelationbetweenthetwo answers. Would thestudents who reported manydates say that they werehappierthanthosewithfewerdates? Surprisingly, no: the

correlation between theanswers was about zero.Evidently, dating was notwhat came first to thestudents’ minds when theywere asked to assess theirhappiness. Another group ofstudents saw the same twoquestions, but in reverseorder:

Howmanydatesdidyouhavelastmonth?How happy are you

thesedays?The results this time werecompletely different. In thissequence, the correlationbetween the number of datesand reported happiness wasabout as high as correlationsbetween psychologicalmeasures can get. Whathappened?The explanation is

straightforward, and it is agoodexampleofsubstitution.

Datingwasapparentlynotthecenter of these students’ life(inthefirstsurvey,happinessand dating wereuncorrelated), but when theywere asked to think abouttheir romantic life, theycertainly had an emotionalreaction. The students whohad many dates wereremindedofahappyaspectoftheir life, while those whohad none were reminded ofloneliness and rejection. The

emotion aroused by thedating question was still oneveryone’s mind when thequery about generalhappinesscameup.The psychology of what

happened is preciselyanalogous to the psychologyofthesizeillusioninfigure9.“Happinessthesedays”isnota natural or an easyassessment. A good answerrequires a fair amount ofthinking. However, the

students who had just beenasked about their dating didnot need to think hardbecause they already had intheir mind an answer to arelated question: how happytheywerewiththeirlovelife.They substituted thequestionto which they had areadymade answer for thequestiontheywereasked.Here again, as we did for

the illusion,we can ask:Arethe students confused? Do

they really think that the twoquestions—theonetheywereasked and the one theyanswer—are synonymous?Of course not. The studentsdo not temporarily lose theirabilitytodistinguishromanticlife from life as a whole. Ifaskedaboutthetwoconcepts,they would say they aredifferent. But they were notasked whether the conceptsare different. They wereasked how happy they were,

and System 1 has a readyanswer.Dating is not unique. The

same pattern is found if aquestion about the students’relationswiththeirparentsorabout their financesimmediately precedes thequestion about generalhappiness. In both cases,satisfaction in the particulardomain dominates happinessreports. Any emotionallysignificantquestionthatalters

aperson’smoodwillhavethesame effect. WYSIATI. Thepresent state of mind loomsvery large when peopleevaluatetheirhappiness.

TheAffectHeuristicThe dominance ofconclusionsoverargumentsismost pronounced whereemotions are involved. Thepsychologist Paul Slovic hasproposed an affect heuristic

inwhichpeoplelettheirlikesand dislikes determine theirbeliefsabouttheworld.Yourpolitical preferencedeterminestheargumentsthatyou find compelling. If youlikethecurrenthealthpolicy,you believe its benefits aresubstantialanditscostsmoremanageable than the costs ofalternatives. If you are ahawk in your attitude towardother nations, youprobabltheр"0%y think they

arerelativelyweakandlikelyto submit to your country’swill. If you are a dove, youprobablythinktheyarestrongand will not be easilycoerced. Your emotionalattitude to such things asirradiated food, red meat,nuclear power, tattoos, ormotorcycles drives yourbeliefs about their benefitsand their risks. If youdislikeany of these things, youprobablybelievethatitsrisks

are high and its benefitsnegligible.Theprimacyof conclusions

doesnotmeanthatyourmindiscompletelyclosedand thatyour opinions are whollyimmune to information andsensible reasoning. Yourbeliefs, and even youremotional attitude, maychange(atleastalittle)whenyou learn that the risk of anactivity you disliked issmaller than you thought.

However, the informationabout lower risks will alsochange your view of thebenefits (for the better) evenif nothing was said aboutbenefits in the informationyoureceived.We see here a new side of

the“personality”ofSystem2.Until now I have mostlydescribeditasamoreor lessacquiescent monitor, whichallowsconsiderableleewaytoSystem 1. I have also

presented System 2 as activein deliberatememory search,complex computations,comparisons, planning, andchoice. In the bat-and-ballproblem and in many otherexamples of the interplaybetween the two systems, itappeared that System 2 isultimatelyincharge,withtheability to resist thesuggestionsofSystem1,slowthings down, and imposelogical analysis. Self-

criticism is one of thefunctionsofSystem2. In thecontextofattitudes,however,System 2 is more of anapologist for the emotions ofSystem 1 than a critic ofthose emotions—an endorserrather than an enforcer. Itssearch for information andarguments is mostlyconstrained to informationthat is consistent withexisting beliefs, not with anintention to examine them.

An active, coherence-seekingSystem 1 suggests solutionstoanundemandingSystem2.

SpeakingofSubstitutionand

Heuristics

“Do we still rememberthe question we aretrying to answer? Orhave we substituted aneasierone?”

“Thequestionwefaceiswhether this candidatecan succeed. Thequestion we seem toanswer is whether sheinterviews well. Let’snotsubstitute.”

“He likes theproject, sohe thinks its costs arelow and its benefits arehigh. Nice example of

theaffectheuristic.”

“Weareusinglastyear’sperformance as aheuristic to predict thevalueofthefirmseveralyears from now. Is thisheuristic good enough?What other informationdoweneed?”

The table below contains alist of features and activitiesthat have been attributed to

System 1. Each of the activesentences replaces astatement, technically moreaccurate but harder tounderstand, to the effect thata mental event occursautomatically and fast. Myhope is that the list of traitswill help you develop anintuitive sense of the“personality” of the fictitiousSystem 1. As happens withother characters you know,youwill have hunches about

what System 1 would dounder differentcircumstances, and most ofyourhuncheswillbecorrect.

Characteristics ofSystem1

generates impressions,feelings, andinclinations; when

endorsed by System 2these become beliefs,attitudes,andintentionsoperates automaticallyand quickly, with littleor no effort, and nosense of voluntarycontrolcan be programmed bySystem 2 to mobilizeattention when aparticular pattern isdetected(search)executes skilled

responses and generatesskilled intuitions, afteradequatetrainingcreates a coherentpatternofactivatedideasinassociativememorylinks a sense ofcognitive ease toillusions of truth,pleasant feelings, andreducedvigilancedistinguishes thesurprising from thenormal

infersandinventscausesandintentionsneglects ambiguity andsuppressesdoubtis biased to believe andconfirmexaggerates emotionalconsistency(haloeffect)focuses on existingevidence and ignoresabsent evidence(WYSIATI)generatesalimitedsetofbasicassessments

representssetsbynormsandprototypes,doesnotintegratematches intensitiesacross scales (e.g., sizetoloudness)computes more thanintended (mentalshotgun)sometimessubstitutesaneasier question for adifficultone(heuristics)is more sensitive tochanges than to states

(prospecttheory)*

overweights lowprobabilities*

shows diminishingsensitivity to quantity(psychophysics)*

responds more stronglyto losses than to gains(lossaversion)*

frames decisionproblems narrowly, inisolation from one

another*

P

Part2

P

HeuristicsandBiases

P

TheLawofSmallNumbers

A study of the incidence ofkidney cancer in the 3,141counties of the United a><HЉStates reveals aremarkable pattern. Thecounties in which theincidenceofkidneycancer islowest are mostly rural,sparsely populated, and

located in traditionallyRepublican states in theMidwest, the South, and theWest.What do youmake ofthis?Your mind has been very

activeinthelastfewseconds,anditwasmainlyaSystem2operation. You deliberatelysearched memory andformulatedhypotheses.Someeffort was involved; yourpupilsdilated,andyourheartrate increased measurably.

But System 1 was not idle:the operation of System 2depended on the facts andsuggestions retrieved fromassociative memory. Youprobably rejected the ideathat Republican politicsprovide protection againstkidney cancer. Very likely,youendedupfocusingonthefactthatthecountieswithlowincidence of cancer aremostly rural. The wittystatisticians Howard Wainer

and Harris Zwerling, fromwhomIlearnedthisexample,commented, “It is both easyand tempting to infer thattheir low cancer rates aredirectly due to the cleanliving of the rural lifestyle—no air pollution, no waterpollution,accesstofreshfoodwithout additives.” Thismakesperfectsense.Now consider the counties

in which the incidence ofkidney cancer is highest.

These ailing counties tend tobe mostly rural, sparselypopulated, and located intraditionally Republicanstates in the Midwest, theSouth,andtheWest.Tongue-in-cheek, Wainer andZwerling comment: “It iseasy to infer that their highcancerratesmightbedirectlyduetothepovertyoftherurallifestyle—no access to goodmedical care, a high-fat diet,and too much alcohol, too

much tobacco.”Something iswrong, of course. The rurallifestyle cannot explain bothvery high and very lowincidenceofkidneycancer.Thekeyfactorisnotthatthe

counties were rural orpredominantly Republican. Itis that rural counties havesmall populations. And themain lesson to be learned isnot about epidemiology, it isabout the difficultrelationship between our

mindandstatistics.System1ishighlyadeptinoneformofthinking—it automaticallyand effortlessly identifiescausal connections betweenevents,sometimesevenwhenthe connection is spurious.When told about the high-incidence counties, youimmediately assumed thatthese counties are differentfrom other counties for areason, that there must be acause that explains this

difference. As we shall see,however, System 1 is ineptwhen faced with “merelystatistical” facts, whichchange the probability ofoutcomes but do not causethemtohappen.A random event, by

definition,doesnotlenditselftoexplanation,butcollectionsof random events do behavein a highly regular fashion.Imagine a large urn filledwith marbles. Half the

marbles are red, half arewhite. Next, imagine a verypatient person (or a robot)whoblindlydraws4marblesfrom the urn, records thenumber of red balls in thesample,throwstheballsbackinto theurn,and thendoes itall again,many times. If yousummarize the results, youwill find that theoutcome“2red, 2 white” occurs (almostexactly) 6 times as often asthe outcome “4 red” or “4

white.”This relationship is amathematical fact. You canpredict the outcome ofrepeated sampling from anurnjustasconfidentlyasyoucan predictwhatwill happenif you hit an egg with ahammer. You cannot predictevery detail of how the shellwill shatter, but you can besure of the general idea.There is a difference: thesatisfying sense of causationthat you experience when

thinking of a hammer hittingan egg is altogether absentwhen you think aboutsampling.A related statistical fact is

relevant to the cancerexample.Fromthesameurn,two very patient marblecounters thatрy dake turns.Jackdraws4marblesoneachtrial, Jill draws 7. They bothrecordeachtimetheyobservea homogeneous sample—allwhiteorallred.Iftheygoon

long enough, Jack willobserve such extremeoutcomesmoreoftenthanJill—by a factor of 8 (theexpected percentages are12.5%and1.56%).Again,nohammer, no causation, but amathematicalfact:samplesof4 marbles yield extremeresults more often thansamplesof7marblesdo.Nowimaginethepopulation

of the United States asmarbles in agianturn.Some

marbles are marked KC, forkidney cancer. You drawsamples of marbles andpopulateeachcounty in turn.Rural samples are smallerthanothersamples.Justasinthe game of Jack and Jill,extreme outcomes (very highand/orvery lowcancer rates)aremostlikelytobefoundinsparsely populated counties.This is all there is to thestory.We started from a fact that

calls for a cause: theincidence of kidney cancervarieswidely across countiesand the differences aresystematic.TheexplanationIoffered is statistical: extremeoutcomes(bothhighandlow)aremorelikelytobefoundinsmall than in large samples.This explanation is notcausal. The small populationofacountyneithercausesnorprevents cancer; it merelyallows the incidence of

cancer tobemuchhigher (ormuch lower) than it is in thelargerpopulation.Thedeepertruthisthatthereisnothingtoexplain. The incidence ofcancer is not truly lower orhigher than normal in acounty with a smallpopulation, it just appears tobe so in a particular yearbecause of an accident ofsampling. If we repeat theanalysis next year, we willobserve the same general

pattern of extreme results inthe small samples, but thecounties where cancer wascommon last year will notnecessarily have a highincidence this year. If this isthe case, the differencesbetween dense and ruralcounties do not really countas facts: they are whatscientists call artifacts,observations that areproduced entirely by someaspect of the method of

research—in this case, bydifferencesinsamplesize.The story I have told may

havesurprisedyou,butitwasnot a revelation. You havelongknownthattheresultsoflarge samples deserve moretrust than smaller samples,and even people who areinnocent of statisticalknowledge have heard aboutthis law of large numbers.But “knowing” is not a yes-no affair and you may find

that the following statementsapplytoyou:

The feature “sparselypopulated” did notimmediatelystandoutasrelevant when you readthe epidemiologicalstory.Youwereatleastmildlysurprised by the size ofthe difference betweensamples of 4 and

samplesof7.Even now, you mustexertsomementalefforttosee that thefollowingtwo statements meanexactlythesamething:

Large samples aremore precise thansmallsamples.Smallsamplesyieldextreme resultsmore often thanlargesamplesdo.

Thefirststatementhasaclearring of truth, but until thesecond version makesintuitive sense, you have nottrulyunderstoodthefirst.The bottom line: yes, you

did know that the results oflarge samples are moreprecise, but you may nowrealizethatyoudidnotknowit very well. You are notalone. The first study thatAmos and I did together

showed that evensophisticated researchershave poor intuitions and awobbly understanding ofsamplingeffects.

TheLawofSmallNumbers

My collaboration with Amosintheearly1970sbeganwithadiscussionof theclaimthatpeople who have had notraining in statistics are good

“intuitive statisticians.” Hetold my seminar and me ofresearchers at the Universityof Michigan who weregenerally optimistic aboutintuitive statistics. I hadstrong feelings about thatclaim, which I tookpersonally: I had recentlydiscovered that I was not agoodintuitivestatistician,andI did not believe that I wasworsethanothers.Foraresearchpsychologist,

sampling variation is not acuriosity; it isanuisanceandacostlyobstacle,whichturnsthe undertaking of everyresearch project into agamble. Suppose that youwish to confirm thehypothesis that thevocabulary of the averagesix-year-oldgirlislargerthanthe vocabulary of an averageboy of the same age. Thehypothesis is true in thepopulation; the average

vocabulary of girls is indeedlarger.Girls and boys vary agreat deal, however, and bythe luck of the draw youcould select a sample inwhich the difference isinconclusive, or even one inwhich boys actually scorehigher. If you are theresearcher, this outcome iscostly to you because youhave wasted time and effort,and failed to confirm ahypothesis that was in fact

true. Using a sufficientlylarge sample is the onlywayto reduce the risk.Researchers who pick toosmall a sample leavethemselves at the mercy ofsamplingluck.The risk of error can be

estimated for any givensamplesizebyafairlysimpleprocedure. Traditionally,however, psychologists donotusecalculationstodecideon a sample size. They use

their judgment, which iscommonly flawed.AnarticleI had read shortly before thedebate with Amosdemonstratedthemistakethatresearchers made (they stilldo) by a dramaticobservation. The authorpointedoutthatpsychologistscommonly chose samples sosmall that they exposedthemselves to a 50% risk offailing to confirm their truehypotheses!No researcher in

his right mind would acceptsuch a risk. A plausibleexplanation was thatpsychologists’ decisionsabout sample size reflectedprevalent intuitivemisconceptions of the extentofsamplingvariation.The article shocked me,

because it explained sometroublesIhadhadinmyownresearch. Like most researchpsychologists,Ihadroutinelychosensamplesthatweretoo

small and had often obtainedresults that made no sense.Now I knew why: the oddresultswere actually artifactsof my research method. Mymistake was particularlyembarrassingbecauseItaughtstatistics and knew how tocompute the sample size thatwould reduce the risk offailuretoanacceptablelevel.But I had never chosen asample size by computation.Like my colleagues, I had

trusted tradition and myintuition in planning myexperiments and had neverthought seriously about theissue.WhenAmosvisitedtheseminar, I had alreadyreached the conclusion thatmy intuitions were deficient,and in the course of theseminar we quickly agreedthat the Michigan optimistswerewrong.Amos and I set out to

examine whether I was the

only fool or a member of amajority of fools, by testingwhether researchers selectedfor mathematical expertisewouldmakesimilarmistakes.Wedevelopedaquestionnairethat described realisticresearch situations, includingreplications of successfulexperiments. It asked theresearchers to choose samplesizes, to assess the risks offailure to which theirdecisions exposed them, and

to provide advice tohypothetical graduatestudents planning theirresearch. Amos collected theresponses of a group ofsophisticated participants(including authors of twostatistical textbooks) at ameetatiрp>Amos and I called our first

joint article “Belief in theLawofSmallNumbers.”Weexplained, tongue-in-cheek,that “intuitionsabout random

samplingappeartosatisfythelawof small numbers,whichasserts that the law of largenumbers applies to smallnumbers as well.” We alsoincluded a strongly wordedrecommendation thatresearchers regard their“statistical intuitions withproper suspicion and replaceimpression formation bycomputation wheneverpossible.”

ABiasofConfidenceOverDoubt

In a telephone poll of300 seniors, 60%supportthepresident.

If you had to summarize themessage of this sentence inexactly three words, whatwould they be? Almostcertainly you would choose“elderly support president.”

Thesewords provide the gistof the story. The omitteddetailsofthepoll, thatitwasdone on the phone with asample of 300, are of nointerest in themselves; theyprovide backgroundinformation that attracts littleattention. Your summarywould be the same if thesample size had beendifferent. Of course, acompletely absurd numberwoulddrawyourattention(“a

telephone poll of 6 [or 60million] elderly voters…”).Unlessyouareaprofessional,however, you may not reactvery differently to a sampleof 150 and to a sample of3,000.Thatisthemeaningofthestatementthat“peoplearenot adequately sensitive tosamplesize.”Themessage about the poll

contains information of twokinds: the story and thesourceofthestory.Naturally,

you focuson the story ratherthan on the reliability of theresults.Whenthereliabilityisobviously low, however, themessage will be discredited.Ifyouaretoldthat“apartisangrouphasconductedaflawedand biased poll to show thatthe elderly support thepresident…” you will ofcourse reject the findings ofthe poll, and they will notbecome part of what youbelieve. Instead, the partisan

poll and its false results willbecome a new story aboutpoliticallies.Youcanchooseto disbelieve a message insuch clear-cut cases. But doyou discriminate sufficientlybetween “I read in The NewYork Times…” and “I heardat the watercooler…”? Canyour System 1 distinguishdegrees of belief? Theprinciple of WY SIATIsuggeststhatitcannot.As I described earlier,

System 1 is not prone todoubt. It suppressesambiguity and spontaneouslyconstructs stories that are ascoherent as possible. Unlessthe message is immediatelynegated, the associations thatitevokeswillspreadasifthemessagewere true.System2iscapableofdoubt,becauseitcan maintain incompatiblepossibilitiesatthesametime.However, sustaining doubt isharderwork than sliding into

certainty. The law of smallnumbersisamanifestationofa general bias that favorscertainty over doubt, whichwillturnupinmanyguisesinfollowingchapters.The strong bias toward

believing that small samplesclosely resemble thepopulation from which theyare drawn is also part of alarger story: we are prone toexaggerate the consistencyand coherence of what we

see.The exaggerated faith ofresearchers in what can belearned from a fewobservationsiscloselyrelatedto the halo effect thрhe , thesense we often get that weknow and understand aperson about whom weactually know very little.System 1 runs ahead of thefacts in constructing a richimage on the basis of scrapsof evidence. A machine forjumping to conclusions will

actasifitbelievedinthelawof small numbers. Moregenerally, it will produce arepresentation of reality thatmakestoomuchsense.

CauseandChanceThe associative machineryseeks causes. The difficultywe have with statisticalregularities is that they callfor a different approach.Instead of focusing on how

theeventathandcametobe,the statistical view relates ittowhatcouldhavehappenedinstead.Nothing inparticularcaused it to be what it is—chance selected it fromamongitsalternatives.Our predilection for causal

thinkingexposesustoseriousmistakes in evaluating therandomness of truly randomevents. For an example, takethe sex of six babies born insequence at a hospital. The

sequenceofboysandgirls isobviouslyrandom; theeventsareindependentofeachother,and the number of boys andgirls who were born in thehospital in the last fewhourshas no effect whatsoever onthesexofthenextbaby.Nowconsider three possiblesequences:

BBBGGGGGGGGGBGBBGB

Are the sequences equallylikely? The intuitive answer—“of course not!”—is false.Because the events areindependent and because theoutcomes B and G are(approximately) equallylikely, then any possiblesequence of six births is aslikelyasanyother.Evennowthatyouknowthisconclusionis true, it remainscounterintuitive,becauseonly

the third sequence appearsrandom. As expected,BGBBGB is judged muchmorelikelythantheothertwosequences. We are patternseekers, believers in acoherent world, in whichregularities (such as asequence of six girls) appearnotbyaccidentbutasaresultofmechanical causalityorofsomeone’s intention. We donot expect to see regularityproduced by a random

process, and whenwe detectwhatappearstobearule,wequicklyrejecttheideathattheprocess is truly random.Random processes producemany sequences thatconvince people that theprocess is not random afterall. You can see whyassuming causality couldhave had evolutionaryadvantages. It is part of thegeneral vigilance that wehave inherited from

ancestors. We areautomatically on the lookoutfor the possibility that theenvironment has changed.Lions may appear on theplain at random times, but itwould be safer to notice andrespond to an apparentincrease in the rate ofappearanceofpridesoflions,evenifitisactuallyduetothefluctuations of a randomprocess.The widespread

misunderstanding ofrandomness sometimes hassignificant consequences. Inour article onrepresentativeness,AmosandIcitedthestatisticianWilliamFeller, who illustrated theease with which people seepatterns where none exists.During the intensive rocketbombingofLondoninWorldWar II, it was generallybelieved that the bombingcouldnotberandombecause

a map of the hits revealedconspicuous gaps. Somesuspected that German spieswerelocatedintheunharmedareas. A careful statisticalanalysis revealed that thedistribution of hits wastypicalofarandomprocess—andtypicalaswellinevokinga strong impression that itwas not random. “To theuntrained eye,” Fellerremarks, “randomnessappears as regularity or

tendencytocluster.”I soon had an occasion to

apply what I had learnedfrpeaрrainom Feller. TheYom Kippur War broke outin 1973, and my onlysignificantcontributiontothewareffortwastoadvisehighofficers in the Israeli AirForcetostopaninvestigation.The air war initially wentquitebadlyforIsrael,becauseof the unexpectedly goodperformance of Egyptian

ground-to-airmissiles.Losseswerehigh,andtheyappearedto be unevenly distributed. Iwas told of two squadronsflying from the same base,one of which had lost fourplanes while the other hadlost none. An inquiry wasinitiated in the hope oflearningwhat it was that theunfortunate squadron wasdoing wrong. There was noprior reason to believe thatone of the squadrons was

moreeffectivethantheother,and no operationaldifferenceswerefound,butofcourse the lives of the pilotsdiffered in many randomways, including, as I recall,how often they went homebetween missions andsomething about the conductof debriefings. My advicewasthatthecommandshouldaccept that the differentoutcomes were due to blindluck, and that the

interviewing of the pilotsshould stop. I reasoned thatluck was the most likelyanswer, that a randomsearchfor a nonobvious cause washopeless, and that in themeantime the pilots in thesquadron that had sustainedlosses did not need the extraburdenofbeingmade to feelthat they and their deadfriendswereatfault.Someyearslater,Amosand

his students Tom Gilovich

andRobertVallone caused astir with their study ofmisperceptions ofrandomness in basketball.The “fact” that playersoccasionally acquire a hothandisgenerallyacceptedbyplayers, coaches, and fans.Theinferenceisirresistible:aplayer sinks three or fourbaskets in a row and youcannot help forming thecausal judgment that thisplayer is now hot, with a

temporarily increasedpropensity to score. Playerson both teams adapt to thisjudgment—teammates aremorelikelytopasstothehotscorer and the defense ismore likely to doubleteam.Analysis of thousands ofsequences of shots led to adisappointing conclusion:thereisnosuchthingasahothand in professionalbasketball, either in shootingfromthefieldorscoringfrom

thefoulline.Ofcourse,someplayers are more accuratethanothers, but the sequenceofsuccessesandmissedshotssatisfies all tests ofrandomness. The hot hand isentirely in the eye of thebeholders, who areconsistently too quick toperceive order and causalityin randomness.Thehot handis a massive and widespreadcognitiveillusion.The public reaction to this

research is part of the story.Thefindingwaspickedupbythe press because of itssurprisingconclusion,andthegeneral response wasdisbelief. When thecelebrated coach of theBoston Celtics, RedAuerbach, heard of Gilovichand his study, he responded,“Who is this guy? So hemakesastudy.Icouldn’tcareless.” The tendency to seepatterns in randomness is

overwhelming—certainlymore impressive than a guymakingastudy.The illusion of pattern

affects our lives in manywaysoffthebasketballcourt.Howmanygoodyearsshouldyou wait before concludingthat an investment adviser isunusuallyskilled?Howmanysuccessfulacquisitionsshouldbe needed for a board ofdirectors to believe that theCEO has extraordinary flair

for such deals? The simpleanswer to these questions isthat if you follow yourintuition,youwillmoreoftenthannoterrbymisclassifyingarandomeventassystematic.We are far too willing toreject thebelief thatmuchofwhatweseeinlifeisrandom.Ibeganthischapterwiththe

example of cancer incidenceacross theUnitedStates.Theexample appears in a bookintended for statistics

teachers, but I learned aboutit fromanamusingarticlebythe two statisticians I quotedearlier, Howard Wainer andHarris Zwerling. Their essayfocused on a largeiiveрothersnvestment, some$1.7 billion,which theGatesFoundation made to followup intriguing findings on thecharacteristics of the mostsuccessful schools. Manyresearchers have sought thesecretofsuccessfuleducation

by identifying the mostsuccessfulschoolsinthehopeof discovering whatdistinguishes them fromothers. One of theconclusionsofthisresearchisthat the most successfulschools, on average, aresmall. In a survey of 1,662schools in Pennsylvania, forinstance,6ofthetop50weresmall, which is anoverrepresentationbyafactorof 4. These data encouraged

theGatesFoundationtomakea substantial investment inthecreationofsmallschools,sometimes by splitting largeschools intosmallerunits.Atleast half a dozen otherprominent institutions, suchas theAnnenbergFoundationandthePewCharitableTrust,joined the effort, as did theU.S. Department ofEducation’sSmallerLearningCommunitiesProgram.This probably makes

intuitive sense to you. It iseasy to construct a causalstorythatexplainshowsmallschools are able to providesuperior education and thusproduce high-achievingscholarsbygivingthemmorepersonal attention andencouragement than theycould get in larger schools.Unfortunately, the causalanalysis is pointless becausethe facts are wrong. If thestatisticians who reported to

the Gates Foundation hadasked about thecharacteristics of the worstschools, they would havefound that bad schools alsotend to be smaller thanaverage. The truth is thatsmall schools are not betteron average; they are simplymore variable. If anything,say Wainer and Zwerling,largeschoolstendtoproducebetter results, especially inhighergradeswhereavariety

of curricular options isvaluable.Thanks to recent advances

in cognitive psychology, wecan now see clearly whatAmos and I could onlyglimpse: the law of smallnumbers ispartof two largerstoriesabout theworkingsofthemind.

Theexaggerated faith insmall samples is only

one example of a moregeneralillusion—wepaymore attention to thecontentofmessagesthanto information abouttheir reliability,andasaresultendupwithaviewof the world around usthat issimplerandmorecoherent than the datajustify. Jumping toconclusions is a safersportintheworldofourimagination than it is in

reality.Statistics produce manyobservations that appearto beg for causalexplanations but do notlend themselves to suchexplanations.Manyfactsof the world are due tochance, includingaccidents of sampling.Causal explanations ofchance events areinevitablywrong.

SpeakingoftheLawofSmallNumbers

“Yes, thestudiohashadthree successful filmssincethenewCEOtookover. But it is too earlyto declare he has a hothand.”

“Iwon’tbelievethat thenew trader is a genius

before consulting astatistician who couldestimate the likelihoodof his streak being achanceevent.”

“The sample ofobservationsistoosmallto make any inferences.Let’snot follow the lawofsmallnumbers.”

“I plan to keep the

resultsoftheexperimentsecret until we have asufficiently largesample.Otherwisortрxpere wewill face pressure toreach a conclusionprematurely.”

P

Anchors

Amos and I once rigged awheel of fortune. It wasmarkedfrom0to100,butwehad it built so that it wouldstop only at 10 or 65. Werecruited students of theUniversity of Oregon asparticipants in ourexperiment.Oneofuswouldstand in front of a small

group, spin the wheel, andask them to write down thenumber on which the wheelstopped,whichofcoursewaseither 10 or 65. We thenaskedthemtwoquestions:

Is the percentage ofAfrican nations amongUN members larger orsmaller than the numberyoujustwrote?

What is your best guess

of the percentage ofAfrican nations in theUN?

The spin of a wheel offortune—evenone that isnotrigged—cannotpossiblyyielduseful information aboutanything,andtheparticipantsin our experiment shouldsimply have ignored it. Butthey did not ignore it. Theaverage estimates of thosewhosaw10and65were25%

and45%,respectively.The phenomenon we were

studyingissocommonandsoimportant in the everydayworld that you should knowits name: it is an anchoringeffect. It occurswhen peopleconsideraparticularvalueforan unknown quantity beforeestimating that quantity.What happens is one of themost reliable and robustresults of experimentalpsychology: the estimates

stay close to thenumber thatpeopleconsidered—hencetheimageofananchor.Ifyouareasked whether Gandhi wasmorethan114yearsoldwhenhediedyouwillendupwithamuchhigherestimateofhisage at death than you wouldif the anchoring questionreferredtodeathat35.Ifyouconsider how much youshould pay for a house, youwill be influenced by theaskingprice.Thesamehouse

will appear more valuable ifitslistingpriceishighthanifit is low, even if you aredetermined to resist theinfluenceofthisnumber;andso on—the list of anchoringeffects is endless. Anynumber thatyouareasked toconsider as a possiblesolution to an estimationproblem will induce ananchoringeffect.We were not the first to

observe the effects of

anchors, but our experimentwasthefirstdemonstrationofits absurdity: people’sjudgmentswereinfluencedbyan obviously uninformativenumber.Therewasnowaytodescribe the anchoring effectof a wheel of fortune asreasonable. Amos and Ipublished the experiment inour Science paper, and it isoneof thebestknownof thefindingswereportedthere.Therewasonlyonetrouble:

Amos and I did not fullyagree on the psychology ofthe anchoring effect. Hesupported one interpretation,Ilikedanother,andweneverfound a way to settle theargument. The problem wasfinally solved decades laterby the efforts of numerousinvestigators. It is now clearthat Amos and I were bothright. Two differentmechanisms produceanchoring effects—one for

each system.There is a formof anchoring that occurs in adeliberate process ofadjustment, an operation ofSystem 2. And there isanchoring that occurs by apriming effect, an automaticmanifestationofSystem1.

AnchoringasAdjustment

Amos liked the idea of anadjust-and-anchorheuristicas

a strategy for estimatinguncertain quantities: startfrom an anchoring number,assess whether it is too highor too low, and graduallyadjust your estimate bymentally “moving” from theanchor. The adjustmenttypically ends prematurely,because people stop whentheyarenolongercertainthatthey should move farther.Decades after ourdisagreement, andyears after

Amos’s death, convincingevidence of such a processwasofferedindependentlybytwo psychologists who hadworked closely with Amosearly in their careers: EldarShafir and Tom Gilovichtogether with their ownstudents—Amos’sintellectualgrandchildren!Togettheidea,takeasheet

ofpaper anddrawa2½-inchline going up, starting at thebottom of the page—without

a ruler. Now take anothersheet,andstartatthetopanddrawa linegoingdownuntilit is 2½ inches from thebottom. Compare the lines.There is a good chance thatyour first estimate of 2½inches was shorter than thesecond. The reason is thatyoudonotknowexactlywhatsuchalinelookslike;thereisa range of uncertainty. Youstop near the bottom of theregion of uncertainty when

you start from the bottom ofthe page and near the top ofthe region when you startfrom the top. Robyn LeBoeufandShafirfoundmanyexamples of that mechanismin daily experience.Insufficientadjustmentneatlyexplainswhyyouarelikelytodrivetoofastwhenyoucomeoff the highway onto citystreets—especially if you aretalking with someone as youdrive. Insufficient adjustment

is also a source of tensionbetween exasperated parentsandteenagerswhoenjoyloudmusic in their room. LeBoeuf and Shafir note that a“well-intentioned child whoturns down exceptionallyloudmusictomeetaparent’sdemand that itbeplayedata‘reasonable’volumemay failto adjust sufficiently from ahigh anchor, and may feelthat genuine attempts atcompromise are being

overlooked.” The driver andthe child both deliberatelyadjust down, and both fail toadjustenough.Now consider these

questions:

When did GeorgeWashington becomepresident?What is the boilingtemperature of water atthe top of MountEverest?

The first thing that happenswhen you consider each ofthese questions is that ananchor comes to your mind,and you know both that it iswrongandthedirectionofthecorrect answer. You knowimmediately that GeorgeWashingtonbecamepresidentafter 1776, and you alsoknow that the boilingtemperature of water at thetopofMountEverestislower

than 100°C. You have toadjust in the appropriatedirection by findingarguments to move awayfrom the anchor. As in thecase of the lines, you arelikely to stop when you arenolongersureyoushouldgofarther—at the near edge oftheregionofuncertainty.

Nick Epley and TomGilovich found evidence that

adjustment is a deliberateattempt to find reasons tomove away from the anchor:people who are instructed toshake their head when theyhear the anchor, as if theyrejectedit,movefartherfromthe anchor, and people whonod their head showenhanced anchoring. Epleyand Gilovich also confirmedthatadjustmentisaneffortfuloperation. People adjust less(stay closer to the anchor)

when their mental resourcesare depleted, either becausetheir memory is loaded withdighdth=igitsorbecausetheyare slightly drunk.Insufficient adjustment is afailure of a weak or lazySystem2.SowenowknowthatAmos

was right for at least somecases of anchoring, whichinvolveadeliberateSystem2adjustment in a specifieddirectionfromananchor.

AnchoringasPrimingEffect

When Amos and I debatedanchoring, I agreed thatadjustmentsometimesoccurs,butIwasuneasy.Adjustmentis a deliberate and consciousactivity, but inmost cases ofanchoring there is nocorresponding subjectiveexperience. Consider thesetwoquestions:

Was Gandhi more orless than 144 years oldwhenhedied?How old was Gandhiwhenhedied?

Did you produce yourestimate by adjusting downfrom 144? Probably not, buttheabsurdlyhighnumberstillaffected your estimate. Myhunchwasthatanchoringisacaseofsuggestion.Thisistheword we use when someone

causesustosee,hear,orfeelsomethingbymerelybringingit to mind. For example, thequestion “Doyounow feel aslight numbness in your leftleg?”alwayspromptsquiteafewpeopletoreportthattheirleft leg does indeed feel alittlestrange.Amos was more

conservativethanIwasabouthunches, and he correctlypointed out that appealing tosuggestion did not help us

understand anchoring,becausewedidnotknowhowtoexplainsuggestion.Ihadtoagree thathewas right,but Inever became enthusiasticabout the idea of insufficientadjustment as the sole causeof anchoring effects. Weconductedmany inconclusiveexperiments in an effort tounderstandanchoring,butwefailedandeventuallygaveupthe idea of writing moreaboutit.

The puzzle that defeated usis now solved, because theconcept of suggestion is nolonger obscure: suggestion isa priming effect, whichselectivelyevokescompatibleevidence.Youdidnotbelievefor a moment that Gandhilived for 144 years, but yourassociative machinery surelygenerated an impression of averyancientperson.System1understands sentences bytryingtomakethemtrue,and

the selective activation ofcompatiblethoughtsproducesa family of systematic errorsthat make us gullible andprone to believe too stronglywhateverwebelieve.WecannowseewhyAmosandIdidnot realize that there weretwo types of anchoring: theresearch techniques andtheoretical ideas we neededdid not yet exist. They weredeveloped, much later, byother people. A process that

resembles suggestion isindeed at work in manysituations: System 1 tries itsbest to construct a world inwhich the anchor is the truenumber. This is one of themanifestations of associativecoherence that I described inthefirstpartofthebook.The German psychologists

ThomasMussweilerandFritzStrack offered the mostcompellingdemonstrationsofthe role of associative

coherence in anchoring. Inone experiment, they askedan anchoring question abouttemperature: “Is the annualmean temperature inGermany higher or lowerthan20°C(68°F)?”or“Istheannual mean temperature inGermany higher or lowerthan5°C(40°F)?”All participants were then

brieflyshownwordsthattheywere asked to identify. Theresearchers found that 68°F

made it easier to recognizesummer words (like sun andbeach), and 40°F facilitatedwinter words (like frost andski). The selective activationof compatible memoriesexplains anchoring: the highandthelownumbersactivatedifferent sets of ideas inmemory. The estimates ofannual temperature draw onthesebiasedsamplesofideasand are therefore biased aswell.Inanotherelegantstudy

in thesamevein,participantswereaskedabouttheaveragepriceofGermancars.Ahighanchorselectivelyprimed thenames of luxury brands(Mercedes, Audi), whereasthelowanchorprimedbrandsassociated with mass-marketcars (Volkswagen). We sawearlier that any prime willtend to evoke informationthat is compatible with it.Suggestionandanchoringareboth explained by the same

automatic operation ofSystem1.AlthoughIdidnotknow how to prove it at thetime,myhunchaboutthelinkbetween anchoring andsuggestion turned out to becorrect.

TheAnchoringIndexMany psychologicalphenomena can bedemonstrated experimentally,but few can actually be

measured. The effect ofanchors is an exception.Anchoring can be measured,anditisanimpressivelylargeeffect. Some visitors at theSan Francisco Exploratoriumwereaskedthefollowingtwoquestions:

Is the height of thetallest redwood more orlessthan1,200feet?What is your best guessabout the height of the

tallestredwood?The “high anchor” in thisexperiment was 1,200 feet.For other participants, thefirst question referred to a“lowanchor”of180feet.Thedifference between the twoanchorswas1,020feet.Asexpected,thetwogroups

producedverydifferentmeanestimates: 844 and 282 feet.The difference between themwas 562 feet. The anchoring

index is simply the ratio ofthe two differences(562/1,020) expressed as apercentage: 55%. Theanchoring measure would be100% for people whoslavishly adopt the anchor asan estimate, and zero forpeoplewhoareabletoignorethe anchor altogether. Thevalue of 55% that wasobserved in this example istypical. Similar values havebeen observed in numerous

otherproblems.Theanchoringeffectisnota

laboratorycuriosity;itcanbejust as strong in the realworld. In an experimentconducted some years ago,real-estate agents were givenan opportunity to assess thevalue of a house that wasactually on themarket. Theyvisited the house and studieda comprehensive booklet ofinformation that included anasking price. Half the agents

saw an asking price thatwassubstantially higher than thelisted price of the house; theotherhalfsawanaskingpricethat was substantially lower.Each agent gave her opinionabout a reasonable buyingprice for the house and thelowest price at which shewouldagreetosell thehouseif she owned it. The agentswere then asked about thefactorsthathadaffectedtheirjudgment. Remarkably, the

asking price was not one ofthese factors; theagents tookprideintheirabilitytoignoreit. They insisted that thelistingpricehadno effectontheirresponses,buttheywerewrong: the anchoring effectwas 41%. Indeed, theprofessionals were almost assusceptible to anchoringeffects as business schoolstudents with no real-estateexperience, whose anchoringindex was 48%. The only

difference between the twogroups was that the studentsconceded that they wereinfluenced by the anchor,while the professionalsdeniedthatinfluence.Powerful anchoring effects

are found in decisions thatpeople make about money,such as when they choosehowmuch to contribute al.lsdenied to a cause. Todemonstrate this effect, wetold participants in the

Exploratorium study aboutthe environmental damagecaused by oil tankers in thePacific Ocean and askedabout their willingness tomake an annual contribution“to save 50,000 offshorePacific Coast seabirds fromsmalloffshoreoilspills,untilways are found to preventspills or require tankerowners to pay for theoperation.” This questionrequires intensity matching:

the respondents are asked, ineffect, to find the dollaramountofacontribution thatmatches the intensityof theirfeelings about the plight ofthe seabirds. Some of thevisitors were first asked ananchoring question, such as,“Wouldyoubewillingtopay$5…,”beforethepoint-blankquestion of how much theywouldcontribute.When no anchor was

mentioned, thevisitors at the

Exploratorium—generally anenvironmentally sensitivecrowd—said they werewilling to pay $64, onaverage.When theanchoringamount was only $5,contributions averaged $20.Whentheanchorwasaratherextravagant $400, thewillingness to pay rose to anaverageof$143.The difference between the

high-anchor and low-anchorgroups was $123. The

anchoring effect was above30%, indicating thatincreasing the initial requestby $100 brought a return of$30inaveragewillingnesstopay.Similar or even larger

anchoring effects have beenobtained in numerous studiesof estimates and ofwillingness to pay. Forexample, French residents ofthe heavily pollutedMarseilles regionwere asked

what increase in living coststhey would accept if theycould live in a less pollutedregion. The anchoring effectwas over 50% in that study.Anchoring effects are easilyobserved in online trading,where the same item isoftenoffered at different “buynow” prices. The “estimate”in fine-art auctions isalsoananchor that influences thefirstbid.There are situations in

which anchoring appearsreasonable.Afterall, it isnotsurprising that people whoare asked difficult questionsclutch at straws, and theanchorisaplausiblestraw.Ifyou know next to nothingabout the trees of Californiaand are asked whether aredwood can be taller than1,200 feet, you might inferthatthisnumberisnottoofarfrom the truth. Somebodywho knows the true height

thought up that question, sotheanchormaybeavaluablehint. However, a key findingof anchoring research is thatanchors that are obviouslyrandom can be just aseffective as potentiallyinformative anchors. Whenweusedawheeloffortunetoanchor estimates of theproportionofAfricannationsin the UN, the anchoringindex was 44%, well withinthe range of effects observed

with anchors that couldplausibly be taken as hints.Anchoring effects of similarsize have been observed inexperimentsinwhichthelastfewdigitsoftherespondent’sSocial Security number wasused as the anchor (e.g., forestimating the number ofphysicians in their city). Theconclusion is clear: anchorsdo not have their effectsbecause people believe theyareinformative.

The power of randomanchors has beendemonstrated in someunsettling ways. Germanjudges with an average ofmore than fifteen years ofexperience on the bench firstreadadescriptionofawomanwho had been caughtshoplifting, then rolledapairof dice that were loaded soevery roll resulted ineithera3ora9.Assoonas thedicecame to a stop, the judges

were asked whether theywouldsentencethewomantoa term in prison greater orlesser, in months, than thenumber showingon thedice.Finally, the judges wereinstructedtospecifytheexactprison sentence they wouldgive to the shoplifter. Onaverage,thosewhohadrolleda9 said theywould sentenceher to 8 months; those whorolleda3saidthifAfricatheywould sentence her to 5

months; the anchoring effectwas50%.

UsesandAbusesofAnchors

By now you should beconvinced that anchoringeffects—sometimes due topriming, sometimes toinsufficient adjustment—areeverywhere. Thepsychological mechanismsthat produce anchoringmake

us far more suggestible thanmostofuswouldwanttobe.Andofcoursetherearequiteafewpeoplewhoarewillingand able to exploit ourgullibility.Anchoring effects explain

why, for example, arbitraryrationing is an effectivemarketing ploy. A few yearsago, supermarket shoppers inSioux City, Iowa,encountered a salespromotion for Campbell’s

soup at about 10% off theregular price.On some days,a sign on the shelf said limitof 12 per person. On otherdays, the sign said no limitper person. Shopperspurchased an average of 7cans when the limit was inforce, twice asmany as theybought when the limit wasremoved. Anchoring is notthe sole explanation.Rationing also implies thatthe goods are flying off the

shelves, and shoppers shouldfeel some urgency aboutstocking up. But we alsoknow that themention of 12cans as a possible purchasewould produce anchoringeven if the number wereproducedbyaroulettewheel.Weseethesamestrategyat

work in the negotiation overthepriceofahome,whenthesellermakesthefirstmovebysetting the list price. As inmany other games, moving

firstisanadvantageinsingle-issue negotiations—forexample, when price is theonly issue to be settledbetweenabuyerandaseller.Asyoumayhaveexperiencedwhennegotiatingfor thefirsttime in a bazaar, the initialanchorhasapowerful effect.MyadvicetostudentswhenItaughtnegotiationswasthatifyou think the other side hasmadeanoutrageousproposal,you should not come back

with an equally outrageouscounteroffer, creating a gapthatwillbedifficulttobridgein further negotiations.Instead you should make ascene, storm out or threatentodoso,andmakeitclear—to yourself as well as to theother side—that youwill notcontinue the negotiationwiththatnumberonthetable.The psychologists Adam

Galinsky and ThomasMussweiler proposed more

subtle ways to resist theanchoring effect innegotiations. They instructednegotiators to focus theirattention and search theirmemory for argumentsagainst the anchor. TheinstructiontoactivateSystem2 was successful. Forexample,theanchoringeffectisreducedoreliminatedwhenthesecondmoverfocuseshisattentionontheminimalofferthat the opponent would

accept,oron thecosts to theopponent of failing to reachan agreement. In general, astrategy of deliberately“thinking the opposite” maybe a good defense againstanchoring effects, because itnegates the biasedrecruitment of thoughts thatproducestheseeffects.Finally, try your hand at

working out the effect ofanchoring on a problem ofpublic policy: the size of

damages in personal injurycases. These awards aresometimes very large.Businesses that are frequenttargetsofsuch lawsuits, suchas hospitals and chemicalcompanies, have lobbied toset a cap on the awards.Before you read this chapteryou might have thought thatcapping awards is certainlygoodforpotentialdefendants,butnowyoushouldnotbesosure. Consider the effect of

cappingawardsat$1million.This rulewould eliminate alllargerawards,but theanchorwouldalsopullupthesizeofmany awards that wouldotherwisebemuchsmaller.Itwould almost certainlybenefit serious offenders andlarge firms much more thansmallones.

AnchoringandtheTwoSystems

The effects of randomanchors havemuch to tell usabout the relationshipbetween System 1 andSystem 2. Anchoring effectshave always been studied intasksof judgmentandchoicethat are ultimately completedby System 2. However,System 2works on data thatis retrieved frommemory, inan automatic and involuntaryoperation of System 1.

System 2 is thereforesusceptible to the biasinginfluence of anchors thatmakesomeinformationeasierto retrieve. Furthermore,System2hasnocontroloverthe effect and no knowledgeof it. The participants whohavebeenexposedtorandomor absurd anchors (such asGandhi’s death at age 144)confidently deny that thisobviouslyuselessinformationcould have influenced their

estimate,andtheyarewrong.Wesawinthediscussionof

thelawofsmallnumbersthata message, unless it isimmediatelyrejectedasa lie,will have the same effect onthe associative systemregardless of its reliability.Thegistofthemessageisthestory, which is based onwhatever information isavailable,evenifthequantityof the information is slightand its quality is poor:

WYSIATI.When you read astory about the heroic rescueof a wounded mountainclimber, its effect on yourassociative memory is muchthesameifitisanewsreportor the synopsis of a film.Anchoring results from thisassociative activation.Whether the story is true, orbelievable,matterslittle, ifatall. The powerful effect ofrandomanchorsisanextremecase of this phenomenon,

because a random anchorobviously provides noinformationatall.Earlier I discussed the

bewildering variety ofpriming effects, in whichyour thoughts and behaviormaybe influencedbystimulitowhichyoupaynoattentionatall, andevenbystimuliofwhich you are completelyunaware. The main moral ofpriming research is that ourthoughtsandourbehaviorare

influenced, much more thanwe know or want, by theenvironment of the moment.Manypeoplefindtheprimingresults unbelievable, becausethey do not correspond tosubjective experience. Manyothers find the resultsupsetting, because theythreaten the subjective senseof agency and autonomy. Ifthe content of a screen saveronanirrelevantcomputercanaffect your willingness to

help strangers without yourbeing aware of it, how freeare you? Anchoring effectsare threatening in a similarway. You are always awareof the anchor and even payattentiontoit,butyoudonotknow how it guides andconstrains your thinking,because you cannot imaginehowyouwouldhave thoughtif the anchor had beendifferent (or absent).However,youshouldassume

thatanynumberthatisonthetable has had an anchoringeffect on you, and if thestakes are high you shouldmobilize yourself (yourSystem 2) to combat theeffect.

SpeakingofAnchors

“The firm we want toacquire sent us theirbusiness plan, with therevenuetheyexpect.We

shouldn’tletthatnumberinfluence our thinking.Setitaside.”

“Plans are best-casescenarios. Let’s avoidanchoringonplanswhenwe forecast actualoutcomes. Thinkingabout ways the plancould go wrong is onewaytodoit.”

“Our aim in thenegotiation is to getthem anchored on thisnumber.”

&st

“The defendant’slawyers put in afrivolous reference inwhich theymentioned aridiculously low amountofdamages,andtheygot

the judge anchored onit!”

P

TheScienceofAvailability

Amos and I had our mostproductive year in 1971–72,which we spent in Eugene,Oregon. We were the guestsof the Oregon ResearchInstitute, which housedseveral future stars of all thefields inwhichweworked—judgment, decision making,

and intuitive prediction. Ourmain host was Paul Slovic,who had been Amos’sclassmate at Ann Arbor andremained a lifelong friend.Paul was on his way tobecoming the leadingpsychologist among scholarsofrisk,apositionhehasheldfor decades, collecting manyhonors along the way. Paulandhiswife,Roz,introducedustolifeinEugene,andsoonweweredoingwhatpeoplein

Eugene do—jogging,barbecuing, and takingchildren to basketball games.We also worked very hard,running dozens ofexperiments and writing ourarticles on judgmentheuristics. At night I wroteAttentionandEffort. Itwasabusyyear.Oneofourprojectswasthe

study of what we called theavailability heuristic. Wethoughtofthatheuristicwhen

we asked ourselves whatpeopleactuallydowhentheywish to estimate thefrequencyofacategory,suchas “peoplewho divorce aftertheageof60”or“dangerousplants.” The answer wasstraightforward: instances ofthe class will be retrievedfrommemory,andifretrievalis easy and fluent, thecategorywillbejudgedtobelarge. We defined theavailability heuristic as the

process of judging frequencyby “the ease with whichinstancescometomind.”Thestatement seemed clearwhenwe formulated it, but theconcept of availability hasbeen refined since then. Thetwo-systemapproachhadnotyet been developedwhenwestudied availability, and wedid not attempt to determinewhether this heuristic is adeliberate problem-solvingstrategy or an automatic

operation.Wenowknowthatbothsystemsareinvolved.A question we considered

early was how manyinstancesmustberetrievedtogetan impressionof theeasewith which they come tomind. We now know theanswer: none. For anexample,thinkofthenumberof words that can beconstructedfromthetwosetsoflettersbelow.

XUZONLCJMTAPCERHOB

You knew almostimmediately, withoutgeneratinganyinstances,thatone set offers far morepossibilities than the other,probablybya factorof10ormore. Similarly, you do notneedtoretrievespecificnewsstoriestohaveagoodideaofthe relative frequency withwhich different countries

have appeared in the newsduring the past year(Belgium, China, France,Congo, Nicaragua,Romania…).The availability heuristic,

like other heuristics ofjudgment, substitutes onequestion for another: youwish to estimate the size seost c d of a category or thefrequency of an event, butyou report an impression oftheeasewithwhichinstances

cometomind.Substitutionofquestions inevitablyproducessystematic errors. You candiscover how the heuristicleadstobiasesbyfollowingasimple procedure: list factorsother than frequency thatmakeiteasytocomeupwithinstances.Eachfactorinyourlistwillbeapotential sourceof bias. Here are someexamples:

A salient event thatattracts your attentionwill be easily retrievedfrom memory. Divorcesamong Hollywoodcelebrities and sexscandals amongpoliticians attract muchattention, and instanceswill come easily tomind.You are thereforelikely to exaggerate thefrequency of bothHollywooddivorces and

politicalsexscandals.A dramatic eventtemporarilyincreasestheavailability of itscategory. A plane crashthat attracts mediacoverage willtemporarily alter yourfeelingsabout the safetyof flying. Accidents areon your mind, for awhile,afteryouseeacarburningatthesideoftheroad, and the world is

for a while a moredangerousplace.Personal experiences,pictures, and vividexamples are moreavailable than incidentsthat happened to others,or mere words, orstatistics. A judicialerror that affects youwill undermine yourfaith in the justicesystem more than asimilarincidentyouread

aboutinanewspaper.

Resisting this large

collection of potentialavailabilitybiasesispossible,buttiresome.Youmustmakethe effort to reconsider yourimpressionsand intuitionsbyasking such questions as, “Isour belief that theft s byteenagers are a majorproblem due to a few recentinstances in our

neighborhood?” or “Could itbethatIfeelnoneedtogetaflu shot because none of myacquaintancesgot the flu lastyear?” Maintaining one’svigilance against biases is achore—but the chance toavoid a costly mistake issometimesworththeeffort.One of the best-known

studies of availabilitysuggests that awareness ofyour own biases cancontribute to peace in

marriages, and probably inother joint projects. In afamous study, spouses wereasked, “How large was yourpersonal contribution tokeeping the place tidy, inpercentages?” They alsoanswered similar questionsabout “taking out thegarbage,” “initiating socialengagements,”etc.Wouldtheself-estimated contributionsadd up to 100%, ormore, orless? As expected, the self-

assessed contributions addedup to more than 100%. Theexplanation is a simpleavailability bias: bothspouses remember their ownindividual efforts andcontributions much moreclearly than those of theother, and the difference inavailability leads to adifference in judgedfrequency. The bias is notnecessarily self-serving:spouses also overestimated

their contribution to causingquarrels, although to asmaller extent than theircontributions to moredesirableoutcomes.Thesamebias contributes to thecommon observation thatmany members of acollaborative team feel theyhave done more than theirshare and also feel that theothers are not adequatelygrateful for their individualcontributions.

I am generally notoptimistic about the potentialforpersonalcontrolofbiases,but this is an exception. Theopportunity for successfuldebiasing exists because thecircumstancesinwhichissuesof credit allocation come upareeasytoidentify,themoreso because tensions oftenarise when several people atoncefeelthattheireffortsarenot adequately recognized.The mere observation that

there is usually more than100% credit to go around issometimes sufficient todefuse the situation. In anyeve#82ght=nt, it is a goodthing for every individual toremember. You willoccasionally do more thanyourshare,but it isuseful toknow that you are likely tohave that feeling even wheneach member of the teamfeelsthesameway.

ThePsychologyofAvailability

A major advance in theunderstanding of theavailabilityheuristicoccurredin the early 1990s, when agroup of Germanpsychologists led by NorbertSchwarz raised an intriguingquestion: How will people’simpressions of the frequencyofacategorybeaffectedbyarequirementtolistaspecified

numberofinstances?Imagineyourself a subject in thatexperiment:

First,listsixinstancesinwhich you behavedassertively.Next, evaluate howassertiveyouare.

Imagine that you had beenasked for twelve instancesofassertive behavior (a numbermost people find difficult).

Would your view of yourown assertiveness bedifferent?Schwarz and his colleagues

observed that the task oflistinginstancesmayenhancethe judgments of the trait bytwodifferentroutes:

the number of instancesretrievedtheeasewithwhichtheycometomind

The request to list twelveinstances pits the twodeterminants against eachother. On the one hand, youhave just retrieved animpressive number of casesin which youwere assertive.On the other hand,while thefirstthreeorfourinstancesofyour own assertivenessprobably came easily to you,you almost certainlystruggledtocomeupwiththe

last few to complete a set oftwelve; fluency was low.Which will count more—theamount retrieved or the easeandfluencyoftheretrieval?Thecontestyieldedaclear-

cut winner: people who hadjust listed twelve instancesrated themselves as lessassertivethanpeoplewhohadlisted only six. Furthermore,participants who had beenasked to list twelve cases inwhich they had not behaved

assertivelyendedup thinkingof themselves as quiteassertive!Ifyoucannoteasilycome up with instances ofmeekbehavior,youarelikelyto conclude that you are notmeekatall.Self-ratingsweredominated by the ease withwhichexampleshadcome tomind. The experience offluent retrieval of instancestrumped the numberretrieved.An even more direct

demonstration of the role offluencywas offered by otherpsychologists in the samegroup.All the participants intheir experiment listed sixinstances of assertive (ornonassertive) behavior,whilemaintainingaspecifiedfacialexpression. “Smilers” wereinstructed to contract thezygomaticus muscle, whichproduces a light smile;“frowners” were required tofurrow their brow. As you

already know, frowningnormally accompaniescognitivestrainandtheeffectis symmetric: when peopleare instructed to frownwhiledoingatask,theyactuallytryharderandexperiencegreatercognitive strain. Theresearchers anticipated thatthe frowners would havemore difficulty retrievingexamples of assertivebehaviorandwould thereforerate themselves as relatively

lacking in assertiveness.Andsoitwas.

Psychologists enjoyexperiments that yieldparadoxical results, and theyhave appliserv heightedSchwarz’s discovery withgusto.Forexample,people:

believe that they usetheir bicycles less often

after recalling manyratherthanfewinstancesare less confident in achoice when they areasked to produce moreargumentstosupportitarelessconfidentthatanevent was avoidableafterlistingmorewaysitcouldhavebeenavoidedare less impressed by acarafter listingmanyofitsadvantages

AprofessoratUCLAfound

an ingenious way to exploittheavailabilitybias.Heaskeddifferentgroupsofstudentstolist ways to improve thecourse, and he varied therequired number ofimprovements. As expected,the students who listedmoreways to improve the classratedithigher!Perhapsthemostinteresting

finding of this paradoxical

researchisthattheparadoxisnot always found: peoplesometimes go by contentrather than by ease ofretrieval. The proof that youtruly understand a pattern ofbehavior is that you knowhow to reverse it. Schwarzand his colleagues took onthis challenge of discoveringthe conditions under whichthis reversal would takeplace.The ease with which

instances of assertivenesscome to the subject’s mindchanges during the task. Thefirst few instances are easy,but retrieval soon becomesmuch harder. Of course, thesubject also expects fluencyto drop gradually, but thedrop of fluency between sixand twelve instances appearsto be steeper than theparticipant expected. Theresults suggest that theparticipants make an

inference: if I am having somuch more trouble thanexpected coming up withinstancesofmyassertiveness,thenIcan’tbeveryassertive.Note that this inference restson a surprise—fluency beingworse than expected. Theavailability heuristic that thesubjects apply is betterdescribed as an “unexplainedunavailability”heuristic.Schwarz and his colleagues

reasoned that they could

disrupt the heuristic byprovidingthesubjectswithanexplanationforthefluencyofretrieval that theyexperienced. They told theparticipants they would hearbackground music whilerecalling instances and thatthe music would affectperformance in the memorytask.Somesubjectsweretoldthat the music would help,others were told to expectdiminished fluency. As

predicted, participants whoseexperience of fluency was“explained”didnotuseitasaheuristic; the subjects whowere told that music wouldmake retrieval more difficultrated themselves as equallyassertivewhen they retrievedtwelveinstancesaswhentheyretrieved six. Other coverstories have been used withthe same result: judgmentsare no longer influenced byease of retrieval when the

experienceoffluencyisgivenaspuriousexplanationbythepresenceofcurvedorstraighttextboxes,bythebackgroundcolor of the screen, or byother irrelevant factors thatthe experimenters dreamedup.As I have described it, the

process that leads tojudgment by availabilityappearstoinvolveacomplexchain of reasoning. Thesubjects have an experience

of diminishing fluency asthey produce instances.Theyevidently have expectationsabout the rate at whichfluency decreases, and thoseexpectations are wrong: thedifficulty of coming up withnewinstances increasesmorerapidlythantheyexpect.Itistheunexpectedly lowfluencythat causes people whowereasked for twelve instances todescribe themselves asunassertive. When the

surprise is eliminated, lowfluency no longer influencesthe judgment. The processappears to consist of asophisticatedriethesubjsetofinferences. Is the automaticSystem1capableofit?Theansweristhatinfactno

complexreasoningisneeded.Among the basic features ofSystem 1 is its ability to setexpectations and to besurprised when theseexpectationsareviolated.The

systemalsoretrievespossiblecauses of a surprise, usuallyby finding a possible causeamong recent surprises.Furthermore, System 2 canreset the expectations ofSystem 1 on the fly, so thataneventthatwouldnormallybe surprising is now almostnormal.Supposeyouaretoldthat the three-year-old boywho lives next doorfrequentlywears a top hat inhis stroller. You will be far

less surprised when youactually seehimwithhis tophatthanyouwouldhavebeenwithout the warning. InSchwarz’s experiment, thebackground music has beenmentionedasapossiblecauseof retrieval problems. Thedifficultyofretrievingtwelveinstances is no longer asurprise and therefore is lesslikely to be evoked by thetaskofjudgingassertiveness.Schwarz and his colleagues

discovered that people whoarepersonallyinvolvedinthejudgment are more likely toconsider the number ofinstances they retrieve frommemoryand less likely togoby fluency. They recruitedtwo groups of students for astudy of risks to cardiachealth. Half the students hada family history of cardiacdiseaseandwereexpected totake the task more seriouslythan the others, who had no

such history. All were askedto recall either three or eightbehaviorsintheirroutinethatcould affect their cardiachealth (some were asked forrisky behaviors, others forprotective behaviors).Students with no familyhistory of heart disease werecasual about the task andfollowed the availabilityheuristic.Studentswhofoundit difficult to find eightinstances of risky behavior

felt themselves relativelysafe,andthosewhostruggledto retrieve examples of safebehaviors felt themselves atrisk. The students with afamily history of heartdisease showed the oppositepattern—they felt saferwhentheyretrievedmanyinstancesof safe behavior and feltgreater danger when theyretrieved many instances ofrisky behavior. They werealso more likely to feel that

their future behavior wouldbeaffectedbytheexperienceofevaluatingtheirrisk.The conclusion is that the

ease with which instancescome to mind is a System 1heuristic, which is replacedby a focus on content whenSystem 2 is more engaged.Multiple lines of evidenceconverge on the conclusionthat people who letthemselves be guided bySystem 1 are more strongly

susceptible to availabilitybiases thanotherswhoare ina state of higher vigilance.The following are someconditions in which people“go with the flow” and areaffected more strongly byease of retrieval than by thecontenttheyretrieved:

when they are engagedin another effortful taskatthesametime

when theyare inagoodmood because they justthought of a happyepisodeintheirlifeif they score low on adepressionscaleif they areknowledgeable noviceson the topic of the task,in contrast to trueexpertswhentheyscorehighona scale of faith inintuition

if they are (or aremadetofeel)powerful

I find the last finding

particularly intriguing. Theauthors introduce theirarticlewithafamousquote:“Idon’tspend a lot of time takingpollsaround theworld to tellme what I think is the rightway to act. I’ve just got toknowhowIfeel”(Georgeeethe w W. Bush, November

2002). They go on to showthat reliance on intuition isonly in part a personalitytrait. Merely remindingpeople of a time when theyhad power increases theirapparent trust in their ownintuition.

SpeakingofAvailability

“Because of thecoincidence of two

planes crashing lastmonth, she now prefersto take the train. That’ssilly. The risk hasn’treally changed; it is anavailabilitybias.”

“He underestimates therisks of indoor pollutionbecause there are fewmedia stories on them.That’s an availabilityeffect.Heshouldlookat

thestatistics.”

“She has been watchingtoo many spy moviesrecently, so she’s seeingconspiracieseverywhere.”

“The CEO has hadseveral successes in arow, so failure doesn’tcomeeasilytohermind.The availability bias is

making heroverconfident.”

P

Availability,Emotion,andRisk

Studentsofriskwerequicktosee that the idea ofavailability was relevant totheir concerns. Even beforeour work was published, theeconomist HowardKunreuther,whowas then inthe early stages of a careerthat he has devoted to the

study of risk and insurance,noticed that availabilityeffects help explain thepatternof insurancepurchaseand protective action afterdisasters. Victims and nearvictims are very concernedafter a disaster. After eachsignificant earthquake,Californians are for a whilediligent in purchasinginsurance and adoptingmeasures of protection andmitigation. They tie down

their boiler to reduce quakedamage, seal their basementdoors against floods, andmaintain emergency suppliesin good order. However, thememoriesof thedisasterdimover time, and so do worryand diligence. The dynamicsof memory help explain therecurrent cycles of disaster,concern, and growingcomplacencythatarefamiliarto students of large-scaleemergencies.

Kunreuther also observedthat protective actions,whether by individuals orgovernments, are usuallydesignedtobeadequatetotheworst disaster actuallyexperienced. As long ago aspharaonic Egypt, societieshave tracked the high-watermark of rivers thatperiodically flood—and havealways prepared accordingly,apparently assuming thatfloods will not rise higher

than the existing high-watermark. Images of a worsedisasterdonotcomeeasilytomind.

AvailabilityandAffect

The most influential studiesof availability biases werecarried out by our friends inEugene, where Paul Slovicandhis longtimecollaboratorSarah Lichtenstein were

joined by our former studentBaruch Fischhoff. Theycarried out groundbreakingresearch on publicperceptions of risks,including a survey that hasbecomethestandardexampleof an availability bias. Theyasked participants in theirsurvey to siIs th t#considerpairs of causes of death:diabetesandasthma,orstrokeand accidents. For each pair,the subjects indicated the

more frequent cause andestimatedtheratioofthetwofrequencies. The judgmentswere compared to healthstatisticsofthetime.Here’sasampleoftheirfindings:

Strokes cause almosttwiceasmanydeathsasall accidents combined,but 80% of respondentsjudged accidental deathtobemorelikely.

Tornadoes were seen asmore frequent killersthan asthma, althoughthe lattercause20 timesmoredeaths.Death by lightning wasjudged less likely thandeath from botulismeven though it is 52timesmorefrequent.Death by disease is 18times as likely asaccidentaldeath,but thetwo were judged about

equallylikely.Death by accidents wasjudged to be more than300 times more likelythan death by diabetes,butthetrueratiois1:4.

The lesson isclear:estimatesofcausesofdeatharewarpedby media coverage. Thecoverage is itself biasedtoward novelty andpoignancy.Themediadonot

just shapewhat the public isinterested in, but also areshaped by it. Editors cannotignore the public’s demandsthat certain topics andviewpoints receive extensivecoverage. Unusual events(such as botulism) attractdisproportionateattentionandareconsequentlyperceivedasless unusual than they reallyare.Theworldinourheadsisnot a precise replica ofreality; our expectations

aboutthefrequencyofeventsare distorted by theprevalence and emotionalintensity of the messages towhichweareexposed.The estimates of causes of

death are an almost directrepresentation of theactivation of ideas inassociativememory,andareagoodexampleofsubstitution.ButSlovicandhiscolleagueswere led to a deeper insight:they saw that the ease with

which ideas of various riskscome to mind and theemotional reactions to theserisks are inextricably linked.Frightening thoughts andimages occur to us withparticular ease, and thoughtsof danger that are fluent andvividexacerbatefear.Asmentionedearlier,Slovic

eventually developed thenotion of an affect heuristic,in which people makejudgments and decisions by

consultingtheiremotions:DoI like it? Do I hate it? HowstronglydoIfeelaboutit?Inmanydomainsof life,Slovicsaid, people form opinionsand make choices thatdirectlyexpress their feelingsand their basic tendency toapproach or avoid, oftenwithoutknowingthattheyaredoingso.Theaffectheuristicis an instanceof substitution,in which the answer to aneasyquestion (Howdo I feel

aboutit?)servesasananswerto a much harder question(What do I think about it?).Slovic and his colleaguesrelated their views to thework of the neuroscientistAntonio Damasio, who hadproposed that people’semotional evaluations ofoutcomes, and the bodilystates and the approach andavoidance tendenciesassociatedwiththem,allplaya central role in guiding

decision making. Damasioand his colleagues haveobserved that peoplewho donot display the appropriateemotions before they decide,sometimes because of braindamage, also have animpairedabilitytomakegooddecisions. An inability to beguidedbya“healthyfear”ofbad consequences is adisastrousflaw.In a compelling

demonstration of the

workings of the affectheuristic, Slovic’s researchteamsurveyedopinionsaboutvarious technologies,including water fluoridation,chemical plants, foodpreservatives, and cars, andaskedtheirrespondentstolistboththebenefits>The best part of the

experiment came next. Aftercompleting the initial survey,the respondents read briefpassages with arguments in

favorofvarioustechnologies.Some were given argumentsthatfocusedonthenumerousbenefits of a technology;others, arguments thatstressed the low risks. Thesemessages were effective inchanging the emotionalappeal of the technologies.The striking findingwas thatpeople who had received amessageextollingthebenefitsofa technologyalsochangedtheir beliefs about its risks.

Although they had receivedno relevant evidence, thetechnology they now likedmore than before was alsoperceived as less risky.Similarly, respondents whowere told only that the risksof a technology were milddeveloped a more favorableview of its benefits. Theimplication is clear: as thepsychologist Jonathan Haidtsaid in another context, “Theemotional tail wags the

rational dog.” The affectheuristic simplifies our livesby creating a world that ismuchtidierthanreality.Goodtechnologies have few costsin the imaginary world weinhabit, bad technologieshave no benefits, and alldecisionsareeasy.Intherealworld, of course, we oftenface painful tradeoffsbetweenbenefitsandcosts.

ThePublicandthe

ExpertsPaul Slovic probably knowsmore about the peculiaritiesof human judgment of riskthananyotherindividual.Hiswork offers a picture of Mr.and Ms. Citizen that is farfrom flattering: guided byemotion rather than byreason, easily swayed bytrivial details, andinadequately sensitive todifferences between low and

negligibly low probabilities.Slovic has also studiedexperts, who are clearlysuperior in dealing withnumbers and amounts.Experts show many of thesame biases as the rest of usin attenuated form, but oftentheir judgments andpreferences about risksdiverge from those of otherpeople.Differencesbetweenexperts

and the public are explained

in part by biases in layjudgments, but Slovic drawsattention to situations inwhichthedifferencesreflectagenuineconflictofvalues.Hepoints out that experts oftenmeasure risks by the numberof lives (or life-years) lost,while the public draws finerdistinctions, for examplebetween “good deaths” and“bad deaths,” or betweenrandom accidental fatalitiesand deaths that occur in the

course of voluntary activitiessuch as skiing. Theselegitimate distinctions areoftenignoredinstatisticsthatmerely count cases. Slovicargues from suchobservations that the publichas a richer conception ofrisks than the experts do.Consequently, he stronglyresists the view that theexperts should rule, and thattheir opinions should beaccepted without question

when they conflict with theopinions andwishes of othercitizens. When experts andthe public disagree on theirpriorities,hesays,“Eachsidemuiesst respect the insightsandintelligenceoftheother.”In his desire to wrest sole

control of risk policy fromexperts, Slovic haschallenged the foundation oftheir expertise: the idea thatriskisobjective.

“Risk” does not exist“out there,” independentofourmindsandculture,waiting to be measured.Human beings haveinvented the concept of“risk” to help themunderstand and copewith the dangers anduncertainties of life.Although these dangersarereal,thereisnosuchthing as “real risk” or“objectiverisk.”

To illustrate his claim,

Slovic lists nine ways ofdefining the mortality riskassociatedwiththereleaseofa toxic material into the air,ranging from “death permillionpeople” to“deathpermillion dollars of productproduced.” His point is thatthe evaluation of the riskdepends on the choice of ameasure—with the obviouspossibility that the choice

may have been guided by apreference for one outcomeor another. He goes on toconcludethat“definingriskisthus an exercise in power.”You might not have guessedthat one can get to suchthorny policy issues fromexperimental studies of thepsychology of judgment!However,policyisultimatelyaboutpeople,whattheywantand what is best for them.Every policy question

involves assumptions abouthuman nature, in particularabout thechoices thatpeoplemay make and theconsequencesoftheirchoicesfor themselves and forsociety.Another scholar and friend

whom I greatly admire,CassSunstein, disagrees sharplywith Slovic’s stance on thedifferentviewsofexpertsandcitizens, anddefends the roleof experts as a bulwark

against “populist” excesses.Sunstein is one of theforemostlegalscholarsintheUnitedStates,andshareswithotherleadersofhisprofessionthe attribute of intellectualfearlessness. He knows hecan master any body ofknowledge quickly andthoroughly, and he hasmastered many, includingboth the psychology ofjudgment and choice andissues of regulation and risk

policy. His view is that theexisting system of regulationintheUnitedStatesdisplaysaverypoorsettingofpriorities,which reflects reaction topublic pressures more thancarefulobjectiveanalysis.Hestarts from the position thatrisk regulation andgovernment intervention toreducerisksshouldbeguidedbyrationalweightingofcostsand benefits, and that thenatural units for this analysis

arethenumberoflivessaved(or perhaps the number oflife-years saved,which givesmore weight to saving theyoung) and thedollar cost totheeconomy.Poorregulationis wasteful of lives andmoney,bothofwhichcanbemeasured objectively.Sunstein has not beenpersuaded by Slovic’sargument that risk and itsmeasurement is subjective.Many aspects of risk

assessmentaredebatable,buthehasfaithintheobjectivitythat may be achieved byscience,expertise,andcarefuldeliberation.Sunstein came to believe

that biased reactions to risksare an important source oferratic and misplacedpriorities in public policy.Lawmakers and regulatorsmay be overly responsive tothe irrational concerns ofcitizens, both because of

political sensitivity andbecausetheyarepronetothesame cognitive biases asothercitizens.Sunsteinandacollaborator,

the jurist Timur Kuran,invented a name for themechanism through whichbiases flow into policy: theavailability cascade. Theycomment that in the socialcontext, “all heuristics areequal,butavailabilityismoreequal than the others.” They

haveinmindanexpandUnednotion of the heuristic, inwhich availability provides aheuristic for judgments otherthan frequency. In particular,the importance of an idea isoften judged by the fluency(and emotional charge) withwhich that idea comes tomind.An availability cascade is a

self-sustaining chain ofevents,whichmaystart frommedia reports of a relatively

minor event and lead up topublic panic and large-scalegovernment action. On someoccasions, a media storyabout a risk catches theattention of a segment of thepublic, which becomesaroused and worried. Thisemotionalreactionbecomesastory in itself, promptingadditional coverage in themedia, which in turnproducesgreater concernandinvolvement. The cycle is

sometimes sped alongdeliberately by “availabilityentrepreneurs,”individualsororganizations who work toensure a continuous flow ofworryingnews.Thedangerisincreasingly exaggerated asthe media compete forattention-grabbing headlines.Scientists and otherswho trytodampentheincreasingfearand revulsion attract littleattention, most of it hostile:anyone who claims that the

danger is overstated issuspected of associationwitha “heinous cover-up.” Theissue becomes politicallyimportant because it is oneveryone’s mind, and theresponse of the politicalsystem is guided by theintensity of public sentiment.The availability cascade hasnow reset priorities. Otherrisks, and other ways thatresourcescouldbeappliedforthe public good, all have

fadedintothebackground.KuranandSunsteinfocused

ontwoexamplesthatarestillcontroversial:theLoveCanalaffair and the so-called Alarscare. In Love Canal, buriedtoxic waste was exposedduringarainyseasonin1979,causing contamination of thewater well beyond standardlimits,aswellasafoulsmell.The residents of thecommunity were angry andfrightened, and one of them,

Lois Gibbs, was particularlyactiveinanattempttosustaininterest in the problem. Theavailability cascade unfoldedaccording to the standardscript.At its peak thereweredaily stories about LoveCanal,scientistsattemptingtoclaim that the dangers wereoverstated were ignored orshouted down, ABC Newsaired a program titled TheKilling Ground, and emptybaby-size coffins were

paraded in front of thelegislature.Alargenumberofresidents were relocated atgovernment expense, and thecontroloftoxicwastebecamethemajorenvironmentalissueof the 1980s. The legislationthatmandated the cleanup oftoxic sites, called CERCLA,established a Superfund andis considered a significantachievement ofenvironmental legislation. Itwasalsoexpensive,andsome

have claimed that the sameamountofmoneycouldhavesaved many more lives if ithad been directed to otherpriorities. Opinions aboutwhat actually happened atLove Canal are still sharplydivided, and claims of actualdamage to health appear notto have been substantiated.KuranandSunsteinwroteupthe Love Canal story almostas a pseudo-event, while onthe other side of the debate,

environmentalists still speakofthe“LoveCanaldisaster.”Opinionsarealsodividedon

the second example KuranandSunsteinusedtoillustratetheir concept of anavailability cascade, the Alarincident, known to detractorsof environmental concernsasthe“Alarscare”of1989.Alaris a chemical that wassprayedonapples to regulatetheir growth and improvetheir appearance. The scare

began with press stories thatthechemical,whenconsumedin gigantic doses, causedcancerous tumors in rats andmice. The storiesunderstandablyfrightenedthepublic, and those fearsencouraged more mediacoverage, the basicmechanism of an availabilitycascade.Thetopicdominatedthe news and produceddramatic media events suchasthetestimonyoftheactress

Meryl Streep beforeCongress.Theapple industrysu ofstained large losses asapples and apple productsbecameobjectsoffear.Kuranand Sunstein quote a citizenwhocalledintoask“whetherit was safer to pour applejuice down the drain or totake it to a toxic wastedump.” The manufacturerwithdrewtheproductandtheFDA banned it. Subsequentresearch confirmed that the

substance might pose a verysmall risk as a possiblecarcinogen, but the Alarincident was certainly anenormous overreaction to aminorproblem.Theneteffectof the incident on publichealth was probablydetrimental because fewergoodappleswereconsumed.The Alar tale illustrates a

basic limitation in the abilityof our mind to deal withsmall risks: we either ignore

them altogether or give themfar too much weight—nothing in between. Everyparent who has stayed upwaiting for a teenagedaughter who is late from aparty will recognize thefeeling. You may know thatthere is really (almost)nothing to worry about, butyou cannot help images ofdisasterfromcomingtomind.As Slovic has argued, theamount of concern is not

adequately sensitive to theprobability of harm; you areimagining the numerator—the tragic story you saw onthe news—and not thinkingabout the denominator.Sunstein has coined thephrase “probability neglect”to describe the pattern. Thecombination of probabilityneglect with the socialmechanisms of availabilitycascades inevitably leads togross exaggeration of minor

threats, sometimes withimportantconsequences.In today’s world, terrorists

are the most significantpractitioners of the art ofinducing availabilitycascades.Withafewhorribleexceptions such as 9/11, thenumber of casualties fromterror attacks is very smallrelative to other causes ofdeath. Even in countries thathavebeentargetsofintensiveterror campaigns, such as

Israel, theweekly number ofcasualties almost never cameclosetothenumberof trafficdeaths. The difference is inthe availability of the tworisks, the ease and thefrequency with which theycome to mind. Gruesomeimages, endlessly repeated inthemedia, cause everyone tobe on edge.As I know fromexperience, it is difficult toreason oneself into a state ofcomplete calm. Terrorism

speaksdirectlytoSystem1.Where do I come down in

the debate between myfriends?Availabilitycascadesarerealandtheyundoubtedlydistort priorities in theallocationofpublicresources.Cass Sunstein would seekmechanisms that insulatedecision makers from publicpressures, letting theallocation of resources bedetermined by impartialexperts who have a broad

view of all risks and of theresources available to reducethem. Paul Slovic trusts theexperts much less and thepublic somewhat more thanSunstein does, and he pointsoutthatinsulatingtheexpertsfrom the emotions of thepublic produces policies thatthe public will reject—animpossible situation in ademocracy. Both areeminently sensible, and Iagreewithboth.

I share Sunstein’sdiscomfortwith the influenceof irrational fears andavailability cascades onpublicpolicyinthedomainofrisk. However, I also shareSlovic’s belief thatwidespreadfears,eveniftheyare unreasonable, should notbe ignoredbypolicymakers.Rationalornot,fearispainfuland debilitating, and policymakers must endeavor toprotect the public from fear,

notonlyfromrealdangers.Slovic rightly stresses the

resistanceofthepublictotheideaofdecisionsbeingmadeby unelected andunaccountable experts.Furthermore, availabilitycascades may have a long-term benefit by callingattention to classes of risksand by increasing the overallsize of the risk-reductionbudget. The Love Canalincident may have caused

excessive resources to beallocated to the managementof toxic betwaste, but it alsohad a more general effect inraising the priority level ofenvironmental concerns.Democracy is inevitablymessy, in part because theavailability and affectheuristics that guide citizens’beliefs and attitudes areinevitablybiased,eveniftheygenerally point in the rightdirection. Psychology should

inform the design of riskpolicies that combine theexperts’ knowledge with thepublic’s emotions andintuitions.

SpeakingofAvailabilityCascades

“She’s raving about aninnovationthathaslargebenefits and no costs. Isuspect the affectheuristic.”

“This is an availabilitycascade: anonevent thatis inflated by the mediaand the public until itfillsourTVscreensandbecomes all anyone istalkingabout.”

P

TomW’sSpecialty

Have a look at a simplepuzzle:

Tom W is a graduatestudent at the mainuniversity in your state.Please rank thefollowing nine fields ofgraduate specializationinorderofthelikelihood

that Tom W is now astudent in each of thesefields. Use 1 for themost likely, 9 for theleastlikely.

businessadministrationcomputerscienceengineeringhumanitiesandeducationlawmedicinelibraryscience

physicalandlifesciencessocial science and socialwork

This question is easy, and

you knew immediately thattherelativesizeofenrollmentin the different fields is thekey to a solution. So far asyou know, Tom W waspicked at random from thegraduate students at theuniversity, like a singlemarble drawn from an urn.

Todecidewhetheramarbleismore likely to be red orgreen,youneedtoknowhowmany marbles of each colorthere are in the urn. Theproportion of marbles of aparticular kind is called abaserate.Similarly, thebaserate of humanities andeducation in this problem isthe proportion of students ofthat field among all thegraduate students. In theabsence of specific

information about Tom W,youwillgoby thebase ratesand guess that he is morelikely to be enrolled inhumanities and educationthan in computer science orlibraryscience,because thereare more students overall inthe humanities and educationthan in the other two fields.Using base-rate informationistheobviousmovewhennootherinformationisprovided.

Next comes a task that hasnothingtodowithbaserates.

The following is apersonality sketch ofTom W written duringTom’s senior year inhigh school by apsychologist, on thebasis of psychologicaltests of uncertainvalidity:

Tom W is of highintelligence, althoughlackingintruecreativity.He has a need for orderand clarity, and for neatand tidy systems inwhich every detail findsitsappropriateplace.Hiswritingisratherdullandmechanical,occasionallyenlivened by somewhatcorny puns and flashesof imagination of thesci-fi type. He has a

strong drive forcompetence. He seemsto have little feel andlittle sympathy for otherpeople, and does notenjoy interacting withothers. Self-centered, henonetheless has a deepmoralsense.

Nowplease takea sheetof paper and rank thenine fields of

specialization listedbelow by how similarthe description of TomW is to the typicalgraduate student in eachof the following fields.Use1forthemostlikelyand9fortheleastlikely.

Youwillgetmoreoutofthe

chapter ifyougivethetaskaquick try; reading the reporton Tom W is necessary tomake your judgments about

the various graduatespecialties.This question too is

straightforward. It requiresyou to retrieve,orperhaps toconstruct, a stereotype ofgraduate students in thedifferent fields. When theexperiment was firstconducted,intheearly1970s,the average ordering was asfollows. Yours is probablynotverydifferent:

1. computerscience2. engineering3. businessadministration4. physical and life

sciences5. libraryscience6. law7. medicine8. humanities and

education9. socialscienceandsocial

work

You probably rankedcomputer science among thebestfittingbecauseofhintsofnerdiness (“corny puns”). Infact, the description of TomW was written to fit thatstereotype. Another specialtythatmostpeople rankedhighisengineering(“neatandtidysystems”). You probablythought thatTomW is not agood fit with your idea ofsocial science and socialwork (“little feel and little

sympathy for other people”).Professional stereotypesappear to have changed littleinthenearlyfortyyearssinceI designed the description ofTomW.Thetaskofrankingthenine

careers is complex andcertainly requires thediscipline and sequentialorganization of which onlySystem 2 is capable.However,thehintsplantedinthe description (corny puns

and others) were intended toactivateanassociationwithastereotype, an automaticactivityofSystem1.The instructions for this

similarity task required acomparisonofthedescriptionofTomW to the stereotypesof the various fields ofspecialization. For thepurposesoftv>If you examine Tom W

again,youwillseethatheisagood fit to stereotypes of

some small groups ofstudents (computer scientists,librarians, engineers) and amuchpoorerfit to thelargestgroups (humanities andeducation, social science andsocial work). Indeed, theparticipants almost alwaysranked the two largest fieldsvery low. Tom W wasintentionally designed as an“anti-base-rate” character, agood fit to small fieldsandapoorfittothemostpopulated

specialties.

PredictingbyRepresentativeness

Thethirdtaskinthesequencewas administered to graduatestudentsinpsychology,anditis the critical one: rank thefields of specialization inorder of the likelihood thatTom W is now a graduatestudent in each of thesefields. The members of this

prediction group knew therelevant statistical facts: theywere familiar with the baserates of the different fields,andtheyknewthatthesourceof TomW’s description wasnot highly trustworthy.However, we expected themto focus exclusively on thesimilarity of the descriptionto thestereotypes—wecalledit representativeness—ignoring both the base ratesand the doubts about the

veracity of the description.They would then rank thesmall specialty—computerscience—as highly probable,becausethatoutcomegetsthehighest representativenessscore.Amos and I worked hard

during the year we spent inEugene, and I sometimesstayed in the office throughthenight.Oneofmytasksforsuchanightwastomakeupadescription that would pit

representativeness and baseratesagainsteachother.TomW was the result of myefforts, and I completed thedescription in the earlymorning hours. The firstperson who showed up towork that morning was ourcolleague and friend RobynDawes, who was both asophisticated statistician andaskepticaboutthevalidityofintuitive judgment. If anyonewould see the relevance of

thebaserate,itwouldhavetobe Robyn. I called Robynover,gavehimthequestionIhadjusttyped,andaskedhimtoguessTomW’sprofession.Istillrememberhisslysmileas he said tentatively,“computer scientist?” Thatwas a happy moment—eventhe mighty had fallen. Ofcourse, Robyn immediatelyrecognized his mistake assoon as I mentioned “baserate,” but he had not

spontaneously thought of it.Althoughheknewasmuchasanyoneabouttheroleofbaserates in prediction, heneglected them whenpresentedwiththedescriptionofanindividual’spersonality.Asexpected,he substitutedajudgment ofrepresentativeness for theprobability he was asked toassess.Amos and I then collected

answers to thesamequestion

from114graduatestudentsinpsychology at three majoruniversities, allofwhomhadtaken several courses instatistics. They did notdisappoint us.Their rankingsof the nine fields byprobability did not differfrom ratings by similarity tothe stereotype. Substitutionwasperfectinthiscase:therewas no indication that theparticipantsdidanythingelsebut judge representativeness.

The question aboutprobability (likelihood) wasdifficult, but the questionabout similarity was easier,and it was answered instead.This is a serious mistake,because judgments ofsimilarity and probak tbilityare not constrained by thesame logical rules. It isentirely acceptable forjudgments of similarity to beunaffected by base rates andalsobythepossibilitythatthe

description was inaccurate,butanyonewho ignoresbaserates and the quality ofevidence in probabilityassessments will certainlymakemistakes.Theconcept“theprobability

thatTomWstudiescomputerscience” is not a simple one.Logicians and statisticiansdisagree about its meaning,andsomewouldsayithasnomeaning at all. For manyexperts it is a measure of

subjective degree of belief.There are some events youare sureof, forexample, thatthesunrosethismorning,andothers you considerimpossible, such as thePacific Ocean freezing all atonce. Then there are manyevents, such as your next-door neighbor being acomputer scientist, to whichyou assign an intermediatedegree of belief—which isyourprobabilityofthatevent.

Logicians and statisticianshave developed competingdefinitions of probability, allvery precise. For laypeople,however, probability (asynonym of likelihood ineveryday language) is avague notion, related touncertainty, propensity,plausibility,andsurprise.Thevagueness isnotparticular tothis concept, nor is itespecially troublesome. Weknow,moreor less,whatwe

mean when we use a wordsuch asdemocracy or beautyandthepeoplewearetalkingto understand, more or less,what we intended to say. Inall the years I spent askingquestions about theprobability of events, no oneeverraisedahandtoaskme,“Sir, what do you mean byprobability?” as they wouldhavedoneifIhadaskedthemto assess a strange conceptsuchasglobability.Everyone

actedas if theyknewhow toanswer my questions,although we all understoodthat itwouldbeunfair toaskthem for an explanation ofwhatthewordmeans.People who are asked to

assess probability are notstumped,becausetheydonottry to judge probability asstatisticians and philosophersuse the word. A questionabout probability orlikelihood activates a mental

shotgun, evoking answers toeasier questions. One of theeasy answers is an automaticassessment ofrepresentativeness—routinein understanding language.The (false) statement that“Elvis Presley’s parentswantedhimtobeadentist”ismildly funny because thediscrepancy between theimages of Presley and adentist is detectedautomatically. System 1

generates an impression ofsimilarity without intendingto do so. Therepresentativenessheuristicisinvolvedwhen someone says“She will win the election;you can see she is awinner”or “He won’t go far as anacademic; toomany tattoos.”Werelyonrepresentativenesswhen we judge the potentialleadership of a candidate forofficebytheshapeofhischinor the forcefulness of his

speeches.Although it is common,

prediction byrepresentativeness is notstatistically optimal. MichaelLewis’s bestsellingMoneyballisastoryabouttheinefficiency of this mode ofprediction. Professionalbaseball scouts traditionallyforecast the success ofpossible players in part bytheirbuildandlook.Theheroof Lewis’s book is Billy

Beane, the manager of theOakland A’s, who made theunpopular decision tooverrule his scouts and toselectplayersbythestatisticsof past performance. Theplayers the A’s picked wereinexpensive, because otherteams had rejected them fornot looking the part. Theteamsoonachievedexcellentresultsatlowcost.

TheSinsof

RepresentativenessJudging probability byalsrepresentativeness hasimportant virtues: theintuitive impressions that itproduces are often—indeed,usually—more accurate thanchanceguesseswouldbe.

On most occasions,people who act friendlyareinfactfriendly.

A professional athletewhoisverytallandthinis much more likely toplay basketball thanfootball.People with a PhD aremore likely to subscribetoThe New York Timesthan people who endedtheireducationafterhighschool.Young men are morelikely than elderlywomen to drive

aggressively.

Inallthesecasesandinmanyothers, there is some truth tothe stereotypes that governjudgments ofrepresentativeness, andpredictions that follow thisheuristicmay be accurate. Inother situations, thestereotypes are false and therepresentativeness heuristicwill mislead, especially if it

causespeopletoneglectbase-rateinformationthatpointsinanotherdirection.Evenwhenthe heuristic has somevalidity,exclusiverelianceonit is associated with gravesinsagainststatisticallogic.One sin of

representativeness is anexcessive willingness topredict the occurrence ofunlikely (low base-rate)events. Here is an example:youseeapersonreadingThe

NewYorkTimes on theNewYork subway. Which of thefollowingisabetterbetaboutthereadingstranger?

ShehasaPhD.She does not have acollegedegree.

Representativenesswouldtellyou to bet on the PhD, butthis is not necessarily wise.You should seriouslyconsider the second

alternative, because manymorenongraduatesthanPhDsride in New York subways.And if you must guesswhether a woman who isdescribed as “a shy poetrylover” studies Chineseliterature or businessadministration, you shouldoptforthelatteroption.Evenif every female student ofChinese literature is shy andloves poetry, it is almostcertain that there are more

bashful poetry lovers in themuch larger population ofbusinessstudents.People without training in

statistics are quite capable ofusingbaseratesinpredictionsundersomeconditions.Inthefirst version of the Tom Wproblem, which provides nodetails about him, it isobvious to everyone that theprobabilityofTomW’sbeinginaparticular field is simplythe base rate frequency of

enrollment in that field.However, concern for baserates evidently disappears assoonasTomW’spersonalityisdescribed.Amos and I originally

believed, on the basis of ourearly evidence, that base-rateinformation will always beneglected when informationabout the specific instance isavailable,but that conclusionwastoostrong.Psychologistshave conducted many

experiments in which base-rate information is explicitlyprovided as part of theproblem, and many of theparticipantsareinfluencedbythosebaserates,althoughtheinformation about theindividual case is almostalways weighted more thanmere statistics. NorbertSchwarz and his colleaguesshowed that instructingpeople to “think like astatistician”enhanced theuse

of base-rate information,whiletheinstructionto“thinklike a clinician” had theoppositeeffect.An experiment that was

conducted a few years agowith Harvard undergradutoates yielded a finding thatsurprised me: enhancedactivationofSystem2causeda significant improvement ofpredictive accuracy in theTom W problem. Theexperiment combined the old

problem with a modernvariation of cognitivefluency. Half the studentswere told to puff out theircheeksduring the task,whiletheothersweretoldtofrown.Frowning, as we have seen,generally increases thevigilance of System 2 andreduces both overconfidenceand the reliance on intuition.The students who puffed outtheir cheeks (an emotionallyneutral expression) replicated

the original results: theyrelied exclusively onrepresentativeness andignoredthebaserates.Astheauthors had predicted,however, the frowners didshow some sensitivity to thebase rates. This is aninstructivefinding.

When an incorrect intuitivejudgment is made, System 1andSystem2shouldbothbe

indicted. System 1 suggestedthe incorrect intuition, andSystem 2 endorsed it andexpressed it in a judgment.However, there are twopossible reasons for thefailure of System 2—ignorance or laziness. Somepeople ignore base ratesbecause they believe them tobe irrelevant in the presenceof individual information.Others make the samemistake because they are not

focused on the task. Iffrowningmakesadifference,laziness seems to be theproper explanation of base-rate neglect, at least amongHarvard undergrads. TheirSystem 2 “knows” that baserates are relevant even whenthey are not explicitlymentioned, but applies thatknowledge only when itinvests special effort in thetask.The second sin of

representativeness isinsensitivity to the quality ofevidence. Recall the rule ofSystem 1: WYSIATI. In theTom W example, whatactivates your associativemachinery is adescriptionofTom,whichmay ormay notbeanaccurateportrayal.Thestatement that Tom W “haslittle feel and little sympathyfor people” was probablyenough toconvinceyou (andmostother readers) thathe is

very unlikely to be a studentof social science or socialwork.Butyouwereexplicitlytold that the descriptionshouldnotbetrusted!You surely understand in

principle that worthlessinformation should not betreated differently from acompletelackofinformation,butWYSIATImakesitverydifficult to apply thatprinciple. Unless you decideimmediately to reject

evidence (for example, bydeterminingthatyoureceiveditfromaliar),yourSystem1willautomaticallyprocesstheinformation available as if itwere true.There is one thingyou can do when you havedoubts about the quality ofthe evidence: let yourjudgmentsofprobability stayclose to the base rate. Don’texpect this exercise ofdiscipline to be easy—itrequiresasignificanteffortof

self-monitoring and self-control.The correct answer to the

Tom W puzzle is that youshouldstayveryclosetoyourprior beliefs, slightlyreducing the initially highprobabilities of well-populated fields (humanitiesand education; social scienceandsocialwork)and slightlyraising the low probabilitiesof rare specialties (libraryscience, computer science).

You are not exactly whereyou would be if you hadknown nothing at all aboutTom W, but the littleevidence you have is nottrustworthy, so thebase ratesshould dominate yourestimates.

HowtoDisciplineIntuition

Your probability that it willrain tomorrow is your

subjective degree of belief,but you should not letyourself believe whatevercomes to your mind. To beuseful,yourbeliefsshouldbeconstrained by the logic ofprobability.So ifyoubelievethat there is a 40% chanceplethat it will rain sometimetomorrow, you must alsobelieve that there is a 60%chance it will not raintomorrow, and you must notbelieve that there is a 50%

chance that it will raintomorrow morning. And ifyou believe that there is a30%chance thatcandidateXwillbeelectedpresident,andan80%chancethathewillbereelected if he wins the firsttime, then you must believethat the chances that he willbeelected twice ina roware24%.The relevant “rules” for

cases such as the Tom Wproblem are provided by

Bayesian statistics. Thisinfluential modern approachtostatistics isnamedafteranEnglish minister of theeighteenth century, theReverend Thomas Bayes,who is creditedwith the firstmajor contribution to a largeproblem: the logic of howpeople should change theirmindinthelightofevidence.Bayes’s rule specifies howpriorbeliefs(intheexamplesof this chapter, base rates)

should be combinedwith thediagnosticityof theevidence,the degree towhich it favorsthe hypothesis over thealternative. For example, ifyou believe that 3% ofgraduatestudentsareenrolledincomputerscience(thebaserate), and you also believethatthedescriptionofTomWis 4 times more likely for agraduate student in that fieldthan in other fields, thenBayes’s rule says you must

believe that the probabilitythat Tom W is a computerscientist is now 11%. If thebase rate had been 80%, thenew degree of belief wouldbe94.1%.Andsoon.The mathematical details

are not relevant in this book.Therearetwoideastokeepinmind about Bayesianreasoningandhowwetendtomess it up. The first is thatbaseratesmatter,eveninthepresence of evidence about

thecaseathand.Thisisoftennot intuitively obvious. Thesecond is that intuitiveimpressions of thediagnosticity of evidence areoften exaggerated. Thecombination of WY SIATIand associative coherencetends to make us believe inthe stories we spin forourselves. The essential keysto disciplined Bayesianreasoning can be simplysummarized:

Anchor your judgmentof the probability of anoutcome on a plausiblebaserate.Question thediagnosticity of yourevidence.

Both ideas arestraightforward. It came as ashock to me when I realized

that I was never taught howto implement them, and thateven now I find it unnaturaltodoso.

SpeakingofRepresentativeness

“The lawn is welltrimmed,thereceptionistlookscompetent,andthefurniture is attractive,but this doesn’t mean itis a well-managed

company. I hope theboard does not go byrepresentativeness.”

“Thisstart-uplooksasifit could not fail, but thebase rate of success intheindustryisextremelylow. How do we knowthiscaseisdifferent?”

“They keep making thesamemistake:predicting

rare events from weakevidence. When theevidence is weak, oneshould stick with thebaserates.”

“I know this report isabsolutelydamning, anditmaybebasedonsolidevidence, but how sureare we?Wemust allowfor that uncertainty inourthinking.”

ht="5%">

P

Linda:LessIsMore

The best-known and mostcontroversial of ourexperiments involved afictitious lady called Linda.Amos and I made up theLinda problem to provideconclusive evidence of theroleofheuristicsinjudgmentand of their incompatibilitywith logic. This is how we

describedLinda:

Linda is thirty-oneyearsold, single, outspoken,and very bright. Shemajored in philosophy.As a student, she wasdeeply concerned withissues of discriminationand social justice, andalso participated inantinucleardemonstrations.

Theaudienceswhoheardthisdescription in the 1980salways laughed because theyimmediatelyknew thatLindahad attended the Universityof California at Berkeley,whichwasfamousatthetimefor its radical, politicallyengaged students. In one ofourexperimentswepresentedparticipants with a list ofeight possible scenarios forLinda. As in the Tom Wproblem, some ranked the

scenarios byrepresentativeness, others byprobability. The Lindaproblemissimilar,butwithatwist.

Linda is a teacher inelementaryschool.Linda works in abookstore and takesyogaclasses.Linda is active in thefeministmovement.Linda is a psychiatric

socialworker.LindaisamemberoftheLeague of WomenVoters.Lindaisabankteller.Linda is an insurancesalesperson.Linda is a bank tellerand is active in thefeministmovement.

Theproblemshowsitsageinseveralways. The League ofWomen Voters is no longer

as prominent as it was, andthe idea of a feminist“movement”soundsquaint,atestimonial to the change inthestatusofwomenover thelast thirty years. Even in theFacebook era, however, it isstill easy to guess the almostperfect consensus ofjudgments: Linda is a verygood fit for an activefeminist, a fairly good fit forsomeone who works in abookstore and takes yoga

classes—and a very poor fitfor a bank teller or aninsurancesalesperson.Now focus on the critical

items in the list: Does Lindalookmore like a bank teller,ormorelikeabanktellerwhois active in the feministmovement? Everyone agreesthat Linda fits the idea of a“feminist bank teller” betterthanshefitsthestereotypeofbanktellers.Thestereotypicalbank teller is not a feminist

activist,andaddingthatdetailtothedescriptionmakesforamorecoherentstory.The twist comes in the

judgments of likelihood,because there is a logicalrelation between the twoscenarios. Think in terms ofVenn diagrams. The set offeminist bank tellers iswholly included in the set ofbanktellers,aseveryfeministbank teller is0%"ustworaban0%" w a bank teller.

Therefore theprobability thatLindaisafeministbanktellermust be lower than theprobability of her being abankteller.Whenyouspecifya possible event in greaterdetail you can only lower itsprobability. The problemtherefore sets up a conflictbetween the intuition ofrepresentativeness and thelogicofprobability.Our initial experiment was

between-subjects. Each

participantsawasetofsevenoutcomes that included onlyone of the critical items(“bank teller” or “feministbank teller”). Some rankedtheoutcomesbyresemblance,others by likelihood. As inthe case of Tom W, theaverage rankings byresemblance and bylikelihood were identical;“feminist bank teller” rankedhigher than “bank teller” inboth.

Then we took theexperiment further, using awithin-subject design. Wemadeup thequestionnaireasyousawit,with“bankteller”inthesixthpositioninthelistand “feminist bank teller” asthe last item. We wereconvincedthatsubjectswouldnotice the relation betweenthe two outcomes, and thattheir rankings would beconsistentwith logic. Indeed,weweresocertainofthisthat

we did not think itworthwhile to conduct aspecial experiment. Myassistantwasrunninganotherexperimentinthelab,andsheasked the subjects tocomplete the new Lindaquestionnaire while signingout,justbeforetheygotpaid.About ten questionnaires

hadaccumulated ina trayonmy assistant’s desk before Icasually glanced at them andfoundthatallthesubjectshad

ranked “feminist bank teller”asmore probable than “bankteller.”IwassosurprisedthatI still retain a “flashbulbmemory”ofthegraycolorofthe metal desk and of whereeveryone was when I madethat discovery. I quicklycalled Amos in greatexcitement to tell him whatwe had found:we had pittedlogic againstrepresentativeness, andrepresentativenesshadwon!

Inthelanguageofthisbook,we had observed a failure ofSystem 2: our participantshad a fair opportunity todetect the relevance of thelogical rule, since bothoutcomes were included inthe same ranking. They didnot take advantage of thatopportunity. When weextended the experiment, wefound that 89% of theundergraduates inoursampleviolated the logic of

probability. We wereconvinced that statisticallysophisticated respondentswould do better, so weadministered the samequestionnaire to doctoralstudents in the decision-science program of theStanford Graduate School ofBusiness, all of whom hadtaken several advancedcourses in probability,statistics,anddecisiontheory.We were surprised again:

85% of these respondentsalso ranked “feminist bankteller” as more likely than“bankteller.”In what we later described

as “increasingly desperate”attempts to eliminate theerror, we introduced largegroupsofpeopletoLindaandasked them this simplequestion:

Which alternative ismoreprobable?

Lindaisabankteller.Linda is a bank tellerand is active in thefeministmovement.

This stark version of theproblem made Linda famousinsomecircles,anditearnedus years of controversy.About 85% to 90% ofundergraduates at severalmajor universities chose thesecond option, contrary tologic. Remarkably, the

sinners seemed to have noshame. When I asked mylarge undergraduatnite classinsomeindignation,“Doyourealizethatyouhaveviolatedan elementary logical rule?”someone in the back rowshouted, “So what?” and agraduate student who madethe same error explainedherself by saying, “I thoughtyou just asked for myopinion.”Thewordfallacyisused,in

general, when people fail toapply a logical rule that isobviouslyrelevant.AmosandI introduced the idea of aconjunction fallacy, whichpeople commit when theyjudge a conjunction of twoevents (here, bank teller andfeminist)tobemoreprobablethan one of the events (bankteller)inadirectcomparison.As in the Müller-Lyer

illusion, the fallacy remainsattractive even when you

recognizeitforwhatitis.Thenaturalist Stephen Jay Goulddescribed his own strugglewith the Linda problem. Heknew the correct answer, ofcourse, and yet, hewrote, “alittlehomunculus inmyheadcontinues to jump up anddown, shouting at me—‘butshecan’tjustbeabankteller;read the description.’” ThelittlehomunculusisofcourseGould’sSystem1speakingtohim in insistent tones. (The

two-system terminology hadnotyetbeenintroducedwhenhewrote.)The correct answer to the

short version of the Lindaproblem was the majorityresponse in only one of ourstudies: 64% of a group ofgraduatestudentsinthesocialsciences at Stanford and atBerkeley correctly judged“feminist bank teller” to beless probable than “bankteller.”Intheoriginalversion

with eight outcomes (shownabove),only15%ofasimilargroup of graduate studentshad made that choice. Thedifference is instructive. Thelonger version separated thetwo critical outcomes by anintervening item (insurancesalesperson), and the readersjudged each outcomeindependently, withoutcomparing them. The shorterversion, in contrast, requiredan explicit comparison that

mobilized System 2 andallowed most of thestatistically sophisticatedstudents to avoid the fallacy.Unfortunately, we did notexplore the reasoning of thesubstantialminority (36%)ofthis knowledgeable groupwhochoseincorrectly.The judgments of

probability that ourrespondents offered, in boththe Tom W and Lindaproblems, corresponded

precisely to judgments ofrepresentativeness (similarityto stereotypes).Representativenessbelongstoa cluster of closely relatedbasic assessments that arelikely to be generatedtogether. The mostrepresentative outcomescombinewith the personalitydescription to produce themost coherent stories. Themost coherent stories are notnecessarily the most

probable, but they areplausible, and the notions ofcoherence, plausibility, andprobability are easilyconfusedbytheunwary.The uncritical substitution

of plausibility for probabilityhas pernicious effects onjudgmentswhenscenariosareused as tools of forecasting.Considerthesetwoscenarios,which were presented todifferent groups, with arequest to evaluate their

probability:

A massive floodsomewhere in NorthAmerica next year, inwhich more than 1,000peopledrown

An earthquake inCalifornia sometimenext year, causing aflood in which morethan1,000peopledrown

The California earthquakescenario is more plausiblethan the North Americascenario, although itsprobability is certainlysmaller. As expected,probability judgments werehigherforthericherandmoreentdetailed scenario, contraryto logic. This is a trap forforecasters and their clients:adding detail to scenariosmakesthemmorepersuasive,butlesslikelytocometrue.

To appreciate the role ofplausibility, consider thefollowingquestions:

Which alternative ismoreprobable?Markhashair.Markhasblondhair.

and

Which alternative ismoreprobable?Janeisateacher.Jane is a teacher and

walkstowork.The two questions have thesame logical structure as theLinda problem, but theycauseno fallacy, because themore detailed outcome isonlymore detailed—it is notmore plausible, or morecoherent, or a better story.Theevaluationofplausibilityand coherence does notsuggest and answer to theprobability question. In the

absence of a competingintuition,logicprevails.

LessIsMore,SometimesEvenInJointEvaluation

Christopher Hsee, of theUniversity ofChicago, askedpeople to price sets ofdinnerware offered in aclearancesaleinalocalstore,where dinnerware regularlyruns between $30 and $60.

There were three groups inhis experiment. The displaybelow was shown to onegroup; Hsee labels that jointevaluation, because it allowsacomparisonofthetwosets.The other two groups wereshown only one of the twosets;thisissingleevaluation.Joint evaluation is a within-subject experiment, andsingle evaluation is between-subjects.

SetA:40pieces

SetB:24pieces

Dinnerplates

8,allingoodcondition

8,allingoodcondition

Soup/saladbowls

8,allingoodcondition

8,allingoodcondition

Dessertplates

8,allingoodcondition

8,allingoodcondition

Cups8,2ofthem

broken

Saucers8,7ofthembroken

Assuming that thedishes in

the two sets are of equalquality,whichisworthmore?This question is easy. YoucanseethatSetAcontainsallthedishesofSetB,andsevenadditionalintactdishes,anditmustbevaluedmore.Indeed,

the participants in Hsee’sjoint evaluation experimentwere willing to pay a littlemoreforSetAthanforSetB:$32versus$30.The results reversed in

single evaluation, where SetB was priced much higherthan Set A: $33 versus $23.Weknowwhythishappened.Sets (including dinnerwaresets!) are represented bynorms and prototypes. Youcan sense immediately that

the average value of thedishes ismuch lower for SetAthanforSetB,becausenoone wants to pay for brokendishes. If the averagedominatestheevaluation,itisnot surprising that Set B isvaluedmore.Hsee called theresultingpatternless ismore.By removing 16 items fromSet A (7 of them intact), itsvalueisimproved.Hsee’s finding was

replicated by the

experimental economist JohnList in a real market forbaseball cards. He auctionedsets of ten high-value cards,and identical sets to whichthree cards of modest valuewere added. As in thedinnerware experiment, thelarger setswere valuedmorethan thesmallerones in jointevaluation, but less in singleevaluation. From theperspective of economictheory,thisresultistroubling:

the economic value of adinnerware set or of acollectionofbaseballcardsisasum-likevariable.Addingapositively valued item to theset can only increase itsvalue.The Linda problem and the

dinnerware problem haveexactly the same structure.Probability, like economicvalue, is a sum-like variable,asillustratedbythisexample:

probability (Linda is ateller) = probability(Linda is feminist teller)+ probability (Linda isnon-feministteller)

Thisisalsowhy,asinHsee’sdinnerware study, singleevaluations of the Lindaproblem produce a less-is-more pattern. System 1averagesinsteadofadding,sowhen the non-feminist banktellers are removed from the

set, subjective probabilityincreases.However, thesum-like nature of the variable isless obvious for probabilitythan for money. As a result,jointevaluationeliminatestheerror only in Hsee’sexperiment, not in the Lindaexperiment.Linda was not the only

conjunction error thatsurvivedjointevaluation.Wefound similar violations oflogic in many other

judgments. Participants inone of these studies wereasked to rank four possibleoutcomes of the nextWimbledon tournament frommost to least probable.BjörnBorgwasthedominanttennisplayer of the day when thestudy was conducted. Theseweretheoutcomes:

A. Borg will win thematch.

B.Borgwilllosethefirst

set.C.Borgwilllosethefirst

setbutwinthematch.D.Borgwillwinthefirst

setbutlosethematch.The critical items are B andC. B is the more inclusiveeventanditsprobabilitymustbe higher than that of anevent it includes.Contrary tologic, but not torepresentativeness orplausibility, 72% assigned B

a lower probability thanC—another instance of less ismore in a direct comparison.Here si again, the scenariothat was judged moreprobable was unquestionablymore plausible, a morecoherent fitwith all thatwasknown about the best tennisplayerintheworld.To head off the possible

objectionthattheconjunctionfallacy is due to amisinterpretation of

probability,we constructed aproblem that requiredprobability judgments, but inwhich the events were notdescribed in words, and theterm probability did notappear at all. We toldparticipants about a regularsix-sided diewith four greenfaces and two red faces,which would be rolled 20times.Theywereshownthreesequences of greens (G) andreds (R), and were asked to

choose one. They would(hypothetically) win $25 iftheirchosensequenceshowedup.Thesequenceswere:1. RGRRR2. GRGRRR3. GRRRRR

Because the die has twice asmany green as red faces, thefirst sequence is quiteunrepresentative—like Linda

being a bank teller. Thesecond sequence, whichcontainssixtosses,isabetterfit to what we would expectfrom this die, because itincludes two G’s. However,thissequencewasconstructedby adding a G to thebeginning of the firstsequence, so it can only beless likelythanthefirst.ThisisthenonverbalequivalenttoLinda being a feminist bankteller.As in theLinda study,

representativenessdominated.Almost two-thirds ofrespondents preferred to beton sequence2 rather thanonsequence 1. When presentedwith arguments for the twochoices, however, a largemajority found the correctargument (favoring sequence1)moreconvincing.The next problem was a

breakthrough, because wefinally found a condition inwhich the incidence of the

conjunctionfallacywasmuchreduced. Two groups ofsubjectssawslightlydifferentvariantsofthesameproblem:

The incidence of errors was

65%inthegroupthatsawtheproblemontheleft,andonly25%inthegroupthatsawtheproblemontheright.Why is the question “How

many of the 100participants…” so mucheasier than “Whatpercentage…”? A likelyexplanation is that thereference to 100 individualsbringsaspatialrepresentationtomind. Imagine thata largenumber of people are

instructed to sort themselvesintogroupsinaroom:“Thosewhose names begin with theletters A to L are told togather in the front leftcorner.” They are theninstructed to sort themselvesfurther. The relation ofinclusionisnowobvious,andyou can see that individualswhose name begins with Cwillbeasubsetof thecrowdinthefrontleftcorner.Inthemedical survey question,

heartattackvictimsendupina corner of the room, andsomeofthemarelessthan55years old. Not everyone willshare this particular vividimagery, but manysubsequent experiments haveshown that the frequencyrepresentation,asitisknown,makes it easy to appreciatethat one group is whollyincluded in the other. Thesolutiontothepuzzleappearsto be that a question phrased

as “how many?” makes youthink of individuals, but thesame question phrased as“whatpercentage?”doesnot.Whathavewelearnedfrom

these studies about theworkings of System 2? Oneconclusion,whichisnotnew,is that System 2 is notimpressively alert. Theundergraduates and graduatestudents who participated inour thastudies of theconjunction fallacy certainly

“knew” the logic of Venndiagrams, but they did notapply it reliably even whenall the relevant informationwaslaidoutinfrontofthem.The absurdity of the less-is-more pattern was obvious inHsee’s dinnerware study andwas easily recognized in the“how many?” representation,butitwasnotapparenttothethousands of people whohave committed theconjunction fallacy in the

originalLindaproblemandinothers like it. In all thesecases, the conjunctionappeared plausible, and thatsufficed for an endorsementofSystem2.The lazinessofSystem2 is

partofthestory.Iftheirnextvacation had depended on it,and if they had been givenindefinite time and told tofollow logic and not toansweruntiltheyweresureoftheir answer, I believe that

most of our subjects wouldhaveavoided theconjunctionfallacy. However, theirvacationdidnotdependonacorrect answer; they spentverylittletimeonit,andwerecontent to answer as if theyhadonlybeen“askedfortheiropinion.” The laziness ofSystem2isanimportantfactof life, and the observationthat representativeness canblock the application of anobviouslogicalruleisalsoof

someinterest.The remarkable aspect of

theLindastoryisthecontrastto the broken-dishes study.The two problems have thesame structure, but yielddifferent results. People whosee the dinnerware set thatincludes broken dishes put avery low price on it; theirbehavior reflects a rule ofintuition.Otherswhoseebothsetsatonceapply the logicalrulethatmoredishescanonly

add value. Intuition governsjudgments in the between-subjectscondition;logicrulesin joint evaluation. In theLinda problem, in contrast,intuitionoftenovercamelogiceven in joint evaluation,although we identified someconditions in which logicprevails.Amos and I believed that

the blatant violations of thelogic of probability that wehad observed in transparent

problemswereinterestingandworth reporting to ourcolleagues.We also believedthat the results strengthenedourargumentaboutthepowerof judgment heuristics, andthat they would persuadedoubters.Andinthiswewerequite wrong. Instead, theLindaproblembecameacasestudy in the norms ofcontroversy.TheLindaproblemattracted

agreatdealofattention,butit

also became a magnet forcritics of our approach tojudgment.Aswehadalreadydone, researchers foundcombinations of instructionsand hints that reduced theincidenceofthefallacy;someargued that, in the contextofthe Linda problem, it isreasonable for subjects tounderstand the word“probability” as if it means“plausibility.” Thesearguments were sometimes

extended to suggest that ourentire enterprise wasmisguided: if one salientcognitive illusion could beweakenedorexplainedaway,others could be aswell.Thisreasoningneglectstheuniquefeature of the conjunctionfallacy as a case of conflictbetween intuition and logic.The evidence that we hadbuilt up for heuristics frombetween-subjects experiment(including studies of Linda)

was not challenged—it wassimplynot addressed, and itssalience was diminished bythe exclusive focus on theconjunction fallacy. The neteffect of the Linda problemwas an increase in thevisibility of our work to thegeneral public, and a smalldent in the credibility of ourapproach among scholars inthe field. This was not at allwhatwehadexpected.Ifyouvisitacourtroomyou

will observe that lawyersapply twostylesofcriticism:todemolish a case they raisedoubts about the strongestarguments that favor it; todiscreditawitness,theyfocuson the weakest part of thetestimony. The focus onweaknesses is also normal inpoliticaverl debates. I do notbelieve it is appropriate inscientific controversies, but Ihavecometoacceptasafactof life that the norms of

debate in the social sciencesdo not prohibit the politicalstyle of argument, especiallywhenlargeissuesareatstake—and the prevalence of biasinhumanjudgmentisa largeissue.Some years ago I had a

friendly conversation withRalph Hertwig, a persistentcritic of the Linda problem,withwhomIhadcollaboratedinavainattempttosettleourdifferences. I askedhimwhy

he and others had chosen tofocus exclusively on theconjunction fallacy, ratherthan on other findings thatprovided stronger support forourposition.Hesmiledasheanswered, “It was moreinteresting,” adding that theLinda problem had attractedsomuchattentionthatwehadnoreasontocomplain.

SpeakingofLessis

More

“Theyconstructedaverycomplicated scenarioandinsistedoncallingithighlyprobable.Itisnot—it is only a plausiblestory.”

“Theyaddedacheapgifttotheexpensiveproduct,andmadethewholedealless attractive. Less is

moreinthiscase.”

“In most situations, adirectcomparisonmakespeoplemore careful andmore logical. But notalways. Sometimesintuitionbeatslogicevenwhen thecorrect answerstaresyouintheface.”

P

CausesTrumpStatistics

Consider the followingscenario and note yourintuitive answer to thequestion.

Acabwasinvolvedinahit-and-run accident atnight.Twocabcompanies, the

Green and the Blue,operateinthecity.You are given thefollowingdata:

85% of the cabs in thecity are Green and 15%areBlue.A witness identified thecab as Blue. The courttested the reliability ofthe witness under the

circumstances thatexisted on the night ofthe accident andconcluded that thewitness correctlyidentified each one ofthe two colors 80% ofthe time and failed 20%ofthetime.

What is the probabilitythat the cab involved in

the accident was BlueratherthanGreen?

ThisisastandardproblemofBayesianinference.Therearetwo items of information: abase rate and the imperfectlyreliable testimony of awitness. In the absence of awitness,theprobabilityoftheguiltycabbeingBlueis15%,which is thebase rateof thatoutcome. If the two cabcompanies had been equally

large, the base ratewould beuninformativeandyouwouldconsider only the reliabilityofthewitness,%">ourw

CausalStereotypesNow consider a variation ofthesamestory,inwhichonlythe presentation of the baseratehasbeenaltered.

You are given thefollowingdata:

The two companiesoperatethesamenumberof cabs, but Green cabsare involved in 85% ofaccidents.The information aboutthe witness is as in thepreviousversion.

The two versions of theproblem are mathematically

indistinguishable,buttheyarepsychologically quitedifferent.Peoplewhoreadthefirstversiondonotknowhowtousethebaserateandoftenignore it. In contrast, peoplewho see the second versiongive considerable weight tothe base rate, and theiraverage judgment is not toofar from the Bayesiansolution.Why?Inthefirstversion,thebase

rate of Blue cabs is a

statistical fact about the cabsin the city. A mind that ishungry for causal storiesfinds nothing to chew on:How does the number ofGreen and Blue cabs in thecity cause this cab driver tohitandrun?In the second version, in

contrast, thedriversofGreencabscausemorethan5timesasmanyaccidentsastheBluecabs do. The conclusion isimmediate: theGreen drivers

must be a collection ofreckless madmen! You havenow formed a stereotype ofGreen recklessness, whichyou apply to unknownindividual drivers in thecompany. The stereotype iseasily fitted into a causalstory,becauserecklessness isacausally relevant factaboutindividual cabdrivers. In thisversion, there are two causalstories that need to becombined or reconciled. The

first is thehitandrun,whichnaturallyevokestheideathata reckless Green driver wasresponsible.Thesecondisthewitness’s testimony, whichstronglysuggeststhecabwasBlue.Theinferencesfromthetwostoriesaboutthecolorofthe car are contradictory andapproximately cancel eachother. The chances for thetwo colors are about equal(the Bayesian estimate is41%, reflecting the fact that

thebaserateofGreencabsisalittlemoreextremethanthereliabilityof thewitnesswhoreportedaBluecab).The cab example illustrates

two types of base rates.Statisticalbaseratesarefactsaboutapopulationtowhichacasebelongs,buttheyarenotrelevant to the individualcase. Causal base rateschangeyourviewofhowtheindividual case came to be.The two types of base-rate

information are treateddifferently:

Statistical base rates aregenerallyunderweighted, andsometimes neglectedaltogether,whenspecificinformation about thecaseathandisavailable.Causal base rates aretreated as informationabouttheindividualcase

and are easily combinedwith other case-specificinformation.

Thecausalversionofthecabproblem had the form of astereotype: Green drivers aredangerous. Stereotypes arestatements about the groupthat are (at least tentatively)acceptedas factsabouteverymember. Hely re are twoexamples:

Mostofthegraduatesofthis inner-city school gotocollege.Interest in cycling iswidespreadinFrance.

These statements are readilyinterpreted as setting up apropensity in individualmembers of the group, andthey fit in a causal story.Many graduates of thisparticular inner-city schoolare eager and able to go to

college, presumably becauseofsomebeneficialfeaturesoflife in that school. There areforces in French culture andsocial life that cause manyFrenchmentotakeaninterestin cycling. You will beremindedofthesefactswhenyouthinkaboutthelikelihoodthat a particular graduate oftheschoolwillattendcollege,orwhenyouwonderwhetherto bring up the Tour deFranceinaconversationwith

aFrenchmanyoujustmet.

Stereotyping isabadwordinourculture,butinmyusageitis neutral. One of the basiccharacteristicsofSystem1 isthatitrepresentscategoriesasnorms and prototypicalexemplars. This is how wethink of horses, refrigerators,and New York policeofficers;weholdinmemoryarepresentationofoneormore

“normal”membersofeachofthese categories. When thecategories are social, theserepresentations are calledstereotypes.Somestereotypesare perniciously wrong, andhostile stereotyping can havedreadful consequences, butthepsychologicalfactscannotbe avoided: stereotypes, bothcorrectandfalse,arehowwethinkofcategories.Youmaynote the irony. In

the context of the cab

problem, theneglectofbase-rateinformationisacognitiveflaw, a failure of Bayesianreasoning,andtherelianceoncausalbaserates isdesirable.Stereotyping the Greendriversimprovestheaccuracyof judgment. In othercontexts, however, such ashiring or profiling, there is astrong social norm againststereotyping, which is alsoembedded in the law.This isas it should be. In sensitive

social contexts, we do notwant to draw possiblyerroneous conclusions aboutthe individual from thestatistics of the group. Weconsider it morally desirableforbaseratestobetreatedasstatistical facts about thegroup rather than aspresumptive facts aboutindividuals. In other words,werejectcausalbaserates.The social norm against

stereotyping, including the

opposition to profiling, hasbeen highly beneficial increatingamorecivilizedandmore equal society. It isusefultoremember,however,that neglecting validstereotypes inevitably resultsin suboptimal judgments.Resistance to stereotyping isalaudablemoralposition,butthe simplistic idea that theresistance is costless iswrong. The costs are worthpaying to achieve a better

society, but denying that thecostsexist,whilesatisfyingtothe soul and politicallycorrect, is not scientificallydefensible. Reliance on theaffectheuristic iscommoninpolitically chargedarguments. The positions wefavor have no cost and thosewe oppose have no benefits.We should be able to dobetter.

CausalSituations

Amos and I constructed thevariants of the cab problem,but we did not invent thepowerful notion of causalbase rates; we borrowed itfrom the psychologist IcekAjzen. In his experiment,Ajzenshowedhisparticipantsbrief vignettes describingsomestudentswhohadtakenan exam at Yale and askedthe participants to judge theprobability that each student

had passed the test. Themanipulation of causal bsoase rates wasstraightforward: Ajzen toldone group that the studentsthey saw had been drawnfrom a class in which 75%passed the exam, and toldanother group that the samestudentshadbeeninaclassinwhichonly25%passed.Thisis a powerful manipulation,because the base rate ofpassing suggests the

immediate inference that thetest that only 25% passedmust have been brutallydifficult. The difficulty of atest is, of course, one of thecausal factors that determineevery student’s outcome. Asexpected, Ajzen’s subjectswere highly sensitive to thecausal base rates, and everystudent was judged morelikely to pass in the high-success condition than in thehigh-failurerate.

Ajzen used an ingeniousmethod to suggest anoncausal base rate. He toldhis subjects that the studentsthey saw had been drawnfrom a sample, which itselfwas constructed by selectingstudents who had passed orfailedtheexam.Forexample,the information for the high-failuregroupreadasfollows:

The investigator wasmainly interested in the

causes of failure andconstructed a sample inwhich 75% had failedtheexamination.

Notethedifference.Thisbaserateisapurelystatisticalfactabout the ensemble fromwhichcaseshavebeendrawn.It has no bearing on thequestion asked, which iswhether the individualstudent passed or failed thetest. As expected, the

explicitly stated base rateshad some effects onjudgment,but theyhadmuchless impact than thestatistically equivalent causalbaserates.System1candealwith stories in which theelements are causally linked,but it is weak in statisticalreasoning. For a Bayesianthinker, of course, theversions are equivalent. It istempting toconclude thatwehave reached a satisfactory

conclusion: causal base ratesare used; merely statisticalfacts are (more or less)neglected. The next study,one ofmy all-time favorites,shows that the situation israthermorecomplex.

CanPsychologybeTaught?

The reckless cabdrivers andthe impossiblydifficult examillustrate two inferences that

people can draw from causalbaserates:astereotypicaltraitthat is attributed to anindividual, and a significantfeature of the situation thataffects an individual’soutcome. The participants inthe experiments made thecorrect inferences and theirjudgments improved.Unfortunately, things do notalwaysworkoutsowell.Theclassic experiment I describenext shows that people will

not draw from base-rateinformation an inference thatconflictswithotherbeliefs.Italso supports theuncomfortable conclusionthat teaching psychology ismostlyawasteoftime.The experiment was

conductedalongtimeagobythe social psychologistRichard Nisbett and hisstudent Eugene Borgida, atthe University of Michigan.They told students about the

renowned “helpingexperiment” that had beenconductedafewyearsearlierat New York University.Participants in thatexperiment were led toindividual booths and invitedto speak over the intercomabouttheirpersonallivesandproblems. They were to talkinturnforabouttwominutes.Only one microphone wasactiveatanyonetime.Therewere six participants in each

group, one of whom was astooge. The stooge spokefirst, following a scriptprepared by theexperimenters. He describedhis problems adjusting toNewYorkandadmittedwithobvious embarrassment thathe was prone to seizures,especiallywhen stressed.Allthe participants then had aturn. When the microphonewas again turned over to thestooge, he became agitated

and incoherent, saidhe felt aseizure coming on, andpeoasked for someone to helphim. The last words heardfrom him were, “C-couldsomebody-er-er-help-er-uh-uh-uh [choking sounds]. I…I’m gonna die-er-er-er I’m…gonna die-er-er-I seizure I-er[chokes, then quiet].”At thispoint the microphone of thenextparticipantautomaticallybecame active, and nothingmore was heard from the

possiblydyingindividual.What do you think the

participantsintheexperimentdid?Sofarastheparticipantsknew, one of them washaving a seizure and hadasked for help. However,there were several otherpeople who could possiblyrespond,soperhapsonecouldstay safely in one’s booth.These were the results: onlyfourofthefifteenparticipantsrespondedimmediatelytothe

appealforhelp.Sixnevergotout of their booth, and fiveothers came out only wellafter the “seizure victim”apparently choked. Theexperiment shows thatindividuals feel relieved ofresponsibility when theyknow that others have heardthesamerequestforhelp.Didtheresultssurpriseyou?

Very probably. Most of usthink of ourselves as decentpeople who would rush to

help in such a situation, andwe expect other decentpeople to do the same. Thepoint of the experiment, ofcourse,was to show that thisexpectation is wrong. Evennormal,decentpeopledonotrushtohelpwhentheyexpectothers to take on theunpleasantness of dealingwith a seizure. And thatmeansyou,too.Are you willing to endorse

the following statement?

“When I read the procedureof the helping experiment Ithought I would come to thestranger’s help immediately,asIprobablywouldifIfoundmyself alone with a seizurevictim.Iwasprobablywrong.If I findmyself inasituationinwhichotherpeoplehaveanopportunity to help, I mightnot step forward. Thepresence of others wouldreduce my sense of personalresponsibility more than I

initially thought.” This iswhatateacherofpsychologywouldhopeyouwould learn.Would you have made thesameinferencesbyyourself?The psychology professor

who describes the helpingexperimentwantsthestudentsto view the low base rate ascausal, just as in the case ofthe fictitious Yale exam. Hewants them to infer, in bothcases,thatasurprisinglyhighrate of failure implies a very

difficult test. The lessonstudents are meant to takeaway is that some potentfeature of the situation, suchas the diffusion ofresponsibility,inducesnormaland decent people such asthem to behave in asurprisinglyunhelpfulway.Changing one’smind about

human nature is hard work,and changing one’smind forthe worse about oneself iseven harder. Nisbett and

Borgida suspected thatstudents would resist theworkand theunpleasantness.Ofcourse,thestudentswouldbe able and willing to recitethe details of the helpingexperiment on a test, andwould even repeat the“official” interpretation interms of diffusion ofresponsibility. But did theirbeliefs about human naturereally change? To find out,Nisbett and Borgida showed

them videos of briefinterviews allegedlyconducted with two peoplewho had participated in theNew York study. Theinterviews were short andbland. The intervieweesappeared to be nice, normal,decent people. Theydescribed their hobbies, theirspare-timeactivities,andtheirplans for the future, whichwere entirely conventional.After watching the video of

an interview, the studentsguessed how quickly thatparticularpersonhadcometothe aid of the strickenstranger.

To applyBayesian reasoningto the task the students wereassigned,youshouldfirstaskyourself what you wouldhaveguessedabouttheastwoindividuals if you had notseen their interviews. This

question is answered byconsulting the base rate. Wehavebeen told thatonly4ofthe 15 participants in theexperiment rushed to helpafter the first request. Theprobability that anunidentified participant hadbeen immediately helpful istherefore 27%. Thus yourprior belief about anyunspecifiedparticipantshouldbe that he did not rush tohelp. Next, Bayesian logic

requires you to adjust yourjudgment in light of anyrelevant information aboutthe individual. However, thevideos were carefullydesignedtobeuninformative;they provided no reason tosuspect that the individualswould be eithermore or lesshelpful than a randomlychosen student. In theabsence of useful newinformation, the Bayesiansolution is to stay with the

baserates.Nisbett and Borgida asked

two groups of students towatch the videos and predictthe behavior of the twoindividuals. The students inthefirstgroupweretoldonlyabout the procedure of thehelpingexperiment,notaboutits results. Their predictionsreflected their views ofhuman nature and theirunderstanding of thesituation. As you might

expect, they predicted thatboth individuals wouldimmediately rush to thevictim’s aid. The secondgroup of students knew boththe procedure of theexperiment and its results.The comparison of thepredictionsofthetwogroupsprovides an answer to asignificant question: Didstudentslearnfromtheresultsof the helping experimentanything that significantly

changed their way ofthinking? The answer isstraightforward: they learnednothing at all. Theirpredictions about the twoindividuals wereindistinguishable from thepredictionsmade by studentswhohadnotbeenexposedtothe statistical results of theexperiment. They knew thebase rate in the group fromwhich the individuals hadbeen drawn, but they

remained convinced that thepeopletheysawonthevideohad been quick to help thestrickenstranger.For teachersofpsychology,

the implicationsof this studyare disheartening. When weteach our students about thebehavior of people in thehelping experiment, weexpect them to learnsomething they had notknown before; we wish tochange how they think about

people’s behavior in aparticularsituation.Thisgoalwas not accomplished in theNisbett-Borgida study, andthere is no reason to believethat the results would havebeen different if they hadchosen another surprisingpsychological experiment.Indeed, Nisbett and Borgidareported similar findings inteaching another study, inwhich mild social pressurecausedpeopletoacceptmuch

more painful electric shocksthan most of us (and them)would have expected.Studentswhodonotdevelopa new appreciation for thepower of social setting havelearnednothingofvaluefromthe experiment. Thepredictions they make aboutrandom strangers, or abouttheir own behavior, indicatethat they have not changedtheirviewofhowtheywouldhavebehaved.Inthewordsof

NisbettandBorgida,students“quietly exempt themselves”(and their friends andacquaintances) from theconclusions of experimentsthat surprise them. Teachersof psychology should notdespair, however, becauseNisbett and Borgida report away to make their studentsappreciate the point of thehelping experiment. Theytookanewgroupofstudentsand taught them the

procedure of the experimentbut did not tell them thegroup results. They showedthe two videos and simplytold their students that thetwo individuals they had justseen had not helped thestranger, then asked them toguess the global results. Theoutcome was dramatic: thestudents’ guesses wereextremelyaccurate.To teach students any

psychology they did not

know before, you mustsurprise them. But whichsurprisewill do?Nisbett andBorgidafoundthatwhentheypresented their students witha surprising statisticis al fact,thestudentsmanagedtolearnnothing at all. But when thestudents were surprised byindividual cases—two nicepeoplewhohadnothelped—they immediately made thegeneralization and inferredthat helping is more difficult

thantheyhadthought.Nisbettand Borgida summarize theresults in a memorablesentence:

Subjects’ unwillingnessto deduce the particularfrom the general wasmatched only by theirwillingness to infer thegeneral from theparticular.

This is a profoundly

important conclusion. Peoplewho are taught surprisingstatistical facts about humanbehaviormaybeimpressedtothe point of telling theirfriends aboutwhat they haveheard,butthisdoesnotmeanthattheirunderstandingoftheworldhasreallychanged.Thetestoflearningpsychologyiswhether your understandingof situations you encounterhaschanged,notwhetheryouhave learned a new fact.

There is a deep gap betweenour thinking about statisticsand our thinking aboutindividual cases. Statisticalresults with a causalinterpretationhavea strongereffect on our thinking thannoncausal information. Buteven compelling causalstatistics will not changelong-held beliefs or beliefsrootedinpersonalexperience.On theotherhand,surprisingindividual cases have a

powerful impact and are amore effective tool forteaching psychology becausethe incongruity must beresolved and embedded in acausalstory.Thatiswhythisbook contains questions thatare addressed personally tothe reader. You are morelikely to learn something byfindingsurprisesinyourownbehavior than by hearingsurprising facts about peopleingeneral.

SpeakingofCausesandStatistics

“We can’t assume thatthey will really learnanything from merestatistics. Let’s showthem one or tworepresentative individualcases to influence theirSystem1.”

“Noneedtoworryabout

this statisticalinformation beingignored.Onthecontrary,it will immediately beused to feed astereotype.”

P

RegressiontotheMean

I had one of the mostsatisfying eureka experiencesof my career while teachingflightinstructorsintheIsraeliAir Force about thepsychology of effectivetraining. I was telling themabout an important principleof skill training: rewards for

improved performance workbetter than punishment ofmistakes. This proposition issupported by much evidencefrom research on pigeons,rats, humans, and otheranimals.When I finished my

enthusiastic speech, one ofthemost seasoned instructorsin the group raised his handand made a short speech ofhis own. He began byconceding that rewarding

improved performance mightbegood for thebirds, but hedeniedthatitwasoptimalforflight cadets.This iswhathesaid: “On many occasions Ihavepraised flightcadets forclean execution of someaerobaticmaneuver.Thenexttime they try the samemaneuver they usually doworse. On the other hand, Ihave often screamed into acadet’s earphone for badexecution, and in general he

doesbetterttaskyryabrtworepon his next try. So pleasedon’t tell us that rewardworks and punishment doesnot, because the opposite isthecase.”This was a joyous moment

of insight, when I saw in anew light a principle ofstatistics that I had beenteaching for years. Theinstructor was right—but hewas also completely wrong!His observation was astute

and correct: occasions onwhich he praised aperformancewerelikelytobefollowed by a disappointingperformance, andpunishments were typicallyfollowedbyanimprovement.But the inference he haddrawn about the efficacy ofreward and punishment wascompletely off the mark.What he had observed isknown as regression to themean,whichinthatcasewas

duetorandomfluctuationsinthe quality of performance.Naturally, he praised only acadetwhoseperformancewasfar better than average. Butthe cadet was probably justlucky on that particularattempt and therefore likelyto deteriorate regardless ofwhether or not he waspraised. Similarly, theinstructorwould shout into acadet’s earphones only whenthe cadet’s performance was

unusually bad and thereforelikely to improve regardlessof what the instructor did.The instructorhadattachedacausal interpretation to theinevitable fluctuations of arandomprocess.The challenge called for a

response, but a lesson in thealgebra of prediction wouldnot be enthusiasticallyreceived. Instead, I usedchalk tomarka targeton thefloor.Iaskedeveryofficerin

the room to turn his back tothe target and throw twocoins at it in immediatesuccession, without looking.We measured the distancesfromthetargetandwrotethetworesultsofeachcontestanton the blackboard. Then werewrote the results in order,from the best to the worstperformance on the first try.Itwasapparentthatmost(butnot all) of those who haddone best the first time

deteriorated on their secondtry, and those who had donepoorly on the first attemptgenerallyimproved.Ipointedout to the instructors thatwhat they saw on the boardcoincided with what we hadheard about the performanceof aerobatic maneuvers onsuccessive attempts: poorperformance was typicallyfollowedbyimprovementandgood performance bydeterioration, without any

help from either praise orpunishment.The discovery I made on

that day was that the flightinstructorsweretrappedinanunfortunate contingency:because theypunishedcadetswhen performancewas poor,they were mostly rewardedby a subsequentimprovement, even ifpunishment was actuallyineffective. Furthermore, theinstructors were not alone in

that predicament. I hadstumbled onto a significantfact of the human condition:the feedback to which lifeexposes us is perverse.Becausewetendtobenicetoother people when theypleaseusandnastywhentheydo not, we are statisticallypunished for being nice andrewardedforbeingnasty.

TalentandLuck

A few years ago, JohnBrockman, who edits theonlinemagazineEdge, askeda number of scientists toreport their “favoriteequation.” These were myofferings:

success=talent+luckgreat success = a littlemore talent + a lot ofluck

The unsurprising idea that

luck often contributes tosuccess has surprisingconsequenceswhenweapplyit to the first two days of ahigh-level golf tournament.To keep things simple,assume thatonbothdays theaverage score of thecompetitors was at par 72.Wefocusonaplayerwhodidveryedwellon thefirstday,closing with a score of 66.What canwe learn from thatexcellent score? An

immediate inference is thatthe golfer is more talentedthan the average participantin the tournament. Theformula for success suggeststhat another inference isequally justified: the golferwho did so well on day 1probablyenjoyedbetter-than-average luck on that day. Ifyou accept that talent andluck both contribute tosuccess, the conclusion thatthe successful golfer was

lucky is as warranted as theconclusionthatheistalented.By the same token, if you

focusonaplayerwhoscored5 over par on that day, youhavereasontoinferboththathe is rather weak and had abadday.Ofcourse,youknowthat neither of theseinferences is certain. It isentirely possible that theplayer who scored 77 isactuallyverytalentedbuthadan exceptionally dreadful

day. Uncertain though theyare, the following inferencesfrom the score on day 1 areplausible and will be correctmore often than they arewrong.

above-average score onday 1 = above-averagetalent+luckyonday1

and

below-average score onday 1 = below-average

talent + unlucky on day1

Now, suppose you know a

golfer’s score on day 1 andareasked topredicthisscoreon day 2. You expect thegolfertoretainthesamelevelof talent on the second day,so your best guesses will be“above average” for the firstplayer and “below average”for the second player. Luck,of course, is a different

matter. Since you have nowayofpredictingthegolfers’luck on the second (or any)day,yourbest guessmustbethatitwillbeaverage,neithergood nor bad. This meansthat in the absence of anyother information, your bestguessabouttheplayers’scoreon day 2 should not be arepeat of their performanceonday1.Thisisthemostyoucansay:

Thegolferwhodidwellon day 1 is likely to besuccessful on day 2 aswell,butlessthanonthefirst, because theunusualluckheprobablyenjoyed on day 1 isunlikelytohold.The golfer who didpoorly on day 1 willprobably be belowaverage on day 2, butwill improve, becausehis probable streak of

bad luck is not likely tocontinue.

Wealsoexpectthedifferencebetween the two golfers toshrink on the second day,althoughourbestguessisthatthe first player will still dobetterthanthesecond.My students were always

surprisedtohearthatthebestpredictedperformanceonday2ismoremoderate,closer to

theaveragethantheevidenceon which it is based (thescoreonday1).This iswhythe pattern is calledregression to the mean. Themore extreme the originalscore,themoreregressionweexpect,becauseanextremelygood score suggests a verylucky day. The regressiveprediction is reasonable, butitsaccuracyisnotguaranteed.A few of the golfers whoscored 66 on day 1 will do

even better on the secondday, if their luck improves.Most will do worse, becausetheir luck will no longer beaboveaverage.Now let us go against the

time arrow. Arrange theplayers by their performanceon day 2 and look at theirperformance on day 1. Youwill find precisely the samepattern of regression to themean. The golfers who didbest on day 2 were probably

lucky on that day, and thebest guess is that they hadbeenlessluckyandhaddonefilesswellonday1.Thefactthat you observe regressionwhen you predict an earlyevent from a later eventshouldhelpconvinceyouthatregression does not have acausalexplanation.Regression effects are

ubiquitous, and so aremisguided causal stories toexplain them. A well-known

example is the “SportsIllustrated jinx,” the claimthat an athlete whose pictureappears on the cover of themagazine is doomed toperformpoorly the followingseason. Overconfidence andthe pressure of meeting highexpectationsareoftenofferedasexplanations.Butthereisasimpler account of the jinx:an athletewho gets to be onthecoverofSportsIllustratedmust have performed

exceptionally well in thepreceding season, probablywiththeassistanceofanudgefromluck—andluckisfickle.I happened to watch the

men’s ski jump event in theWinterOlympicswhileAmosand I were writing an articleabout intuitive prediction.Eachathletehastwojumpsinthe event, and the results arecombined for the final score.I was startled to hear thesportscaster’s comments

whileathleteswerepreparingfor their second jump:“Norway had a great firstjump; he will be tense,hopingtoprotecthisleadandwill probably do worse” or“Swedenhadabadfirstjumpand now he knows he hasnothing to lose and will berelaxed, which should helphim do better.” Thecommentator had obviouslydetected regression to themean and had invented a

causal story for which therewas no evidence. The storyitself could even be true.Perhaps if we measured theathletes’ pulse before eachjumpwemightfindthat theyareindeedmorerelaxedafterabadfirstjump.Andperhapsnot.Thepointtorememberisthat thechangefromthefirstto the second jump does notneed a causal explanation. Itisamathematicallyinevitableconsequence of the fact that

luck played a role in theoutcome of the first jump.Not a very satisfactory story—we would all prefer acausalaccount—butthatisallthereis.

UnderstandingRegression

Whether undetected orwrongly explained, thephenomenon of regression isstrange to the human mind.

Sostrange,indeed,thatitwasfirstidentifiedandunderstoodtwo hundred years after thetheory of gravitation anddifferential calculus.Furthermore, it took one ofthebestmindsofnineteenth-centuryBritaintomakesenseof it, and that with greatdifficulty.Regressiontothemeanwas

discoveredandnamed late inthe nineteenth century by SirFrancisGalton, a half cousin

of Charles Darwin and arenownedpolymath.Youcansensethethrillofdiscoveryinan article he published in1886 under the title“Regression towardsMediocrity in HereditaryStature,” which reportsmeasurements of size insuccessive generations ofseeds and in comparisons ofthe height of children to theheight of their parents. Hewrites about his studies of

seeds:

Theyyieldedresultsthatseemedverynoteworthy,and I used them as thebasis of a lecture beforethe Royal Institution onFebruary 9th, 1877. Itappeared from theseexperiments that theoffspringdidnot tend toresemble their parentseeds in size, but to bealways more mediocre

thanthey—tobesmallerthan the parents, if theparentswerelarge;tobelargerthantheparents,ifthe parents were verysmall…The experimentsshowed further that themean filial regressiontowards mediocrity wasdirectly proportional tothe parental deviationfromit.

Galton obviously expected

his learned audience at theRoyal Institution—the oldestindependent research societyin the world—to be assurprised by his “noteworthyobservation” as he had been.What is truly noteworthy isthat he was surprised by astatistical regularity that isascommon as the air webreathe. Regression effectscan be found wherever welook,butwedonotrecognizethemforwhat theyare.They

hide in plain sight. It tookGalton several years toworkhiswayfromhisdiscoveryoffilial regression insize to thebroadernotionthatregressioninevitably occurs when thecorrelation between twomeasures is less thanperfect,andheneededthehelpofthemost brilliant statisticians ofhis time to reach thatconclusion.One of the hurdles Galton

had to overcome was the

problem of measuringregression between variablesthataremeasuredondifferentscales, such as weight andpiano playing. This is doneby using the population as astandard of reference.Imagine that weight andpiano playing have beenmeasured for 100 children inall grades of an elementaryschool, and that they havebeenrankedfromhightolowon each measure. If Jane

ranks third in piano playingandtwenty-seventhinweight,it is appropriate to say thatsheisabetterpianistthansheis tall. Let us make someassumptionsthatwillsimplifythings:Atanyage,

Piano-playing successdepends only onweeklyhoursofpractice.Weightdependsonlyon

consumption of icecream.Ice cream consumptionand weekly hours ofpracticeareunrelated.

Now, using ranks (or thestandard scores thatstatisticians prefer), we canwritesomeequations:

weight=age+icecreamconsumption

piano playing = age +weeklyhoursofpractice

Youcanseethattherewillberegression to themeanwhenwe predict piano playingfromweight,orviceversa.Ifall you know about Tom isthat he ranks twelfth inweight (well above average),you can infer (statistically)thatheisprobablyolderthanaverage and also that heprobably consumes more ice

cream than other children. IfallyouknowaboutBarbaraisthat she is eighty-fifth inpiano (far below the averageof the group), you can inferthatshe is likely tobeyoungand that she is likely topractice less than most otherchildren.The correlation coefficient

betweentwomeasures,whichvaries between 0 and 1, is ameasure of the relativeweight of the factors they

share. For example, we allshare half our genes witheach of our parents, and fortraits inwhichenvironmentalfactors have relatively littleinfluence, suchasheight, thecorrelation between parentandchild isnot far from .50.Toappreciatethemeaningofthe correlation measure, thefollowingare someexamplesofcoefficients:

The correlation betweenthe size of objectsmeasuredwith precisionin English or in metricunits is 1. Any factorthat influences onemeasure also influencesthe other; 100% ofdeterminantsareshared.The correlation betweenself-reported height andweight among adultAmerican males is .41.If you included women

and children, thecorrelation would bemuch higher, becauseindividuals’ gender andage influence both theirheight ann wd theirweight, boosting therelativeweightofsharedfactors.The correlation betweenSAT scores and collegeGPA is approximately.60. However, thecorrelation between

aptitude tests andsuccess in graduateschool is much lower,largely becausemeasuredaptitudevarieslittle in this selectedgroup. If everyone hassimilar aptitude,differences in thismeasure are unlikely toplay a large role inmeasuresofsuccess.The correlation betweenincome and education

levelintheUnitedStatesisapproximately.40.The correlation betweenfamily income and thelast four digits of theirphonenumberis0.

It took Francis Galton

several years to figure outthat correlation andregression are not twoconcepts—they are differentperspectives on the same

concept. The general rule isstraightforward but hassurprising consequences:whenever the correlationbetween two scores isimperfect, there will beregression to the mean. Toillustrate Galton’s insight,take a proposition that mostpeoplefindquiteinteresting:

Highly intelligentwomen tend to marrymen who are less

intelligentthantheyare.You can get a goodconversationstartedatapartybyasking foranexplanation,and your friends will readilyoblige.Evenpeoplewhohavehad some exposure tostatistics will spontaneouslyinterpret the statement incausalterms.Somemaythinkof highly intelligent womenwanting to avoid thecompetition of equally

intelligent men, or beingforcedtocompromiseintheirchoice of spouse becauseintelligentmendonotwanttocompete with intelligentwomen. More far-fetchedexplanationswill come up ata good party. Now considerthisstatement:

The correlation betweentheintelligencescoresofspouses is less thanperfect.

This statement is obviouslytrueandnotinterestingatall.Who would expect thecorrelation to be perfect?There is nothing to explain.But the statement you foundinteresting and the statementyou found trivial arealgebraically equivalent. Ifthe correlation between theintelligenceofspousesislessthan perfect (and if men andwomen on average do not

differ in intelligence), then itisamathematicalinevitabilitythathighly intelligentwomenwill be married to husbandswho are on average lessintelligent than they are (andvice versa, of course). Theobserved regression to themean cannot be moreinteresting or moreexplainable than theimperfectcorrelation.You probably sympathize

with Galton’s struggle with

the concept of regression.Indeed, the statisticianDavidFreedman used to say that ifthetopicofregressioncomesupinacriminalorcivil trial,the side that must explainregression to the jury willlose the case. Why is it sohard?Themainreasonforthedifficultyisarecurrentthemeof this book: our mind isstronglybiasedtowardcausalexplanations and does notdeal well with “mere

statistics.” When ourattentioniscalledtoanevent,associativememorywill lookforitscause—moreprecisely,activation will automaticallyspread to any cause that isalready stored in memory.Causal explanations will beevoked when regression isdetected, but they will bewrong because the truth isthat regression to the meanhas an explanation but doesnot have a cause. The event

that attracts our attention inthe golfing tournament is thefrequent deterioration of theperformance of the golferswho werecte successful onday 1. The best explanationofitisthatthosegolferswereunusually lucky that day, butthis explanation lacks thecausal force that our mindsprefer.Indeed,wepaypeoplequite well to provideinteresting explanations ofregressioneffects.Abusiness

commentator who correctlyannounces that “the businessdidbetterthisyearbecauseithaddonepoorly last year” islikely to have a short tenureontheair.

Our difficulties with theconcept of regressionoriginatewith both System1and System 2. Withoutspecial instruction, and inquite a few cases even after

some statistical instruction,the relationship betweencorrelation and regressionremains obscure. System 2findsitdifficulttounderstandand learn.This isdue inpartto the insistent demand forcausal interpretations, whichisafeatureofSystem1.

Depressed childrentreated with an energydrink improvesignificantly over a

three-monthperiod.I made up this newspaperheadline, but the fact itreportsistrue:ifyoutreatedagroup of depressed childrenforsometimewithanenergydrink, they would show aclinically significantimprovement. It is also thecase that depressed childrenwho spend some timestandingontheirheadorhugacatfortwentyminutesaday

will also show improvement.Most readers of suchheadlines will automaticallyinfer that theenergydrinkorthe cat hugging caused animprovement, but thisconclusion is completelyunjustified. Depressedchildren are an extremegroup, they are moredepressed than most otherchildren—and extremegroups regress to the meanover time. The correlation

betweendepressionscoresonsuccessive occasions oftestingislessthanperfect,sotherewillberegressiontothemean:depressedchildrenwillget somewhat better overtimeevenif theyhugnocatsand drink no Red Bull. Inorder to conclude that anenergy drink—or any othertreatment—is effective, youmust compare a group ofpatients who receive thistreatmenttoa“controlgroup”

thatreceivesnotreatment(or,better, receives a placebo).Thecontrolgroupisexpectedto improve by regressionalone, and the aim of theexperiment is to determinewhether the treated patientsimprovemorethanregressioncanexplain.Incorrect causal

interpretations of regressioneffects are not restricted toreaders of the popular press.The statistician Howard

Wainer has drawn up a longlist of eminent researcherswho have made the samemistake—confusing merecorrelation with causation.Regression effects are acommon source of trouble inresearch, and experiencedscientists develop a healthyfear of the trap ofunwarrantedcausalinference.

Oneofmyfavoriteexamples

of the errors of intuitiveprediction is adapted fromMax Bazerman’s excellenttext Judgment inManagerialDecisionMaking:

You are the salesforecaster for adepartment store chain.All stores are similar insize and merchandiseselection, but their salesdiffer because oflocation, competition,

andrandomfactors.Youare given the results for2011 and asked toforecast sales for 2012.You have beeninstructed to accept theoverall forecast ofeconomists that saleswill increase overall by10%. How would youcomplete the followingtable?

Store 2011 1 $11,000,000 ________2 $23,000,000 ________3 $18,000,000 ________4 $29,000,000 ________Total $61,000,000 $67,100,000Having read this chapter,

you know that the obvioussolutionofadding10%tothesales of each store is wrong.Youwantyourforecaststoberegressive, which requires

addingmore than10%to thelow-performingbranches andadding less (or evensubtracting) to others. But ifyouaskotherpeople,youarelikely to encounterpuzzlement: Why do youbother themwith an obviousquestion?AsGaltonpainfullydiscovered, the concept ofregression is far fromobvious.

Speakingof

RegressiontoMediocrity

“Shesaysexperiencehastaught her that criticismis more effective thanpraise.Whatshedoesn’tunderstandisthatit’salldue to regression to themean.”

“Perhaps his second

interview was less

impressive than the firstbecausehewasafraidofdisappointing us, butmore likely it was hisfirst that was unusuallygood.”

“Our screeningprocedure is good butnotperfect,soweshouldanticipate regression.We shouldn’t besurprised that the very

bestcandidatesoftenfailto meet ourexpectations.”

P

TamingIntuitivePredictions

Life presents us with manyoccasions to forecast.Economists forecast inflationand unemployment, financialanalysts forecast earnings,military experts predictcasualties, venture capitalistsassessprofitability,publishersand producers predict

audiences, contractorsestimate the time required tocomplete projects, chefsanticipate thedemandfor thedishes on their menu,engineers estimate theamount of concrete neededfor a building, firegroundcommanders assess thenumberof trucks thatwillbeneeded to put out a fire. Inour private lives,we forecastour spouse’s reaction to aproposed move or our own

future adjustment to a newjob.Some predictive judgments,

such as those made byengineers, rely largely onlook-up tables, precisecalculations, and explicitanalyses of outcomesobserved on similaroccasions. Others involveintuitionandSystem1,intwomain varieties. Someintuitions draw primarily onskill and expertise acquired

by repeated experience. Therapid and automaticjudgments and choices ofchess masters, firegroundcommanders, and physiciansthatGaryKleinhasdescribedin Sources of Power andelsewhere illustrate theseskilled intuitions, in which asolution to the currentproblem comes to mindquicklybecausefamiliarcuesarerecognized.Other intuitions, which are

sometimes subjectivelyindistinguishable from thefirst,arise fromtheoperationof heuristics that oftensubstitute an easy questionfor the harder one that wasasked. Intuitive judgmentscan be made with highconfidence even when theyare based on nonregressiveassessments of weakevidence. Of course, manyjudgments, especially in theprofessional domain, are

influenced by a combinationofanalysisandintuition.

NonregressiveIntuitions

Let us return to a personwehavealreadymet:

Julieiscurrentlyaseniorinastateuniversity.Sheread fluently when shewasfouryearsold.Whatis her grade pointaverage(GPA)?

Peoplewhoare familiarwiththe American educationalscenequicklycomeupwithanumber,whichisofteninthevicinity of 3.7 or 3.8. Howdoes this occur? Severaloperations of System 1 areinvolved.

A causal link betweenthe evidence (Julie’sreading) and the target

of the prediction (herGPA)issought.Thelinkcan be indirect. In thisinstance, early readingandahighGDParebothindications of academictalent. Some connectionis necessary. You (yourSystem 2) wouldprobably reject asirrelevant a report ofJulie winning a flyfishingcompetitiowhiredD=n or excelling at

weight lifting in highschool. The process iseffectively dichotomous.We are capable ofrejecting information asirrelevant or false, butadjusting for smallerweaknesses in theevidence is notsomethingthatSystem1can do. As a result,intuitive predictions arealmost completelyinsensitive to the actual

predictive quality of theevidence.Whenalinkisfound, as in the case ofJulie’s early reading,WYSIATIapplies:yourassociative memoryquickly andautomatically constructsthe best possible storyfrom the informationavailable.Next, the evidence isevaluatedinrelationtoarelevant norm. How

precocious is a childwhoreadsfluentlyatagefour?What relativerankor percentile scorecorresponds to thisachievement?Thegroupto which the child iscompared (we call it areference group) is notfully specified, but thisisalsotheruleinnormalspeech: if someonegraduating from collegeis described as “quite

clever” you rarely needto ask, “When you say‘quite clever,’ whichreference group do youhaveinmind?”The next step involvessubstitutionandintensitymatching. Theevaluation of the flimsyevidence of cognitiveability in childhood issubstituted as an answertothequestionabouthercollege GPA. Julie will

be assigned the samepercentile score for herGPA and for herachievementsasanearlyreader.The question specifiedthat the answermust beontheGPAscale,whichrequires anotherintensity-matchingoperation, from ageneral impression ofJulie’s academicachievements to the

GPA that matches theevidence for her talent.The final step is atranslation, from animpression of Julie’srelative academicstandingtotheGPAthatcorrespondstoit.

Intensity matching yields

predictions that are asextreme as the evidence onwhichtheyarebased,leading

people to give the sameanswer to two quite differentquestions:

What is Julie’spercentile score onreadingprecocity?What is Julie’spercentile score onGPA?

By now you should easily

recognize that all theseoperations are features of

System 1. I listed them hereas an orderly sequence ofsteps,butofcoursethespreadof activation in associativememory does not work thisway. You should imagine aprocess of spreadingactivation that is initiallypromptedbytheevidenceandthequestion,feedsbackuponitself, and eventually settlesonthemostcoherentsolutionpossible.

Amos and I once askedparticipants in an experimentto judgedescriptionsofeightcollege freshmen, allegedlywrittenbyacounseloronthebasis of interviews of theentering class. Eachdescription consisted of fiveadjectives,asinthefollowingexample:

intelligent, self-confident, well-read,

hardworking,inquisitiveWe asked some participantstoanswertwoquestions:

How much does thisdescription impress youwithrespecttoacademicability?

What percentage ofdescriptionsof freshmendo you believe wouldimpressyoumore?

Thequestionsrequireyouto

evaluate the evidence bycomparing the description toyournormfordescriptionsofstudents by counselors. Theveryexistenceofsuchanormis remarkable. Although yousurely do not knowhowyouacquired it, youhave a fairlyclear sense of how muchenthusiasm the descriptionconveys: the counselorbelieves that this student is

good, but not spectacularlygood. There is room forstronger adjectives thanintelligent (brilliant,creative), well-read(scholarly, erudite,impressively knowledgeable),andhardworking(passionate,perfectionist). The verdict:very likely to be in the top15%butunlikelytobeinthetop 3%. There is impressiveconsensusinsuchjudgments,atleastwithinaculture.

Theotherparticipantsinourexperiment were askeddifferentquestions:

Whatisyourestimateofthe grade point averagethat the student willobtain?What is the percentageof freshmen who obtainahigherGPA?

You need another look to

detect the subtle difference

between the two sets ofquestions. The differenceshould be obvious, but it isnot. Unlike the firstquestions,whichrequiredyouonlytoevaluatetheevidence,the second set involves agreatdealofuncertainty.Thequestion refers to actualperformanceattheendofthefreshman year. Whathappened during the yearsince the interview wasperformed? How accurately

can you predict the student’sactual achievements in thefirstyearatcollegefromfiveadjectives? Would thecounselorherselfbeperfectlyaccurateifshepredictedGPAfromaninterview?The objective of this study

wastocomparethepercentilejudgments that theparticipants made whenevaluating the evidence inonecase,andwhenpredictingthe ultimate outcome in

another. The results are easyto summarize: the judgmentswere identical. Although thetwo sets of questions differ(one is about thedescription,the other about the student’sfuture academicperformance),theparticipantstreated them as if they werethe same. As was the casewith Julie, the prediction ofthefutureisnotdistinguishedfromanevaluationofcurrentevidence—prediction

matches evaluation. This isperhaps thebestevidencewehave for the role ofsubstitution.Peopleareaskedfor a prediction but theysubstituteanevaluationoftheevidence, without noticingthat thequestion theyansweris not the one they wereasked. This process isguaranteed to generatepredictions that aresystematically biased; theycompletely ignore regression

tothemean.During my military service

in theIsraeliDefenseForces,Ispentsometimeattachedtoaunitthatselectedcandidatesfor officer training on thebasisofaseriesofinterviewsand field tests. Thedesignated criterion forsuccessful prediction was acadet’s final grade in officerschool. The validity of theratings was known to berather poor (I will tell more

about it in a later chapter).The unit still existed yearslater,when Iwas aprofessorand collaboratingwithAmosin the study of intuitivejudgment.Ihadgoodcontactswith the people at the unitandaskedthemforafavor.Inaddition to the usual gradingsystem they used to evaluatethe candidates, I asked fortheir best guess of the gradethateachof the futurecadetswould obtain in officer

school. They collected a fewhundred such forecasts. Theofficers who had producedthe prediof рctions were allfamiliar with the lettergrading system that theschool applied to its cadetsand the approximateproportions of A’s, B’s, etc.,amongthem.Theresultswerestriking: the relativefrequency of A’s and B’s inthe predictions was almostidenticaltothefrequenciesin

thefinalgradesoftheschool.These findings provide a

compelling example of bothsubstitution and intensitymatching. The officers whoprovided the predictionscompletely failed todiscriminate between twotasks:

their usual mission,which was to evaluatethe performance of

candidates during theirstayattheunitthe task I had askedthem to perform, whichwas an actual predictionofafuturegrade

They had simply translatedtheir own grades onto thescale used in officer school,applying intensity matching.Once again, the failure toaddress the (considerable)

uncertainty of theirpredictions had led them topredictions that werecompletelynonregressive.

ACorrectionforIntuitivePredictionsBack to Julie,ourprecociousreader. The correct way topredict her GPA wasintroduced in the precedingchapter. As I did there forgolf on successive days and

forweightandpianoplaying,I write a schematic formulafor the factors thatdeterminereading age and collegegrades:

reading age = sharedfactors+factorsspecifictoreadingage=100%GPA = shared factors +factors specific to GPA=100%

The shared factors involve

genetically determinedaptitude, thedegree towhichthe family supports academicinterests, and anything elsethat would cause the samepeople to be precociousreaders as children andacademically successful asyoungadults.Ofcoursethereare many factors that wouldaffect one of these outcomesandnot theother.Juliecouldhave been pushed to readearly by overly ambitious

parents,shemayhavehadanunhappy love affair thatdepressedher collegegrades,she could have had a skiingaccident during adolescencethatleftherslightlyimpaired,andsoon.Recall that the correlation

between two measures—inthe present case reading ageand GPA—is equal to theproportion of shared factorsamong their determinants.Whatisyourbestguessabout

that proportion? My mostoptimistic guess is about30%.Assumingthisestimate,we have all we need toproduce an unbiasedprediction. Here are thedirectionsforhowtogetthereinfoursimplesteps:1. Startwithanestimateof

averageGPA.2. Determine theGPA that

matchesyourimpression

oftheevidence.3. Estimate the correlation

between your evidenceandGPA.

4. If the correlation is .30,move 30% of thedistance from theaverage to thematchingGPA.

Step 1 gets you the baseline,the GPA you would havepredicted if you were told

nothing about Julie beyondthe fact that she is agraduating senior. In theabsence of information, youwould have predicted theaverage. (This is similar toassigning the base-rateprobability of businessadministration grahavрduateswhen you are told nothingaboutTomW.)Step2isyourintuitive prediction, whichmatches your evaluation ofthe evidence. Step 3 moves

youfromthebaselinetowardyour intuition, but thedistance you are allowed tomove depends on yourestimate of the correlation.Youendup,atstep4,withaprediction that is influencedby your intuition but is farmoremoderate.This approach to prediction

is general. You can apply itwheneveryouneedtopredicta quantitative variable, suchas GPA, profit from an

investment,orthegrowthofacompany. The approachbuildsonyourintuition,butitmoderates it, regresses ittoward the mean. When youhavegoodreasonstotrusttheaccuracy of your intuitiveprediction—a strongcorrelation between theevidenceandtheprediction—theadjustmentwillbesmall.Intuitivepredictionsneedto

becorrectedbecausetheyarenot regressive and therefore

are biased. Suppose that Ipredict for each golfer in atournament that his score onday2willbethesameashisscore on day 1. Thisprediction does not allow forregression to the mean: thegolferswhofaredwellonday1willonaveragedolesswellon day 2, and thosewho didpoorly will mostly improve.When they are eventuallycomparedtoactualoutcomes,nonregressivepredictionswill

be found to be biased. Theyare on average overlyoptimistic for those who didbest on the first day andoverly pessimistic for thosewho had a bad start. Thepredictions are as extreme asthe evidence. Similarly, ifyou use childhoodachievements to predictgrades in college withoutregressing your predictionstoward the mean, you willmore often than not be

disappointedbytheacademicoutcomesofearlyreadersandhappily surprised by thegrades of those who learnedto read relatively late. Thecorrectedintuitivepredictionseliminatethesebiases,sothatpredictions (both high andlow) are about equally likelyto overestimate and tounderestimate the true value.You still make errors whenyourpredictionsareunbiased,buttheerrorsaresmallerand

do not favor either high orlowoutcomes.

ADefenseofExtremePredictions?

IintroducedTomWearliertoillustrate predictions ofdiscrete outcomes such asfield of specialization orsuccess in an examination,which are expressed byassigning a probability to aspecified event (or in that

case by ranking outcomesfrom the most to the leastprobable). I also described aprocedure that counters thecommon biases of discreteprediction: neglect of baserates and insensitivity to thequalityofinformation.The biases we find in

predictionsthatareexpressedon a scale, such as GPA orthe revenue of a firm, aresimilartothebiasesobservedinjudgingtheprobabilitiesof

outcomes.The corrective procedures

arealsosimilar:

Both contain a baselineprediction, which youwouldmakeifyouknewnothingaboutthecaseathand. In the categoricalcase,itwasthebaserate.In thenumerical case, itis the average outcomeintherelevantcategory.

Bothcontainanintuitiveprediction, whichexpresses the numberthatcomestoyourmind,whether it is aprobabilityoraGPA.In both cases, you aimfor a prediction that isintermediatebetweenthebaseline and yourintuitiveresponse.Inthedefaultcaseofnouseful evidence, youstaywiththebaseline.

At the other extreme,you also stay with yourinitial predictiononsр.This will happen, ofcourse, only if youremain completelyconfident in your initialpredictionafteracriticalreview of the evidencethatsupportsit.In most cases you willfind some reason todoubtthatthecorrelationbetween your intuitive

judgmentandthetruthisperfect,andyouwillendup somewhere betweenthetwopoles.

This procedure is an

approximation of the likelyresults of an appropriatestatistical analysis. Ifsuccessful, it will move youtoward unbiased predictions,reasonable assessments ofprobability, and moderate

predictions of numericaloutcomes. The twoprocedures are intended toaddress the same bias:intuitive predictions tend tobe overconfident and overlyextreme.

Correcting your intuitivepredictions is a task forSystem2.Significanteffortisrequired to find the relevantreference category, estimate

the baseline prediction, andevaluate the quality of theevidence. The effort isjustifiedonlywhenthestakesare high and when you areparticularlykeennottomakemistakes. Furthermore, youshould know that correctingyour intuitions maycomplicate your life. Acharacteristic of unbiasedpredictionsisthattheypermitthe prediction of rare orextremeeventsonlywhenthe

information is very good. Ifyou expect your predictionstobeofmodestvalidity, youwill never guess an outcomethat is either rareor far fromthemean. If your predictionsare unbiased, you will neverhavethesatisfyingexperienceof correctly calling anextremecase.Youwillneverbeabletosay,“Ithoughtso!”whenyourbeststudentinlawschool becomes a SupremeCourtjustice,orwhenastart-

up that you thought verypromising eventuallybecomesamajor commercialsuccess.Giventhelimitationsof the evidence, you willnever predict that anoutstanding high schoolstudent will be a straight-Astudent at Princeton. For thesame reason, a venturecapitalist will never be toldthattheprobabilityofsuccessfor a start-up in its earlystagesis“veryhigh.”

The objections to theprinciple of moderatingintuitive predictions must betaken seriously, becauseabsenceofbias isnotalwayswhat matters most. Apreference for unbiasedpredictions is justified if allerrors of prediction aretreated alike, regardless oftheir direction. But there aresituations in which one typeof error is much worse thananother. When a venture

capitalist looks for “the nextbigthing,”theriskofmissingthenextGoogleorFacebookisfarmoreimportantthantherisk of making a modestinvestment in a start-up thatultimately fails. The goal ofventure capitalists is to callthe extreme cases correctly,even at the cost ofoverestimating the prospectsofmanyotherventures.Foraconservative banker makinglarge loans, the risk of a

single borrower goingbankrupt may outweigh therisk of turning down severalwould-be clients who wouldfulfill their obligations. Insuchcases,theuseofextremelanguage (“very goodprospect,” “serious risk ofdefault”) may have somejustificationforthecomfortitprovides, even if theinformation on which thesejudgments are based is ofonlymodestvalidity.

For a rational person,predictions that are unbiasedand moderate should notpresent a problem. After all,the rational venture capitalistknows that even the mostpromisingstart-upshaveonlyamoderatechanceofsuccess.Sheviewsher job aspickingthemostpromisingbetsfromthebetsthatareavailableanddoes not feel the need todelude herself about theprospects of a start-up in

which she plans to invest.Similarly,rationalindividualspredicting the revenue of afirm will not be bound to asingleys р number—theyshould consider the range ofuncertainty around the mostlikely outcome. A rationalpersonwillinvestalargesumin an enterprise that is mostlikelytofailiftherewardsofsuccess are large enough,without deluding herselfabout thechancesofsuccess.

However, we are not allrational, andsomeofusmayneed the securityofdistortedestimates to avoid paralysis.If you choose to deludeyourselfbyacceptingextremepredictions, however, youwill dowell to remain awareofyourself-indulgence.Perhaps the most valuable

contribution of the correctiveprocedures I propose is thattheywillrequireyoutothinkabouthowmuchyouknow.I

will use an example that isfamiliar in the academicworld, but the analogies toother spheres of life areimmediate. A department isabout to hire a youngprofessorandwantstochoosethe one whose prospects forscientificproductivity are thebest. The search committeehas narrowed down thechoicetotwocandidates:

Kim recently completed

her graduate work. Herrecommendations arespectacularandshegavea brilliant talk andimpressed everyone inher interviews. She hasno substantial trackrecord of scientificproductivity.

Jane has held apostdoctoral position forthe last three years. She

hasbeenveryproductiveand her research recordis excellent, but her talkand interviewswere lesssparklingthanKim’s.

The intuitive choice favorsKim, because she left astronger impression, andWYSIATI. But it is also thecase that there is much lessinformation about Kim thanabout Jane. We are back tothe lawof small numbers. In

effect, you have a smallersample of information fromKim than from Jane, andextreme outcomes are muchmorelikelytobeobservedinsmallsamples.There ismoreluckintheoutcomesofsmallsamples, and you shouldtherefore regress yourprediction more deeplytoward the mean in yourprediction of Kim’s futureperformance. When youallowfor thefact thatKimis

likely to regress more thanJane, you might end upselecting Jane although youwere less impressed by her.In the context of academicchoices, I would vote forJane, but it would be astruggle to overcome myintuitive impression thatKimismorepromising.Followingourintuitionsismorenatural,and somehowmore pleasant,thanactingagainstthem.You can readily imagine

similar problems in differentcontexts, such as a venturecapitalist choosing betweeninvestments in two start-upsthat operate in differentmarkets. One start-up has aproduct for which demandcan be estimated with fairprecision. The othercandidate is more excitingandintuitivelypromising,butits prospects are less certain.Whetherthebestguessaboutthe prospects of the second

start-up isstill superiorwhenthe uncertainty is factored inis a question that deservescarefulconsideration.

ATwo-SystemsViewofRegression

Extreme predictions and awillingness to predict rareevents from weak evidenceare both manifestations ofSystem1.Itisnaturalfortheassociative machinery to

match the extremeness ofpredictions to the perceivedextremeness of evidence onwhichitisbased—thisishowsubstitution works. And it isnatural for System 1 togenerate overconfidentjudgments, becauseconfidence, aswe have seen,is determined by thecoherence of the best storyyou can tell from theevidenceathand.Bewarned:your intuitions will deliver

predictions that are tooextreme and you will beinclinehe рd to put far toomuchfaithinthem.Regressionisalsoaproblem

for System 2. The very ideaof regression to the mean isalien and difficult tocommunicate andcomprehend. Galton had ahard time before heunderstood it.Manystatisticsteachers dread the class inwhich the topic comes up,

and their students often endup with only a vagueunderstanding of this crucialconcept.ThisisacasewhereSystem 2 requires specialtraining. Matchingpredictions to theevidence isnot only something we dointuitively; it also seems areasonable thing to do. Wewill not learn to understandregression from experience.Even when a regression isidentified, as we saw in the

storyof theflight instructors,it will be given a causalinterpretation that is almostalwayswrong.

SpeakingofIntuitivePredictions

“That start-up achievedan outstanding proof ofconcept, but weshouldn’texpectthemtodo aswell in the future.Theyarestillalongway

from the market andthereisalotofroomforregression.”

“Our intuitivepredictionis very favorable, but itis probably too high.Let’s take into accountthe strength of ourevidenceandregress theprediction toward themean.”

“The investmentmaybea good idea, even if thebest guess is that itwillfail. Let's not say wereally believe it is thenextGoogle.”

“I read one review ofthat brand and it wasexcellent. Still, thatcouldhavebeenafluke.Let’s consider only thebrands that have a large

number of reviews andpick the one that looksbest.”

P

Part3

P

OverconfidenceP

TheIllusionofUnderstanding

The trader-philosopher-statistician Nassim Talebcould also be considered apsychologist. In The BlackSwan, Taleb introduced thenotion of a narrative fallacyto describe how flawedstories of the past shape ourviews of the world and our

expectations for the future.Narrative fallacies ariseinevitably from ourcontinuous attempt to makesense of the world. Theexplanatory stories thatpeople find compelling aresimple; are concrete ratherthan abstract; assign a largerrole to talent, stupidity, andintentions than to luck; andfocusonafewstrikingeventsthat happened rather than onthe countless events that

failed to happen. Any recentsalienteventisacandidatetobecomethekernelofacausalnarrative.Taleb suggests thatwe humans constantly foolourselves by constructingflimsy accounts of the pastandbelievingtheyaretrue.Good stories provide a

simple and coherent account>A compelling narrative

fosters an illusion ofinevitability. Consider the

story of how Google turnedintoagiantofthetechnologyindustry. Two creativegraduate students in thecomputer science departmentat Stanford University comeup with a superior way ofsearching information on theInternet. They seek andobtain funding to start acompanyandmakeaseriesofdecisions thatwork outwell.Within a few years, thecompany they started is one

ofthemostvaluablestocksinAmerica, and the two formergraduate students are amongthe richest people on theplanet. On one memorableoccasion, they were lucky,which makes the story evenmorecompelling:ayearafterfounding Google, they werewilling to sell their companyfor less than $1 million, butthe buyer said the price wastoo high. Mentioning thesingle lucky incident actually

makes it easier tounderestimate the multitudeof ways in which luckaffectedtheoutcome.A detailed history would

specify the decisions ofGoogle’s founders, but forourpurposesitsufficestosaythatalmosteverychoicetheymadehadagoodoutcome.Amore complete narrativewoulddescribetheactionsofthe firms that Googledefeated. The hapless

competitors would appear tobeblind,slow,andaltogetherinadequateindealingwiththethreat that eventuallyoverwhelmedthem.I intentionally told this tale

blandly,butyougettheidea:there is a very good storyhere. Fleshed out in moredetail, the story could giveyou the sense that youunderstand what madeGooglesucceed;itwouldalsomake you feel that you have

learned a valuable generallesson about what makesbusinesses succeed.Unfortunately, there is goodreason to believe that yoursense of understanding andlearning from the Googlestory is largely illusory. Theultimate test of anexplanation is whether itwould have made the eventpredictable in advance. Nostory of Google’s unlikelysuccess will meet that test,

because no story can includethe myriad of events thatwouldhavecausedadifferentoutcome. The human minddoes not deal well withnonevents.Thefactthatmanyof the important events thatdid occur involve choicesfurther tempts you toexaggerate the role of skilland underestimate the partthat luck played in theoutcome. Because everycritical decision turned out

well, the record suggestsalmost flawless prescience—but bad luck could havedisrupted any one of thesuccessful steps. The haloeffect adds the final touches,lending an aura ofinvincibility to the heroes ofthestory.Like watching a skilled

rafter avoiding one potentialcalamity after another as hegoes down the rapids, theunfoldingoftheGooglestory

is thrilling because of theconstant risk of disaster.However, there is foр aninstructivedifferencebetweenthe two cases. The skilledrafter has gone down rapidshundreds of times. He haslearned to read the roilingwater in front of him and toanticipate obstacles. He haslearned to make the tinyadjustments of posture thatkeep him upright. There arefeweropportunitiesforyoung

men to learnhow to create agiant company, and fewerchancestoavoidhiddenrocks—such as a brilliantinnovation by a competingfirm. Of course there was agreat deal of skill in theGooglestory,butluckplayeda more important role in theactual event than it does inthetellingofit.Andthemoreluck was involved, the lessthereistobelearned.At work here is that

powerful WY SIATI rule.Youcannothelpdealingwiththe limited information youhave as if itwere all there isto know. You build the bestpossible story from theinformation available to you,and if it is a good story, youbelieve it.Paradoxically, it iseasier toconstructacoherentstory when you know little,when there are fewer piecesto fit into the puzzle. Ourcomforting conviction that

the world makes sense restson a secure foundation: ouralmost unlimited ability toignoreourignorance.I have heard of too many

people who “knew wellbefore it happened that the2008 financial crisis wasinevitable.” This sentencecontains a highlyobjectionable word, whichshould be removed from ourvocabulary in discussions ofmajorevents.Thewordis,of

course, knew. Some peoplethought well in advance thatthere would be a crisis, butthey did not know it. Theynowsaytheyknewitbecausethe crisis did in fact happen.This is a misuse of animportant concept. Ineveryday language,we applythe word know only whenwhat was known is true andcanbe shown tobe true.Wecanknowsomethingonlyifitis both true and knowable.

But the people who thoughtthere would be a crisis (andthere are fewer of them thannow remember thinking it)could not conclusively showit at the time. Manyintelligent and well-informedpeoplewerekeenlyinterestedin the future of the economyand did not believe acatastrophe was imminent; Iinfer from this fact that thecrisis was not knowable.What is perverse about the

useofknowinthiscontextisnot that some individuals getcreditforpresciencethattheydo not deserve. It is that thelanguage implies that theworld ismoreknowable thanit is. It helps perpetuate aperniciousillusion.The core of the illusion is

that we believe weunderstand the past, whichimplies that the future alsoshould be knowable, but infact we understand the past

less than we believe we do.Know is not the only wordthat fosters this illusion. Incommon usage, the wordsintuitionandpremonitionalsoarereservedforpastthoughtsthatturnedouttobetrue.Thestatement “I had apremonitionthatthemarriagewould not last, but I waswrong” sounds odd, as doesany sentence about anintuitionthatturnedouttobefalse. To think clearly about

the future, we need to cleanup the language that we useinlabelingthebeliefswehadinthepast.

TheSocialCostsofHindsight

The mind that makes upnarratives about the past is asense-making organ. Whenan unpredicted event occurs,we immediately adjust ourview of the world to

accommodate the surprise.Imagine yourself before afootball game between twoteams that have the samerecord of wins and losses.Now the game is over, andoneteamtrashedtheother.Inyour revised model of theworld, the winning team ismuchstronger than the loser,and your view of the past aswellasofthefuturehasbeenaltered be fрy that newperception. Learning from

surprisesisareasonablethingto do, but it can have somedangerousconsequences.A general limitation of the

human mind is its imperfectability to reconstruct paststates of knowledge, orbeliefs that have changed.Once you adopt a new viewoftheworld(orofanypartofit), you immediately losemuchofyourabilitytorecallwhat you used to believebeforeyourmindchanged.

Many psychologists havestudied what happens whenpeople change their minds.Choosing a topic on whichminds are not completelymade up—say, the deathpenalty—the experimentercarefully measures people’sattitudes. Next, theparticipants see or hear apersuasive pro or conmessage. Then theexperimenter measurespeople’s attitudes again; they

usually are closer to thepersuasivemessagetheywereexposed to. Finally, theparticipantsreporttheopinionthey held beforehand. Thistask turns out to besurprisingly difficult. Askedto reconstruct their formerbeliefs, people retrieve theircurrent ones instead—aninstance of substitution—andmanycannotbelievethattheyeverfeltdifferently.Yourinabilitytoreconstruct

past beliefs will inevitablycause you to underestimatetheextent towhichyouweresurprised by past events.Baruch Fischh off firstdemonstrated this “I-knew-it-all-along”effect,orhindsightbias, when he was a studentin Jerusalem. Together withRuth Beyth (another of ourstudents), Fischh offconducted a survey beforePresident Richard Nixonvisited China and Russia in

1972. The respondentsassigned probabilities tofifteen possible outcomes ofNixon’s diplomaticinitiatives. Would MaoZedong agree to meet withNixon? Might the UnitedStates grant diplomaticrecognition to China? Afterdecades of enmity, could theUnited States and the SovietUnion agree on anythingsignificant?After Nixon’s return from

his travels, Fischh off andBeythasked the samepeopleto recall the probability thatthey had originally assignedtoeachofthefifteenpossibleoutcomes. The results wereclear.Ifaneventhadactuallyoccurred, people exaggeratedthe probability that they hadassigned to it earlier. If thepossible event had not cometo pass, the participantserroneouslyrecalledthattheyhad always considered it

unlikely.Furtherexperimentsshowed that people weredriven to overstate theaccuracy not only of theiroriginal predictions but alsoof those made by others.Similar results have beenfound for other events thatgrippedpublicattention,suchas theO. J. Simpsonmurdertrial and the impeachment ofPresident Bill Clinton. Thetendencytorevisethehistoryof one’s beliefs in light of

what actually happenedproduces a robust cognitiveillusion.Hindsight bias has

pernicious effects on theevaluations of decisionmakers. It leads observers toassess the quality of adecision not by whether theprocess was sound but bywhether its outcome wasgoodorbad.Consideralow-risk surgical intervention inwhich an unpredictable

accidentoccurred thatcausedthe patient’s death. The jurywillbepronetobelieve,afterthe fact, that the operationwas actually risky and thatthe doctor who ordered itshould have known better.This outcome bias makes italmostimpossibletoevaluateadecisionproperly—intermsof the beliefs that werereasonablewhen thedecisionwasmade.Hindsight is especially

unkind to decision makerswho act as agents for others—physicians, financialadvisers, third-base coaches,CEOs, social workers,diplomats,politicians.Weareprone to blame decisionmakers for good decisionsthatworkedoutbadly and togive them too littlecredit forsuccessful movesecaр thatappearobviousonlyafter thefact.Thereisaclearoutcomebias.When theoutcomes are

bad, the clients often blametheiragentsfornotseeingthehandwriting on the wall—forgetting that it was writtenin invisible ink that becamelegible only afterward.Actions that seemed prudentin foresight can lookirresponsibly negligent inhindsight.Basedonanactuallegal case, students inCalifornia were askedwhether the city of Duluth,Minnesota, should have

shouldered the considerablecost of hiring a full-timebridge monitor to protectagainst the risk that debrismight get caught and blockthe free flow of water. Onegroup was shown only theevidenceavailableatthetimeofthecity’sdecision;24%ofthese people felt that Duluthshouldtakeontheexpenseofhiring a flood monitor. Thesecond group was informedthat debris had blocked the

river, causing major flooddamage;56%ofthesepeoplesaid the city should havehired the monitor, althoughthey had been explicitlyinstructednottolethindsightdistorttheirjudgment.Theworsetheconsequence,

thegreaterthehindsightbias.In the case of a catastrophe,such as 9/11, we areespecially ready to believethattheofficialswhofailedtoanticipateitwerenegligentor

blind. On July 10, 2001, theCentral Intelligence Agencyobtained information that al-Qaeda might be planning amajor attack against theUnited States. George Tenet,director of the CIA, broughtthe information not toPresident George W. Bushbut to National SecurityAdviser Condoleezza Rice.Whenthefactslateremerged,Ben Bradlee, the legendaryexecutive editor of The

Washington Post, declared,“It seems to me elementarythat if you’ve got the storythat’s going to dominatehistory youmight aswell gorighttothepresident.”ButonJuly 10, no one knew—orcould have known—that thistidbit of intelligence wouldturnouttodominatehistory.Because adherence to

standardoperatingproceduresis difficult to second-guess,decision makers who expect

to have their decisionsscrutinizedwithhindsightaredriven to bureaucraticsolutions—and toanextremereluctance to take risks. Asmalpractice litigationbecamemore common, physicianschanged their procedures inmultiple ways: ordered moretests, referred more cases tospecialists, appliedconventional treatments evenwhen they were unlikely tohelp.These actions protected

thephysiciansmorethantheybenefited the patients,creating the potential forconflictsofinterest.Increasedaccountability is a mixedblessing.Although hindsight and the

outcomebiasgenerally fosterrisk aversion, they alsobringundeserved rewards toirresponsible risk seekers,such as a general or anentrepreneur who took acrazy gamble and won.

Leaderswhohavebeenluckyareneverpunishedforhavingtaken toomuch risk. Instead,theyarebelievedtohavehadthe flair and foresight toanticipate success, and thesensible people who doubtedthemareseen inhindsightasmediocre,timid,andweak.Afewluckygamblescancrowna reckless leader with a haloofprescienceandboldness.

RecipesforSuccess

The sense-makingmachineryofSystem1makesusseetheworld as more tidy, simple,predictable,andcoherentthanit really is. The illusion thatone has understood the pastfeeds the further illusion thatone can predict and controlthefuture.Theseillusionsarecomforting. They reduce theanxiety that we wouldexperience if we allowedourselves to fully

acknowledge theuncertaintiesofexistence.Weall have a need for thereassuring message thatactions have appropriateconsequences, and thatsuccess will reward wisdomand courage. Manybdecрusinessbooksaretailor-madetosatisfythisneed.Doleadersandmanagement

practices influence theoutcomes of firms in themarket? Of course they do,

and the effects have beenconfirmed by systematicresearch that objectivelyassessedthecharacteristicsofCEOsandtheirdecisions,andrelated them to subsequentoutcomes of the firm. In onestudy, the CEOs werecharacterized by the strategyofthecompaniestheyhadledbefore their currentappointment, as well as bymanagement rules andproceduresadoptedaftertheir

appointment. CEOs doinfluence performance, butthe effects are much smallerthanareadingofthebusinesspresssuggests.Researchers measure the

strengthof relationshipsbyacorrelation coefficient, whichvaries between 0 and 1. Thecoefficient was definedearlier (in relation toregressiontothemean)bytheextenttowhichtwomeasuresare determined by shared

factors. A very generousestimate of the correlationbetween the success of thefirm and the quality of itsCEOmightbeashighas.30,indicating 30% overlap. Toappreciate the significanceofthis number, consider thefollowingquestion:

Suppose you considermanypairsoffirms.Thetwo firms in each pairaregenerallysimilar,but

theCEOofoneof themis better than the other.Howoftenwillyoufindthat the firm with thestronger CEO is themore successful of thetwo?

In a well-ordered andpredictable world, thecorrelation would be perfect(1), and the stronger CEOwould be found to lead themoresuccessfulfirmin100%

of the pairs. If the relativesuccess of similar firms wasdeterminedentirelybyfactorsthattheCEOdoesnotcontrol(call themluck, ifyouwish),you would find the moresuccessful firm led by theweakerCEO50%ofthetime.A correlation of .30 impliesthat you would find thestronger CEO leading thestrongerfirminabout60%ofthepairs—animprovementofa mere 10 percentage points

overrandomguessing,hardlygrist for the hero worship ofCEOswesooftenwitness.Ifyouexpectedthisvalueto

behigher—andmostofusdo—then you should take thatas an indication that you areprone to overestimate thepredictability of the worldyoulivein.Makenomistake:improving the odds ofsuccess from 1:1 to 3:2 is avery significant advantage,both at the racetrack and in

business. From theperspective of most businesswriters,however,aCEOwhohas so little control overperformance would not beparticularly impressive evenif her firm did well. It isdifficult to imagine peopleliningupatairportbookstoresto buy a book thatenthusiastically describes thepractices of business leaderswho, on average, dosomewhatbetterthanchance.

Consumershaveahungerfora clear message about thedeterminants of success andfailure in business, and theyneedstoriesthatofferasenseof understanding, howeverillusory.Inhis penetratingbookTheHalo Effect, PhilipRosenzweig, a businessschool professor based inSwitzerland, shows how thedemand for illusory certaintyismet in two popular genres

of business writing: historiesof the rise (usually) and fall(occasionally) of particularindividuals and companies,and analyses of differencesbetween successful and lesssuccessful firms. Heconcludes that stories ofsuccess and failureconsistently exaggerate theimpactofleadershipstyleandmanagement practices onfirmoutcomes,andthustheirmessageisrarelyuseful.

Toappreciatewhatisgoingon, imagine that businessexperts, such as other CEOs,are asked to commenton thereputation of the chiefexecutiveofacompany.Theypoрare keenly aware ofwhether the company hasrecently been thriving orfailing. Aswe saw earlier inthe case of Google, thisknowledge generates a halo.The CEO of a successfulcompanyislikelytobecalled

flexible, methodical, anddecisive. Imagine that a yearhas passed and things havegone sour. The sameexecutiveisnowdescribedasconfused, rigid, andauthoritarian. Bothdescriptionssoundrightatthetime: it seems almost absurdto call a successful leaderrigid and confused, or astruggling leader flexible andmethodical.Indeed, thehaloeffect isso

powerful that you probablyfind yourself resisting theideathatthesamepersonandthe same behaviors appearmethodical when things aregoing well and rigid whenthings are going poorly.Becauseofthehaloeffect,weget the causal relationshipbackward: we are prone tobelieve that the firm failsbecause its CEO is rigid,when the truth is that theCEO appears to be rigid

because the firm is failing.This is how illusions ofunderstandingareborn.Thehaloeffectandoutcome

bias combine to explain theextraordinaryappealofbooksthat seek to draw operationalmorals from systematicexamination of successfulbusinesses. One of the best-knownexamplesofthisgenreis Jim Collins and Jerry I.Porras’s Built to Last. Thebook contains a thorough

analysis of eighteen pairs ofcompeting companies, inwhich one was moresuccessfulthantheother.Thedata for these comparisonsare ratingsofvariousaspectsofcorporateculture,strategy,and management practices.“We believe every CEO,manager, andentrepreneur inthe world should read thisbook,” the authors proclaim.“You can build a visionarycompany.”

The basic message ofBuiltto Last and other similarbooksisthatgoodmanagerialpractices can be identifiedand that good practices willbe rewarded by good results.Bothmessagesareoverstated.Thecomparisonof firms thathave been more or lesssuccessful is to a significantextent a comparison betweenfirms thathavebeenmoreorless lucky. Knowing theimportance of luck, you

should be particularlysuspicious when highlyconsistent patterns emergefrom the comparison ofsuccessfulandlesssuccessfulfirms. In the presence ofrandomness, regular patternscanonlybemirages.Because luck plays a large

role, thequalityofleadershipand management practicescannot be inferred reliablyfromobservationsofsuccess.And even if you had perfect

foreknowledge that a CEOhas brilliant vision andextraordinary competence,you still would be unable topredicthowthecompanywillperform with much betteraccuracy than the flip of acoin. On average, the gap incorporate profitability andstock returns between theoutstandingfirmsandthelesssuccessful firms studied inBuilttoLastshranktoalmostnothing in the period

following the study. Theaverage profitability of thecompanies identified in thefamous In Search ofExcellence dropped sharplyaswellwithinashorttime.Astudy of Fortune’s “MostAdmired Companies” findsthat over a twenty-yearperiod, the firms with theworstratingswentontoearnmuch higher stock returnsthanthemostadmiredfirms.Youareprobablytemptedto

think of causal explanationsfor these observations:perhaps the successful firmsbecame complacent, the lesssuccessful firms tried harder.But this is thewrongway tothink about what happened.Theaveragegapmustshrink,because the original gapwasdue in good part to luck,whichcontributedbothtothesuccess of the top firms andtothelaggingperformanceofthe rest. We have already

encountered this statisticalfact of life: regression to themean.Stories of how businesses

rise and fall strike a chordwithreadersbyofferingwhatthe human mind needs: asimple message of triumphand failure that identifiesclear causes and ignores thedeterminative power of luckand the inevitability ofregression. These storiesinduce and maintain an

illusion of understanding,imparting lessons of littleenduring value to readerswho are all too eager tobelievethem.

SpeakingofHindsight

“The mistake appearsobvious, but it is justhindsight.Youcouldnothave known inadvance.”

“He’slearningtoomuchfrom this success story,whichistootidy.Hehasfallen for a narrativefallacy.”

“Shehasnoevidenceforsaying that the firm isbadly managed. All sheknows is that its stockhas gone down. This isan outcome bias, part

hindsight and part haloeffect.”

“Let’s not fall for theoutcomebias.Thiswasastupid decision eventhough it worked outwell.”

P

TheIllusionofValidity

System1isdesignedtojumpto conclusions from littleevidence—and it is notdesigned to know the size ofits jumps. Because ofWYSIATI,only theevidenceat hand counts. Because ofconfidence by coherence, thesubjective confidence we

have in our opinions reflectsthecoherenceofthestorythatSystem1 andSystem2haveconstructed. The amount ofevidence and its quality donot count for much, becausepoor evidence can make averygoodstory.Forsomeofourmostimportantbeliefswehave no evidence at all,except that people we loveand trust hold these beliefs.Considering how little weknow, the confidence we

have in our beliefs ispreposterous—and it is alsoessential.

TheIllusionofValidity

Many decades ago I spentwhatseemedlikeagreatdealof time under a scorchingsun, watching groups ofsweatysoldiersastheysolveda problem. I was doing mynational service in the Israeli

Army at the time. I hadcompleted an undergraduatedegree in psychology, andafter a year as an infantryofficer was assigned to thearmy’s Psychology Branch,where one of my occasionalduties was to help evaluatecandidates for officertraining. We used methodsthat had been developed bythe British Army in WorldWarII.One test, called the

“leaderless group challenge,”wasconductedonanobstaclefield. Eight candidates,strangers to each other, withall insignia of rank removedand only numbered tags toidentifythem,wereinstructedto lift a long log from theground and haul it to a wallaboutsixfeethigh.Theentiregrouphad to get to theotherside of the wall without thelog touching either theground or the wall, and

without anyone touching thewall. If any of these thingshappened,theyhadtodeclareitsigрЉTandstartagain.There was more than one

way to solve the problem.Acommonsolutionwasfor theteam to send several men tothe other side by crawlingoverthepoleasitwasheldatan angle, like a giant fishingrod,byothermembersof thegroup. Or else some soldierswould climb onto someone’s

shoulders and jump across.Thelastmanwouldthenhaveto jump up at the pole, heldup at an angle by the rest ofthe group, shinny his wayalong its length as the otherskept him and the polesuspendedintheair,andleapsafely to the other side.Failure was common at thispoint,whichrequiredthemtostartalloveragain.As a colleague and I

monitored the exercise, we

made note of who tookcharge,who tried to lead butwas rebuffed, howcooperative each soldier wasin contributing to the groupeffort. We saw who seemedto be stubborn, submissive,arrogant, patient, hot-tempered, persistent, or aquitter. We sometimes sawcompetitive spite whensomeone whose idea hadbeenrejectedbythegroupnolongerworkedveryhard.And

we saw reactions to crisis:who berated a comradewhosemistakehadcausedthewhole group to fail, whosteppedforwardtoleadwhenthe exhausted team had tostartover.Underthestressoftheevent,wefelt,eachman’strue nature revealed itself.Our impression of eachcandidate’s character was asdirect and compelling as thecolorofthesky.After watching the

candidates make severalattempts, we had tosummarizeourimpressionsofsoldiers’ leadership abilitiesand determine, with anumerical score, who shouldbe eligible for officertraining.Wespentsometimediscussing each case andreviewing our impressions.The task was not difficult,because we felt we hadalready seen each soldier’sleadershipskills.Someofthe

men had looked like strongleaders, others had seemedlikewimpsor arrogant fools,others mediocre but nothopeless.Quite a few lookedso weak that we ruled themout as candidates for officerrank. When our multipleobservations of eachcandidate converged on acoherent story, we werecompletely confident in ourevaluationsandfeltthatwhatwe had seen pointed directly

tothefuture.Thesoldierwhotookoverwhenthegroupwasin trouble and led the teamover thewallwasa leader atthat moment. The obviousbest guess about how hewould do in training, or incombat,wasthathewouldbeas effective then as he hadbeen at the wall. Any otherprediction seemedinconsistentwiththeevidencebeforeoureyes.Because our impressions of

how well each soldier hadperformed were generallycoherentandclear,ourformalpredictions were just asdefinite. A single scoreusuallycametomindandwerarely experienced doubts orformed conflictingimpressions. We were quitewilling to declare, “This onewill never make it,” “Thatfellow is mediocre, but heshoulddookay,”or“Hewillbeastar.”Wefeltnoneedto

question our forecasts,moderate them, orequivocate. If challenged,however,wewerepreparedtoadmit, “But of courseanything could happen.” Wewere willing to make thatadmission because, despiteour definite impressionsabout individual candidates,we knew with certainty thatour forecasts were largelyuseless.The evidence thatwe could

not forecast successaccurately wasoverwhelming. Every fewmonths we had a feedbacksession in which we learnedhowthecadetsweredoingatthe officer-training schooland could compare ourassessments against theopinionsofcommanderswhohadbeenmonitoringthemforsome time. The story wasalways the same: our abilityto predict performance at the

school was negligible. Ourforecasts were better thanblind guesses, but not bymuch.Weweedဆredowncastfor

a while after receiving thediscouraging news. But thiswas the army.Useful or not,there was a routine to befollowed and orders to beobeyed. Another batch ofcandidates arrived the nextday. We took them to theobstacle field,wefaced them

with the wall, they lifted thelog,andwithinafewminuteswe saw their true naturesrevealed,asclearlyasbefore.The dismal truth about thequalityofourpredictionshadno effectwhatsoever on howwe evaluated candidates andvery little effect on theconfidence we felt in ourjudgments and predictionsaboutindividuals.What happened was

remarkable. The global

evidence of our previousfailure should have shakenour confidence in ourjudgments of the candidates,but it did not. It should alsohave caused us to moderateourpredictions,butitdidnot.We knew as a general factthatourpredictionswerelittlebetter than random guesses,butwe continued to feel andact as if each of our specificpredictions was valid. I wasreminded of theMüller-Lyer

illusion, in which we knowthe lines are of equal lengthyet still see them as beingdifferent. I was so struck bythe analogy that I coined aterm for our experience: theillusionofvalidity.I had discovered my first

cognitiveillusion.

Decadeslater,Icanseemanyof the central themes of mythinking—andofthisbook—

in that old story. Ourexpectations for the soldiers’future performance were aclear instanceof substitution,and of the representativenessheuristicinparticular.Havingobserved one hour of asoldier’s behavior in anartificialsituation,wefeltweknewhowwellhewouldfacethe challenges of officertraining and of leadership incombat.Ourpredictionswerecompletely nonregressive—

wehadnoreservationsaboutpredicting failure oroutstanding success fromweak evidence. This was aclear instance of WYSIATI.We had compellingimpressions of the behaviorwe observed and no goodway to represent ourignorance of the factors thatwould eventually determinehowwellthecandidatewouldperformasanofficer.Looking back, the most

striking part of the story isthat our knowledge of thegeneral rule—that we couldnotpredict—hadnoeffectonour confidence in individualcases. Icanseenow thatourreactionwassimilartothatofNisbett and Borgida’sstudentswhen theywere toldthatmostpeopledidnothelpastrangersufferingaseizure.They certainly believed thestatistics they were shown,but the base rates did not

influence their judgment ofwhether an individual theysaw on the video would orwould not help a stranger.Just as Nisbett and Borgidashowed, people are oftenreluctant to infer theparticularfromthegeneral.Subjective confidence in a

judgment is not a reasonedevaluation of the probabilitythat this judgment is correct.Confidence is a feeling,which reflects the coherence

of the information and thecognitive ease of processingit. It is wise to takeadmissions of uncertaintyseriously, but declarations ofhigh confidence mainly tellyou that an individual hasconstructed a coherent storyin his mind, not necessarilythatthestoryistrue.

TheIllusionofStock-PickingSkill

In1984,AmosandIandourfriend Richard Thaler visitedaWallStreetfirm.Ourhost,asenior investment manager,had invited us to discuss therole of judgment biases ininvesting. I knew so littleabout finance that I did noteven knowwhat to ask him,but I remember oneexchange. “When you sell astock,” d nဆI asked, “whobuysit?”Heansweredwitha

wave in the vague directionofthewindow,indicatingthathe expected the buyer to besomeoneelseverymuch likehim. That was odd: Whatmadeonepersonbuyandtheother sell? What did thesellers think they knew thatthebuyersdidnot?Since then, my questions

about the stock market havehardenedintoalargerpuzzle:amajorindustryappearstobebuilt largelyonan illusionof

skill. Billions of shares aretraded every day, withmanypeoplebuyingeachstockandothersselling it to them.It isnotunusualformorethan100million shares of a singlestock tochangehands inoneday. Most of the buyers andsellers know that they havethe same information; theyexchangethestocksprimarilybecause they have differentopinions. The buyers thinkthepriceistoolowandlikely

torise,whilethesellersthinkthepriceishighandlikelytodrop. The puzzle is whybuyersandsellersalike thinkthat the current price iswrong. What makes thembelieve they know moreabout what the price shouldbe than themarketdoes?Formostofthem,thatbeliefisanillusion.In its broad outlines, the

standard theory of how thestock market works is

accepted by all theparticipants in the industry.Everybody in the investmentbusiness has read BurtonMalkiel’s wonderful book ARandom Walk Down WallStreet. Malkiel’s central ideais that a stock’s priceincorporates all the availableknowledgeaboutthevalueofthe company and the bestpredictions about the futureof the stock. If some peoplebelieve that the price of a

stock will be highertomorrow,theywillbuymoreofit today.This,inturn,willcause its price to rise. If allassets in a market arecorrectly priced, no one canexpect either to gain or tolosebytrading.Perfectpricesleavenoscopeforcleverness,but they also protect foolsfromtheirownfolly.Wenowknow, however, that thetheory is not quite right.Many individual investors

lose consistently by trading,an achievement that a dart-throwing chimp could notmatch. The firstdemonstrationofthisstartlingconclusion was collected byTerry Odean, a financeprofessor at UC Berkeleywhowasoncemystudent.Odean began by studying

the trading recordsof 10,000brokerage accounts ofindividual investors spanninga seven-year period. He was

able to analyze everytransaction the investorsexecuted through that firm,nearly 163,000 trades. Thisrich set of data allowedOdean to identify allinstancesinwhichaninvestorsold some of his holdings inonestockandsoonafterwardbought another stock. Bythese actions the investorrevealed that he (most of theinvestors were men) had adefinite idea about the future

ofthetwostocks:heexpectedthestockthathechosetobuytodobetter thanthestockhechosetosell.Todeterminewhether those

ideas were well founded,Odean compared the returnsof the stock the investor hadsold and the stock he hadbought in its place, over thecourse of one year after thetransaction. The results wereunequivocally bad. Onaverage, the shares that

individual traders sold didbetterthanthosetheybought,byavery substantialmargin:3.2 percentage points peryear, above and beyond thesignificant costs of executingthetwotrades.It is important to remember

that this is a statement aboutaverages: some individualsdid much better, others didmuch worse. However, it isclear that for the largemajority of individual

investors, taking a showerand doing nothing wouldhavebeenabetterpolicythanimplementing the ideas thatcame to their minds. Laterresearch by Odean and hiscolleague Brad Barbersupported this conclusion. Ina paper titled “Trading IsHazardous to Yourt-tဆWealth,”theyshowedthat,onaverage, the most activetraders had the poorestresults, while the investors

who traded the least earnedthehighestreturns.Inanotherpaper, titled “Boys Will BeBoys,”theyshowedthatmenacted on their useless ideassignificantly more often thanwomen, and that as a resultwomen achieved betterinvestmentresultsthanmen.Of course, there is always

someoneon theother sideofeach transaction; in general,thesearefinancialinstitutionsand professional investors,

who are ready to takeadvantage of the mistakesthat individual traders makeinchoosingastocktosellandanother stock to buy.Furtherresearch by Barber andOdeanhasshedlightonthesemistakes.Individualinvestorslike to lock in their gains byselling“winners,” stocks thathave appreciated since theywere purchased, and theyhang on to their losers.Unfortunately for them,

recent winners tend to dobetterthanrecentlosersintheshort run, so individuals sellthe wrong stocks. They alsobuy the wrong stocks.Individual investorspredictably flock tocompanies that draw theirattention because they are inthe news. Professionalinvestors are more selectiveinresponding tonews.Thesefindings provide somejustification for the label of

“smart money” that financeprofessionals apply tothemselves.Although professionals are

able toextractaconsiderableamount of wealth fromamateurs, few stock pickers,if any, have the skill neededto beat the marketconsistently, year after year.Professional investors,includingfundmanagers, failabasictestofskill:persistentachievement. The diagnostic

for the existence of any skillis the consistency ofindividual differences inachievement. The logic issimple: if individualdifferences in any one yearare due entirely to luck, theranking of investors andfundswillvaryerraticallyandthe year-to-year correlationwill be zero. Where there isskill, however, the rankingswill be more stable. Thepersistence of individual

differences is themeasurebywhich we confirm theexistence of skill amonggolfers, car salespeople,orthodontists, or speedy tollcollectorsontheturnpike.Mutual funds are run by

highly experienced andhardworking professionalswho buy and sell stocks toachieve the best possibleresults for their clients.Nevertheless, the evidencefrommorethanfiftyyearsof

research is conclusive: for alarge majority of fundmanagers, the selection ofstocks is more like rollingdice than like playing poker.Typically at least two out ofevery three mutual fundsunderperform the overallmarketinanygivenyear.Moreimportant,theyear-to-

year correlation between theoutcomes of mutual funds isverysmall,barelyhigherthanzero.The successful funds in

any given year are mostlylucky; they have a good rollof the dice. There is generalagreementamongresearchersthat nearly all stock pickers,whethertheyknowitornot—and few of them do—areplaying a game of chance.The subjective experience oftradersisthattheyaremakingsensibleeducatedguessesinasituationofgreatuncertainty.In highly efficient markets,however, educated guesses

are no more accurate thanblindguesses.

Some years ago I had anunusual opportunity toexamine the illusion offinancial skillupclose. Ihadbeen invited to speak to agroup of investment advisersin a firm that providedfinancial advice and otherservices to very wealthyclients.Iaskedforsomedata

to prepare my presentationand was granted a smalltreasure: a spreadsheetsummarizing the investmentoutcomes of some twenty-five anonymous wealthadvisers, for each of eightconsecutive years. Eachadviser’s scoofဆre for eachyear was his (most of themwere men) main determinantofhisyear-endbonus. Itwasa simple matter to rank theadvisersbytheirperformance

ineachyearandtodeterminewhether therewerepersistentdifferences in skill amongthem and whether the sameadvisers consistentlyachieved better returns fortheirclientsyearafteryear.To answer the question, I

computed correlationcoefficients between therankingsineachpairofyears:year 1 with year 2, year 1with year 3, and so on upthrough year 7 with year 8.

That yielded 28 correlationcoefficients,oneforeachpairof years. I knew the theoryand was prepared to findweak evidence of persistenceof skill.Still, Iwas surprisedtofindthattheaverageofthe28 correlations was .01. Inother words, zero. Theconsistent correlations thatwould indicate differences inskill were not to be found.The results resembled whatyou would expect from a

dice-rolling contest, not agameofskill.No one in the firm seemed

to be aware of the nature ofthegamethatitsstockpickerswere playing. The advisersthemselves felt they werecompetent professionalsdoingaseriousjob,andtheirsuperiors agreed. On theevening before the seminar,Richard Thaler and I haddinner with some of the topexecutives of the firm, the

peoplewhodecideonthesizeofbonuses.Weaskedthemtoguess the year-to-yearcorrelation in the rankingsofindividual advisers. Theythought they knewwhat wascoming and smiled as theysaid “not very high” or“performance certainlyfluctuates.”Itquicklybecameclear, however, that no oneexpected the averagecorrelationtobezero.Our message to the

executives was that, at leastwhen it came to buildingportfolios, the firm wasrewarding luck as if it wereskill. This should have beenshockingnewstothem,butitwas not. There was no signthattheydisbelievedus.Howcouldthey?Afterall,wehadanalyzed their own results,and they were sophisticatedenough to see theimplications, which wepolitely refrained from

spelling out.We all went oncalmlywithourdinner,andIhave no doubt that both ourfindings and theirimplications were quicklyswept under the rug and thatlifeinthefirmwentonjustasbefore.Theillusionofskillisnot only an individualaberration; it is deeplyingrainedinthecultureoftheindustry.Facts that challengesuch basic assumptions—andthereby threaten people’s

livelihood and self-esteem—are simplynotabsorbed.Themind does not digest them.This is particularly true ofstatistical studies ofperformance, which providebase-rate information thatpeoplegenerallyignorewhenit clashes with their personalimpressionsfromexperience.The next morning, we

reported the findings to theadvisers, and their responsewasequallybland.Theirown

experience of exercisingcareful judgmentoncomplexproblems was far morecompelling to them than anobscurestatistical fact.Whenwe were done, one of theexecutives I had dined withthe previous evening drovemetotheairport.Hetoldme,withatraceofdefensiveness,“Ihavedoneverywellforthefirmandnoonecantakethatawayfromme.”Ismiledandsaid nothing. But I thought,

“Well, I took it away fromyou this morning. If yoursuccess was due mostly tochance, howmuch credit areyouentitledtotakeforit?”

WhatSupportstheIllusionsofSkilland

Validity?Cognitive illusions can bemore stubborn than visualillusions. What you learnedabouttheMüller-Lyerillusion

did not change the way yousee the lines, but it changedyourbehavior.Younowknowthat you cannot trust yourimpression of the lenglliဆthof lines that have finsappended to them, and youalsoknowthatinthestandardMüller-Lyer display youcannot trust what you see.Whenaskedabout the lengthof the lines, you will reportyour informed belief, not theillusion that you continue to

see. In contrast, when mycolleagues and I in the armylearned that our leadershipassessment tests had lowvalidity,weacceptedthatfactintellectually, but it had noimpact on either our feelingsor our subsequent actions.Theresponseweencounteredinthefinancialfirmwasevenmore extreme. I amconvinced that the messagethatThalerandIdeliveredtoboth the executives and the

portfolio managers wasinstantly put away in a darkcorner of memory where itwouldcausenodamage.Why do investors, both

amateur and professional,stubbornly believe that theycandobetterthanthemarket,contrary to an economictheory that most of themaccept, and contrary to whatthey could learn from adispassionate evaluation oftheir personal experience?

Many of the themes ofprevious chapters come upagain in the explanation ofthe prevalence andpersistence of an illusion ofskillinthefinancialworld.The most potent

psychological cause of theillusion is certainly that thepeople who pick stocks areexercising high-level skills.They consult economic dataand forecasts, they examineincome statements and

balance sheets, they evaluatethe quality of topmanagement, and theyassessthe competition. All this isserious work that requiresextensive training, and thepeople who do it have theimmediate (and valid)experience of using theseskills. Unfortunately, skill inevaluating the businessprospects of a firm is notsufficientforsuccessfulstocktrading, where the key

question is whether theinformation about the firm isalready incorporated in theprice of its stock. Tradersapparently lack the skill toanswer this crucial question,buttheyappeartobeignorantof their ignorance. As I haddiscovered from watchingcadets on the obstacle field,subjective confidence oftraders is a feeling, not ajudgment. Our understandingof cognitive ease and

associative coherence locatessubjective confidence firmlyinSystem1.Finally, the illusions of

validity and skill aresupported by a powerfulprofessional culture. Weknow that people canmaintain an unshakable faithin any proposition, howeverabsurd, when they aresustained by a community oflike-minded believers. Giventheprofessionalcultureofthe

financialcommunity,itisnotsurprising that large numbersof individuals in that worldbelieve themselves to beamong the chosen few whocan do what they believeotherscannot.

TheIllusionsofPundits

The idea that the future isunpredictable is underminedevery day by the ease with

which the past is explained.AsNassimTalebpointedoutin The Black Swan, ourtendency to construct andbelievecoherentnarrativesofthepastmakes itdifficult forus to accept the limitsofourforecasting ability.Everything makes sense inhindsight,afactthatfinancialpunditsexploiteveryeveningas they offer convincingaccounts of the day’s events.And we cannot suppress the

powerful intuition that whatmakes sense in hindsighttoday was predictableyesterday. The illusion thatweunderstandthepastfostersoverconfidence in our abilitytopredictthefuture.Theoften-usedimageofthe

“march of history” impliesorderanddirection.Marches,unlike strolls or walks, arenotrandom.Wethinkthatweshouldbe able to explain thepast by focusing on either

large social movements andcultural and technologicaldevelopments or theintentions and abilities of afewgcoဆreatmen.Theideathatlargehistoricaleventsaredetermined by luck isprofoundly shocking,although it is demonstrablytrue.It ishardtothinkofthehistory of the twentiethcentury, including its largesocial movements, withoutbringingintheroleofHitler,

Stalin, andMaoZedong.Buttherewas amoment in time,just before an egg wasfertilized, when there was afifty-fifty chance that theembryo that became Hitlercould have been a female.Compounding the threeevents, there was aprobabilityofone-eighthofatwentiethcenturywithoutanyofthethreegreatvillainsandit is impossible to argue thathistory would have been

roughly the same in theirabsence. The fertilization ofthese three eggs hadmomentous consequences,and it makes a joke of theidea that long-termdevelopmentsarepredictable.Yet the illusion of valid

prediction remains intact, afact that is exploited bypeople whose business isprediction—not onlyfinancial experts but punditsin business and politics, too.

Television and radio stationsand newspapers have theirpanelsofexpertswhosejobitis to comment on the recentpast and foretell the future.Viewersandreadershavetheimpression that they arereceiving information that issomehow privileged, or atleast extremely insightful.Andthereisnodoubtthatthepundits and their promotersgenuinely believe they areoffering such information.

PhilipTetlock,apsychologistat the University ofPennsylvania,explainedtheseso-calledexpertpredictionsina landmark twenty-yearstudy,which he published inhis 2005 book ExpertPolitical Judgment: HowGood Is It? How Can WeKnow? Tetlock has set theterms for any futurediscussionofthistopic.Tetlock interviewed 284

peoplewhomadetheir living

“commenting or offeringadvice on political andeconomic trends.” He askedthem to assess theprobabilities that certaineventswouldoccurinthenottoo distant future, both inareas of the world in whichthey specialized and inregionsaboutwhichtheyhadless knowledge. WouldGorbachev be ousted in acoup? Would the UnitedStates go to war in the

PersianGulf?Which countrywould become the next bigemerging market? In all,Tetlock gathered more than80,000 predictions. He alsoasked the experts how theyreached their conclusions,how they reacted whenprovedwrong, and how theyevaluated evidence that didnot support their positions.Respondents were asked torate the probabilities of threealternativeoutcomes inevery

case: the persistence of thestatusquo,moreofsomethingsuch as political freedom oreconomic growth, or less ofthatthing.The results were

devastating. The expertsperformed worse than theywould have if they hadsimply assigned equalprobabilities to each of thethree potential outcomes. Inother words, people whospend their time, and earn

their living, studying aparticular topic producepoorer predictions than dart-throwing monkeys whowould have distributed theirchoices evenly over theoptions. Even in the regiontheyknewbest, expertswerenot significantly better thannonspecialists.Those who know more

forecast very slightly betterthan those who know less.But those with the most

knowledge are often lessreliable.Thereasonisthattheperson who acquires moreknowledge develops anenhanced illusionofher skilland becomes unrealisticallyoverconfident.“Wereach thepointofdiminishingmarginalpredictive returns forknowledge disconcertinglyquickly,” Tetlock writes. “Inthis age of academichyperspecialization, there isno reason for supposing that

contributorstotopjournals—distinguished politicalscientists, area studyspecialists, economists, andso on—are any better thanjournalistsorattentivereadersof The New York Times in‘reading&#oulဆ8217;emerging situations.” Themore famous the forecaster,Tetlock discovered, themoreflamboyant the forecasts.“Experts in demand,” hewrites, “were more

overconfident than theircolleagues who eked outexistences far from thelimelight.”Tetlock also found that

expertsresistedadmittingthatthey had been wrong, andwhentheywerecompelledtoadmit error, they had a largecollection of excuses: theyhadbeenwrongonly in theirtiming, an unforeseeableeventhad intervened,or theyhad been wrong but for the

rightreasons.Expertsarejusthuman in the end. They aredazzled by their ownbrilliance and hate to bewrong.Expertsare ledastraynotbywhat theybelieve,butby how they think, saysTetlock. He uses theterminology from IsaiahBerlin’s essay on Tolstoy,“TheHedgehogandtheFox.”Hedgehogs “know one bigthing” and have a theoryabouttheworld;theyaccount

for particular eventswithin acoherent framework, bristlewith impatience toward thosewho don’t see things theirway, and are confident intheir forecasts. They are alsoespecially reluctant to admiterror.Forhedgehogs,afailedprediction is almost always“offonlyontiming”or“verynearly right.” They areopinionated and clear, whichis exactly what televisionproducers love to see on

programs.Twohedgehogsondifferent sides of an issue,each attacking the idioticideas of the adversary, makeforagoodshow.Foxes, by contrast, are

complexthinkers.Theydon’tbelieve that one big thingdrives the march of history(for example, they areunlikely to accept the viewthat Ronald Reagan single-handedly ended the coldwarby standing tall against the

Soviet Union). Instead thefoxes recognize that realityemergesfromtheinteractionsofmany different agents andforces, including blind luck,often producing large andunpredictable outcomes. Itwas the foxes who scoredbest in Tetlock’s study,although their performancewas still very poor.They areless likely than hedgehogs tobe invited to participate intelevisiondebates.

ItisNottheExperts’Fault—TheWorldis

DifficultThemainpointofthischapterisnotthatpeoplewhoattemptto predict the future makemany errors; that goeswithout saying. The firstlesson is that errors ofprediction are inevitablebecause the world isunpredictable. The second is

that high subjectiveconfidenceisnottobetrustedas an indicator of accuracy(low confidence could bemoreinformative).Short-term trends can be

forecast, and behavior andachievements can bepredicted with fair accuracyfrom previous behaviors andachievements.Butwe shouldnot expect performance inofficertrainingandincombatto be predictable from

behavior on an obstacle field—behavior both on the testand in the real world isdetermined by many factorsthat are specific to theparticular situation. Removeone highly assertive memberfrom a group of eightcandidates and everyoneelse’s personalities willappear to change. Let asniper’sbulletmovebyafewcentimeters and theperformanceofanofficerwill

betransformed.Idonotdenythe validity of all tests—if atest predicts an importantoutcomewithavalidityof.20or .30, the test should beused. But you should notexpect more. You shouldexpect little or nothing fromWallStreetstockpickerswhohopetobemoreaccuratethanthe market in predicting thefuture of prices. And youshouldnotexpectmuchfrompundits making long-term

forecasts—althoughtheymayhave valuable insights intothe near future. The line thatseparates the possiblypredictable future from theunpredictabledistantfutureisinဆyettobedrawn.

SpeakingofIllusorySkill

“He knows that therecord indicates that thedevelopment of this

illness is mostlyunpredictable. How canhebesoconfidentinthiscase? Sounds like anillusionofvalidity.”

“She has a coherentstory that explains allshe knows, and thecoherence makes herfeelgood.”

“What makes him

believethatheissmarterthan the market? Is thisanillusionofskill?”

“She isahedgehog.Shehas a theory thatexplains everything, andit gives her the illusionthat she understands theworld.”

“The question is notwhether these experts

are well trained. It iswhether their world ispredictable.”

P

Intuitionsvs.Formulas

PaulMeehlwasastrangeandwonderful character, and oneof the most versatilepsychologistsofthetwentiethcentury. Among thedepartments inwhich he hadfaculty appointments at theUniversityofMinnesotawerepsychology, law, psychiatry,

neurology, and philosophy.He also wrote on religion,politicalscience,andlearningin rats. A statisticallysophisticatedresearcherandafierce critic of empty claimsinclinicalpsychology,Meehlwas also a practicingpsychoanalyst. He wrotethoughtful essays on thephilosophical foundations ofpsychological research that Ialmost memorized while Iwas a graduate student. I

nevermetMeehl,buthewasone of my heroes from thetime I read his Clinical vs.Statistical Prediction: ATheoretical Analysis and aReviewoftheEvidence.In the slim volume that he

later called “my disturbinglittle book,” Meehl reviewedthe results of 20 studies thathadanalyzedwhetherclinicalpredictions based on thesubjective impressions oftrained professionals were

moreaccurate than statisticalpredictions made bycombining a few scores orratingsaccordingtoarule.Ina typical study, trainedcounselors predicted thegradesoffreshmenattheendof the school year. Thecounselors interviewed eachstudentforforty-fiveminutes.Theyalsohadaccess tohighschool grades, severalaptitudetests,andafour-pagepersonal statement. The

statisticalalgorithmusedonlyafractionofthisinformation:high school grades and oneaptitude test. Nevertheless,the formula was moreaccurate than 11 of the 14counselors. Meehl reportedgenerally similar resultsacross a variety of otherforecast outcomes, includingviolations of parole, successinpilot training,andcriminalrecidivism.Not surprisingly, Meehl’s

book provoked shock anddisbelief among clinicalpsychologists, and thecontroversy it started hasengendered a stream ofresearch that is still flowingtoday, more than fiftyyephyဆЉ diars after itspublication. The number ofstudiesreportingcomparisonsof clinical and statisticalpredictions has increased toroughly twohundred,but thescore in the contest between

algorithms and humans hasnot changed. About 60% ofthe studies have shownsignificantly better accuracyfor the algorithms.The othercomparisonsscoredadrawinaccuracy, but a tie istantamount to a win for thestatistical rules, which arenormallymuchlessexpensiveto use than expert judgment.No exception has beenconvincinglydocumented.The range of predicted

outcomes has expanded tocover medical variables suchas the longevity of cancerpatients,thelengthofhospitalstays,thediagnosisofcardiacdisease,andthesusceptibilityof babies to sudden infantdeath syndrome; economicmeasures such as theprospects of success for newbusinesses, the evaluation ofcreditrisksbybanks,andthefuture career satisfaction ofworkers;questionsof interest

to government agencies,including assessments of thesuitability of foster parents,theoddsofrecidivismamongjuvenile offenders, and thelikelihood of other forms ofviolent behavior; andmiscellaneousoutcomes suchastheevaluationofscientificpresentations, the winners offootballgames,andthefutureprices of Bordeaux wine.Eachofthesedomainsentailsa significant degree of

uncertainty andunpredictability.We describethem as “low-validityenvironments.”Ineverycase,the accuracy of experts wasmatched or exceeded by asimplealgorithm.AsMeehl pointed out with

justified pride thirty yearsafter the publication of hisbook, “There is nocontroversy in social sciencewhich shows such a largebody of qualitatively diverse

studies coming out souniformly in the samedirectionasthisone.”The Princeton economist

and wine lover OrleyAshenfelter has offered acompelling demonstration ofthepowerofsimplestatisticsto outdo world-renownedexperts. Ashenfelter wantedto predict the future value offine Bordeaux wines frominformation available in theyear they are made. The

questionisimportantbecausefinewinestakeyearstoreachtheir peak quality, and theprices of mature wines fromthe same vineyard varydramatically across differentvintages; bottles filled onlytwelve months apart candiffer in value by a factor of10 or more. An ability toforecast future prices is ofsubstantial value, becauseinvestors buy wine, like art,in the anticipation that its

valuewillappreciate.It is generally agreed that

the effect of vintage can bedue only to variations in theweather during the grape-growing season. The bestwinesareproducedwhen thesummer is warm and dry,which makes the Bordeauxwine industry a likelybeneficiary of globalwarming.Theindustryisalsohelpedbywetsprings,whichincrease quantity without

much effect on quality.Ashenfelter converted thatconventional knowledge intoa statistical formula thatpredictsthepriceofawine—for a particular property andat a particular age—by threefeatures of the weather: theaverage temperature over thesummer growing season, theamount of rain at harvest-time, and the total rainfallduring the previous winter.Hisformulaprovidesaccurate

priceforecastsyearsandevendecades into the future.Indeed, his formula forecastsfuture prices much moreaccurately than the currentprices of young wines do.This new example of a“Meehl pattern” challengesthe abilities of the expertswhose opinions help shapethe early price. It alsochallenges economic theory,according to which pricesshouldreflectalltheavailable

information, including theweather. Ashenfelter’sformulaisextremelyaccurate—thecorrelationbetweenhispredictions and actual pricesisabove.90.Whyare experts e yinferior

to algorithms? One reason,which Meehl suspected, isthat experts try to be clever,think outside the box, andconsider complexcombinations of features inmaking their predictions.

Complexitymaywork in theoddcase,butmoreoftenthannotitreducesvalidity.Simplecombinations of features arebetter. Several studies haveshown that human decisionmakers are inferior to apredictionformulaevenwhenthey are given the scoresuggested by the formula!They feel that they canoverrule the formula becausethey have additionalinformation about the case,

but they are wrong moreoften than not. According toMeehl, there are fewcircumstances underwhich itis a good idea to substitutejudgment for a formula. In afamous thought experiment,he described a formula thatpredicts whether a particularpersonwill go to themoviestonight and noted that it isproper to disregard theformula if information isreceived that the individual

broke a leg today. The name“broken-leg rule” has stuck.The point, of course, is thatbrokenlegsareveryrare—aswellasdecisive.Another reason for the

inferiorityofexpertjudgmentis that humans areincorrigibly inconsistent inmaking summary judgmentsof complex information.When asked to evaluate thesame information twice, theyfrequently give different

answers. The extent of theinconsistency is often amatter of real concern.Experiencedradiologistswhoevaluate chest X-rays as“normal” or “abnormal”contradictthemselves20%ofthe time when they see thesame picture on separateoccasions. A study of 101independent auditors whowere asked to evaluate thereliability of internalcorporate audits revealed a

similar degree ofinconsistency.Areviewof41separate studies of thereliabilityof judgmentsmadeby auditors, pathologists,psychologists, organizationalmanagers, and otherprofessionals suggests thatthis level of inconsistency istypical, even when a case isreevaluated within a fewminutes. Unreliablejudgments cannot be validpredictorsofanything.

The widespreadinconsistencyisprobablydueto the extreme contextdependencyofSystem1.Weknowfromstudiesofprimingthat unnoticed stimuli in ourenvironment have asubstantial influence on ourthoughts and actions. Theseinfluences fluctuate frommomenttomoment.Thebriefpleasureofacoolbreezeonahot day may make youslightly more positive and

optimistic about whateveryou are evaluating at thetime. The prospects of aconvict being granted parolemay change significantlyduring the time that elapsesbetween successive foodbreaks in the parole judges’schedule. Because you havelittle direct knowledge ofwhat goes on in your mind,youwillneverknowthatyoumight have made a differentjudgment or reached a

different decision under veryslightly differentcircumstances. Formulas donot suffer from suchproblems. Given the sameinput, they always return thesame answer. Whenpredictability is poor—whichit is in most of the studiesreviewed by Meehl and hisfollowers—inconsistency isdestructive of any predictivevalidity.The research suggests a

surprising conclusion: tomaximize predictiveaccuracy, final decisionsshould be left to formulas,especially in low-validityenvironments. In admissiondecisionsformedicalschools,for example, the finaldetermination is often madeby the faculty members whointerview the candidate. Theevidence is fragmentary, butthere are solid grounds for aconjecture: conducting an

interviewislikelytodiminishthe accuracy of a selectionprocedure, if the interviewersalsomakethefinaladmissiondecisions. Becauseinterviewers areoverconfident in theirintuitions,theywillassigntoomuchweighttotheirpersonalimpressions and too littleweight to other sources ofinformation, loweringvalidity. Similarly, theexpertswhoevaluatethequas

plity of immature wine topredict its future have asource of information thatalmostcertainlymakesthingsworseratherthanbetter: theycan taste the wine. Inaddition, of course, even ifthey have a goodunderstanding of the effectsof the weather on winequality, theywill not be abletomaintaintheconsistencyofaformula.

The most importantdevelopmentinthefieldsinceMeehl’s original work isRobyn Dawes’s famousarticle“TheRobustBeautyofImproper Linear Models inDecision Making.” Thedominant statistical practicein the social sciences is toassignweightstothedifferentpredictors by following analgorithm, called multiple

regression, that is now builtinto conventional software.The logic of multipleregression is unassailable: itfinds theoptimal formula forputting together a weightedcombinationofthepredictors.However, Dawes observedthat the complex statisticalalgorithm adds little or novalue.Onecandojustaswellby selecting a set of scoresthat have some validity forpredicting the outcome and

adjusting the values to makethem comparable (by usingstandard scores or ranks). Aformula that combines thesepredictorswithequalweightsislikelytobejustasaccurateinpredictingnewcasesasthemultiple-regression formulathat was optimal in theoriginal sample. More recentresearch went further:formulas that assign equalweights to all the predictorsare often superior, because

they are not affected byaccidentsofsampling.The surprising success of

equal-weighting schemes hasan important practicalimplication: it is possible todevelop useful algorithmswithout any prior statisticalresearch. Simple equallyweighted formulas based onexisting statistics or oncommonsenseareoftenverygoodpredictorsofsignificantoutcomes. In a memorable

example,Dawes showed thatmarital stability is wellpredictedbyaformula:

frequencyoflovemakingminus frequency ofquarrels

Youdon’twantyourresulttobeanegativenumber.The important conclusion

from this research is that analgorithm that is constructedonthebackofanenvelopeis

often good enough tocompete with an optimallyweighted formula, andcertainly good enough tooutdo expert judgment. Thislogiccanbeapplied inmanydomains, ranging from theselection of stocks byportfolio managers to thechoicesofmedicaltreatmentsbydoctorsorpatients.Aclassicapplicationofthis

approach is a simplealgorithm that has saved the

lives of hundreds ofthousands of infants.Obstetricians had alwaysknown that an infant who isnotbreathingnormallywithina few minutes of birth is athigh risk of brain damage ordeath. Until theanesthesiologist VirginiaApgar intervened in 1953,physicians and midwivesused their clinical judgmentto determine whether a babywas in distress. Different

practitioners focused ondifferentcues.Somewatchedfor breathing problemswhileothers monitored how soonthe baby cried. Without astandardized procedure,danger signs were oftenmissed, and many newborninfantsdied.

One day over breakfast, amedical resident asked howDr. Apgar would make a

systematic assessment of anewborn. “That’s easy,” shereplied.“Youwoulddoitlikethis.”Apgar jotteddownfivevariables (heart rate,respiration, reflex, muscletone, and color) and threescores (0, 1, or 2, dependingon the robustness of eachsign). Realizing that shemight have made abreakequthrough that anydelivery room couldimplement, Apgar began

ratinginfantsbythisruleoneminute after they were born.Ababywithatotalscoreof8or above was likely to bepink, squirming, crying,grimacing, with a pulse of100ormore—ingoodshape.A baby with a score of 4 orbelow was probably bluish,flaccid, passive, with a slowor weak pulse—in need ofimmediate intervention.Applying Apgar’s score, thestaffindeliveryroomsfinally

had consistent standards fordetermining which babieswere in trouble, and theformula is credited for animportant contribution toreducinginfantmortality.TheApgar test is still used everyday in every delivery room.Atul Gawande’s recent AChecklist Manifesto providesmany other examples of thevirtues of checklists andsimplerules.

TheHostilitytoAlgorithms

Fromtheveryoutset,clinicalpsychologists responded toMeehl’s ideas with hostilityand disbelief. Clearly, theywereinthegripofanillusionof skill in terms of theirability to make long-termpredictions. On reflection, itiseasytoseehowtheillusioncame about and easy tosympathize with the

clinicians’ rejection ofMeehl’sresearch.The statistical evidence of

clinical inferioritycontradictsclinicians’ everydayexperience of the quality oftheir judgments.Psychologistswhoworkwithpatients have many hunchesduring each therapy session,anticipating how the patientwill respond to anintervention, guessing whatwill happen next. Many of

these hunches are confirmed,illustrating the reality ofclinicalskill.The problem is that the

correct judgments involveshort-term predictions in thecontext of the therapeuticinterview, a skill in whichtherapists may have years ofpractice. The tasks at whichthey fail typically requirelong-term predictions aboutthepatient’sfuture.Thesearemuchmoredifficult,eventhe

best formulas do onlymodestly well, and they arealso tasks that the clinicianshave never had theopportunity to learn properly—they would have to waityearsforfeedback,insteadofreceiving the instantaneousfeedback of the clinicalsession. However, the linebetween what clinicians candowellandwhattheycannotdo at allwell is not obvious,and certainly not obvious to

them. They know they areskilled, but they don’tnecessarily know theboundaries of their skill.Notsurprisingly, then, the ideathat a mechanicalcombination of a fewvariables could outperformthe subtle complexity ofhuman judgment strikesexperienced clinicians asobviouslywrong.Thedebateaboutthevirtues

of clinical and statistical

prediction has always had amoral dimension. Thestatistical method, Meehlwrote, was criticized byexperienced clinicians as“mechanical, atomistic,additive, cut and dried,artificial, unreal, arbitrary,incomplete, dead, pedantic,fractionated, trivial, forced,static, superficial, rigid,sterile, academic,pseudoscientific and blind.”The clinical method, on the

otherhand,waslaudedbyitsproponents as “dynamic,global, meaningful, holistic,subtle, sympathetic,configural, patterned,organized, rich, deep,genuine, sensitive,sophisticated, real, living,concrete, natural, true to life,andunderstanding.”Thisisanattitudewecanall

recognize. When a humancompetes with a machine,whether it is John Henry a-

hammerin’ on the mountainor the chess genius GarryKasparov facing off againstthe computerDeepBlue, oursympathies lie with ourfellow human. The aversionto algorithms makingdecisions that affect humansis rooted in the strongpreference that many peoplehave for the ormnatural overthe synthetic or artificial.Asked whether they wouldrather eat an organic or a

commercially grown apple,most people prefer the “allnatural”one.Evenafterbeinginformed that the two applestaste thesame,have identicalnutritional value, and areequally healthful, a majoritystill prefer the organic fruit.Even the producers of beerhave found that they canincreasesalesbyputting“AllNatural” or “NoPreservatives”onthelabel.The deep resistance to the

demystificationofexpertiseisillustrated by the reaction ofthe European winecommunity to Ashenfelter’sformula for predicting theprice of Bordeaux wines.Ashenfelter’s formulaansweredaprayer:onemightthus have expected thatwinelovers everywhere would begrateful to him fordemonstrablyimprovingtheirability to identify the winesthatlaterwouldbegood.Not

so. The response in Frenchwine circles, wrote TheNewYork Times, ranged“somewhere between violentand hysterical.” Ashenfelterreports that one oenophilecalledhis findings“ludicrousandabsurd.”Anotherscoffed,“It is like judging movieswithout actually seeingthem.”The prejudice against

algorithmsismagnifiedwhenthe decisions are

consequential. Meehlremarked, “I do not quiteknow how to alleviate thehorror some clinicians seemto experience when theyenvisage a treatable casebeing denied treatmentbecausea‘blind,mechanical’equation misclassifies him.”In contrast, Meehl and otherproponents of algorithmshavearguedstronglythatitisunethical to rely on intuitivejudgments for important

decisions if an algorithm isavailablethatwillmakefewermistakes. Their rationalargumentiscompelling,butitruns against a stubbornpsychological reality: formost people, the cause of amistakematters.Thestoryofa child dying because analgorithm made a mistake ismore poignant than the storyofthesametragedyoccurringas a result of human error,and the difference in

emotional intensity is readilytranslated into a moralpreference.Fortunately, the hostility to

algorithms will probablysoften as their role ineveryday life continues toexpand.Lookingforbooksormusic we might enjoy, weappreciate recommendationsgenerated by soft ware. Wetake it for granted thatdecisions about credit limitsare made without the direct

intervention of any humanjudgment. We areincreasingly exposed toguidelines thathave theformofsimplealgorithms,suchasthe ratio of good and badcholesterol levels we shouldstrive to attain.The public isnowwellawarethatformulasmaydobetterthanhumansinsomecriticaldecisions in theworldof sports: howmuchaprofessional team should payfor particular rookie players,

or when to punt on fourthdown. The expanding list oftasks that are assigned toalgorithms should eventuallyreduce the discomfort thatmost people feel when theyfirst encounter the pattern ofresults that Meehl describedinhisdisturbinglittlebook.

LearningfromMeehlIn 1955, as a twenty-one-year-old lieutenant in the

IsraeliDefenseForces, Iwasassigned to set up aninterview system for theentire army. If you wonderwhy such a responsibilitywould be forced uponsomeone so young, bear inmind that the state of Israelitself was only seven yearsold at the time; all itsinstitutions were underconstruction, and someonehad to build them.Odd as itsounds today, my bachelor’s

degree in psychologyprobably qualified me as thebest-trained psychologist inthe army. My directsupervisor, a brilliantresearcher, had a degree inchemistry.An idilnterviewroutinewas

already in place when I wasgiven my mission. Everysoldier drafted into the armycompleted a battery ofpsychometric tests, and eachman considered for combat

duty was interviewed for anassessment of personality.The goal was to assign therecruit a score of generalfitnessforcombatandtofindthe best match of hispersonality among variousbranches: infantry, artillery,armor, and so on. Theinterviewerswere themselvesyoung draftees, selected forthis assignment by virtue oftheir high intelligence andinterest in dealing with

people. Most were women,whowereat the timeexemptfrom combat duty. Trainedfor a few weeks in how toconduct a fifteen- to twenty-minute interview, they wereencouraged to cover a rangeof topics and to form ageneral impression of howwell the recruit would do inthearmy.Unfortunately, follow-up

evaluations had alreadyindicated that this interview

procedurewasalmostuselessfor predicting the futuresuccess of recruits. I wasinstructed to design aninterviewthatwouldbemoreuseful but would not takemore time. Iwasalso told totryoutthenewinterviewandtoevaluateitsaccuracy.Fromthe perspective of a seriousprofessional, I was no morequalified for the task than Iwas to build a bridge acrosstheAmazon.

Fortunately,IhadreadPaulMeehl’s “little book,” whichhad appeared just a yearearlier. I was convinced byhis argument that simple,statisticalrulesaresuperiortointuitive“clinical”judgments.I concluded that the thencurrentinterviewhadfailedatleast in part because itallowed the interviewers todo what they found mostinteresting, which was tolearn about the dynamics of

the interviewee’smental life.Instead, we should use thelimitedtimeatourdisposaltoobtain as much specificinformationaspossibleaboutthe interviewee’s life in hisnormalenvironment.Anotherlesson I learned from Meehlwas that we should abandonthe procedure in which theinterviewers’ globalevaluations of the recruitdeterminedthefinaldecision.Meehl’s book suggested that

such evaluations should notbe trusted and that statisticalsummaries of separatelyevaluated attributes wouldachievehighervalidity.Idecidedonaprocedure in

whichtheinterviewerswouldevaluate several relevantpersonality traits and scoreeach separately. The finalscore of fitness for combatduty would be computedaccording to a standardformula,withnofurtherinput

fromtheinterviewers.Imadeupalistofsixcharacteristicsthat appeared relevant toperformanceinacombatunit,including “responsibility,”“sociability,” and “masculinepride.” I then composed, foreach trait, a series of factualquestions about theindividual’s life before hisenlistment, including thenumber of different jobs hehad held, how regular andpunctual he had been in his

work or studies, thefrequency of his interactionswith friends, and his interestand participation in sports,among others. The idea wasto evaluate as objectively aspossible howwell the recruithaddoneoneachdimension.By focusing on

standardized, factualquestions, I hoped to combatthe halo effect, wherefavorable first impressionsinfluence later judgments.As

a further precaution againsthalos, I instructed theinterviewerstogothroughthesixtraits inafixedsequence,rating each trait on a five-pointscalebeforegoingontothenext.Andthatwasthat. Iinformedtheinterviewersthatthey need not concernthemselves with the recruit’sfuture adjustment to themilitary. Their only taskwasto elicit relevant facts abouthis past and to use that

information to score eachpersonalitydimension.“Yourfunctionistoprovidereliablemeasurements,” I told them.“Leave the predicok tivevalidity to me,” by which Imeant the formula that Iwasgoing to devise to combinetheirspecificratings.Theinterviewerscameclose

to mutiny. These brightyoungpeopleweredispleasedto be ordered, by someonehardlyolder than themselves,

to switch off their intuitionand focus entirely on boringfactual questions. One ofthem complained, “You areturning us into robots!” So Icompromised. “Carry out theinterview exactly asinstructed,” I told them,“andwhenyouaredone,haveyourwish: close your eyes, try toimagine the recruit as asoldier, and assign him ascoreonascaleof1to5.”Several hundred interviews

were conducted by this newmethod, and a few monthslaterwecollectedevaluationsof the soldiers’ performancefrom the commandingofficersof theunits towhichthey had been assigned. Theresults made us happy. AsMeehl’s book had suggested,the new interview procedurewas a substantialimprovement over the oldone. The sum of our sixratings predicted soldiers’

performance much moreaccurately than the globalevaluations of the previousinterviewing method,although far from perfectly.We had progressed from“completely useless” to“moderatelyuseful.”The big surprise tomewas

that the intuitive judgmentthat the interviewerssummoned up in the “closeyour eyes” exercise also didverywell,indeedjustaswell

asthesumof thesixspecificratings. I learned from thisfinding a lesson that I havenever forgotten: intuitionadds value even in the justlyderided selection interview,but only after a disciplinedcollection of objectiveinformation and disciplinedscoringofseparatetraits.Isetaformulathatgavethe“closeyour eyes” evaluation thesameweightasthesumofthesix trait ratings. A more

general lesson that I learnedfrom thisepisodewasdonotsimply trust intuitivejudgment—your own or thatofothers—butdonotdismissit,either.Some forty-five years later,

after I won a Nobel Prize ineconomics, I was for a shorttime a minor celebrity inIsrael. On one of my visits,someone had the idea ofescorting me around my oldarmybase,whichstillhoused

the unit that interviews newrecruits. I was introduced tothe commanding officer ofthe Psychological Unit, andshe described their currentinterviewing practices,whichhad not changed much fromthe system I had designed;there was, it turned out, aconsiderable amount ofresearch indicating that theinterviews still worked well.Asshecametotheendofherdescription of how the

interviewsare conducted, theofficer added, “And then wetellthem,‘Closeyoureyes.’”

DoItYourselfThemessageofthischapterisreadily applicable to tasksother thanmakingmanpowerdecisions for an army.Implementing interviewprocedures in the spirit ofMeehl and Dawes requiresrelatively little effort but

substantial discipline.Supposethatyouneedtohireasalesrepresentativeforyourfirm.Ifyouareseriousabouthiring the best possibleperson for the job, this iswhat you should do. First,select a few traits that areprerequisites for success inthis position (technicalproficiency, engagingpersonality,reliability,andsoon). Don’t overdo it—sixdimensionsisagoodnumber.

The traits you choose shouldbeasindependentaspossiblefrom each other, and youshould feel that you canassess them reliably byasking a few factualquestions. Next, make a listof those questions for eachtraitandthinkabouthowyouwill score it, say on a 1–5scale. You should have anidea ofwhat youwill caleigl“veryweak”or“verystrong.”These preparations should

takeyouhalfanhourorso,asmall investment that canmake a significant differencein the quality of the peopleyou hire. To avoid haloeffects, you must collect theinformation on one trait at atime,scoringeachbeforeyoumoveon to thenextone.Donot skip around. To evaluateeachcandidate,addupthesixscores. Because you are incharge of the final decision,you should not do a “close

your eyes.” Firmly resolvethat you will hire thecandidatewhosefinalscoreisthe highest, even if there isanother one whom you likebetter—trytoresistyourwishto invent broken legs tochange the ranking. A vastamount of research offers apromise: you aremuchmorelikely to find the bestcandidate if you use thisprocedurethanifyoudowhatpeople normally do in such

situations,whichistogointothe interviewunprepared andtomakechoicesbyanoverallintuitive judgment such as “IlookedintohiseyesandlikedwhatIsaw.”

SpeakingofJudgesvs.Formulas

“Whenever we canreplacehuman judgmentbya formula,weshouldatleastconsiderit.”

“He thinks hisjudgments are complexand subtle, but a simplecombination of scorescould probably dobetter.”

“Let’sdecideinadvancewhat weight to give tothe datawe have on thecandidates’ pastperformance. Otherwise

we will give too muchweighttoourimpressionfromtheinterviews.”

P

ExpertIntuition:WhenCanWeTrust

It?

Professional controversiesbring out the worst inacademics.Scientificjournalsoccasionally publishexchanges, often beginningwith someone’s critique ofanother’s research, followed

by a reply and a rejoinder. Ihave always thought thatthese exchanges are a wasteof time. Especially when theoriginal critique is sharplyworded, the reply and therejoinder are often exercisesinwhatIhavecalledsarcasmfor beginners and advancedsarcasm. The replies rarelyconcede anything to a bitingcritique, and it is almostunheard of for a rejoinder toadmit that the original

critique was misguided orerroneous in any way. On afew occasions I haveresponded tocriticisms that Ithought were grosslymisleading, because a failureto respondcanbe interpretedasconcedingerror,butIhavenever found the hostileexchanges instructive. Insearchofanotherwaytodealwith disagreements, I haveengagedinafew“adversarialcollaborations,” in which

scholarswho disagree on thescience agree to write ajointly authored paper ontheir differences, andsometimes conduct researchtogether. In especially tensesituations, the research ismoderatedbyanarbiter.My most satisfying and

productive adversarialcollaboration was with GaryKlein, the intellectual leaderof an association of scholarsand practitioners who do not

like the kind of work I do.Theycallthemselvesstudentsof Naturalistic DecisionMaking,orNDM,andmostlywork in organizations wherethe"0%Љ tyoften studyhowexperts work. The N DMersadamantlyrejectthefocusonbiases in the heuristics andbiases approach. Theycriticize thismodel as overlyconcerned with failures anddriven by artificialexperiments rather than by

thestudyofrealpeopledoingthings that matter. They aredeeply skeptical about thevalue of using rigidalgorithms to replace humanjudgment, and PaulMeehl isnotamong theirheroes.GaryKlein has eloquentlyarticulated this position overmanyyears.Thisishardlythebasisfora

beautifulfriendship,butthereis more to the story. I hadnever believed that intuition

is always misguided. I hadalso been a fan of Klein’sstudies of expertise infirefighterssinceIfirstsawadraft of a paper he wrote inthe1970s,andwasimpressedby his book Sources ofPower, much of whichanalyzes how experiencedprofessionals developintuitive skills. I invited himtojoininanefforttomaptheboundary that separates themarvels of intuition from its

flaws. He was intrigued bythe idea and we went aheadwith the project—with nocertainty that it wouldsucceed.Wesetouttoansweraspecificquestion:Whencanyou trust an experiencedprofessional who claims tohave an intuition? It wasobvious that Klein would bemoredisposed to be trusting,and I would be moreskeptical.Butcouldweagreeon principles for answering

thegeneralquestion?Over seven or eight years

we had many discussions,resolvedmanydisagreements,almost blew up more thanonce, wrote many draft s,became friends, andeventually published a jointarticle with a title that tellsthe story: “Conditions forIntuitiveExpertise:AFailureto Disagree.” Indeed, we didnot encounter real issues onwhichwe disagreed—butwe

didnotreallyagree.

MarvelsandFlawsMalcolm Gladwell’sbestseller Blink appearedwhile Klein and I wereworkingontheproject,anditwas reassuring to findourselves in agreement aboutit. Gladwell’s book openswith the memorable story ofart experts faced with anobject that is described as a

magnificent example of akouros, a sculpture of astriding boy. Several of theexperts had strong visceralreactions:theyfeltintheirgutthat thestatuewasafakebutwere not able to articulatewhatitwasaboutitthatmadethem uneasy. Everyone whoread the book—millions did—remembers that story as atriumph of intuition. Theexpertsagreedthattheyknewthe sculpture was a fake

without knowing how theyknew—the very definition ofintuition.Thestoryappearstoimplythatasystematicsearchfor the cue that guided theexpertswouldhavefailed,butKleinandIbothrejectedthatconclusion.Fromourpointofview, such an inquiry wasneeded, and if it had beenconducted properly (whichKlein knows how to do), itwould probably havesucceeded.

Although many readers ofthe kouros example weresurely drawn to an almostmagical view of expertintuition, Gladwell himselfdoesnotholdthatposition.Ina laterchapterhedescribesamassive failure of intuition:Americans elected PresidentHarding, whose onlyqualification for the positionwas that he perfectly lookedthe part. Square jawed andtall,hewastheperfectimage

of a strong and decisiveleader. People voted forsomeone who looked strongand decisive without anyotherreasontobelievethathewas. An intuitive predictionof how Harding wouldperform as president arosefrom substituting onequestionforanother.Areaderof this book should expectsuch an intuition to be heldwithconfidence.

IntuitionasRecognition

The early experiences thatshaped Klein’s views ofintuition were starklydifferent from mine. Mythinking was formed byobserving the illusion ofvalidity in myself and byreading Paul Meehl’sdemonstrations of theinferiority of clinicalprediction. In contrast,

Klein’sviewswereshapedbyhisearlystudiesoffiregroundcommanders (the leaders offirefighting teams). Hefollowedthemastheyfoughtfiresandlaterinterviewedtheleader about his thoughts ashemade decisions. As Kleindescribed it in our jointarticle, he and hiscollaborators

investigated how thecommanderscouldmake

good decisions withoutcomparing options. Theinitial hypothesis wasthat commanders wouldrestrict their analysis toonly a pair of options,but that hypothesisproved to be incorrect.In fact, the commandersusuallygeneratedonlyasingle option, and thatwas all they needed.They could draw on therepertoire of patterns

that they had compiledduring more than adecade of both real andvirtual experience toidentify a plausibleoption, which theyconsidered first. Theyevaluated this option bymentallysimulating it tosee if it would work inthe situation they werefacing….Ifthecourseofaction they wereconsidering seemed

appropriate, they wouldimplement it. If it hadshortcomings, theywouldmodify it. If theycould not easily modifyit,theywouldturntothenext most plausibleoption and run throughthesameprocedureuntilan acceptable course ofactionwasfound.

Klein elaborated thisdescription into a theory of

decision making that hecalled the recognition-primeddecision(RPD)model,whichappliestofirefightersbutalsodescribes expertise in otherdomains, including chess.The process involves bothSystem 1 and System 2. Inthe first phase, a tentativeplan comes to mind by anautomatic function ofassociativememory—System1. The next phase is adeliberate process in which

theplanismentallysimulatedto check if it will work—anoperation of System 2. Themodel of intuitive decisionmakingaspatternrecognitiondevelops ideas presentedsome time ago by HerbertSimon, perhaps the onlyscholar who is recognizedand admired as a hero andfounding figure by all thecompetingclansandtribesinthestudyofdecisionmaking.I quoted Herbert Simon’s

definition of intuition in theintroduction,but itwillmakemore sense when I repeat itnow: “The situation hasprovided a cue; this cue hasgiven the expert access toinformation stored inmemory, and the informationprovidestheanswer.Intuitionis nothing more and nothinglessthanrecognition.”This strong statement

reducestheapparentmagicofintuition to the everyday

experience of memory. Wemarvel at the story of thefirefighter who has a suddenurge to escape a burninghousejustbeforeitcollapses,because thefirefighterknowsthe danger intuitively,“without knowing how heknows.”However,wealsodonot know how weimmediately know that aperson we see as we enter aroomisourfriendPeter.Themoral of Simon’s remark is

that the mystery of knowingwithout knowing is not adistinctive feature ofintuition; it is the norm ofmentallife.

AcquiringSkillHow does the informationthat supports intuition get“stored in memory”? Certaintypes of intuitions areacquired very quickly. Wehave inherited from our

ancestors a great facility tolearn when to be afraid.Indeed, one experience isoften sufficient to establish along-term aversion and fear.Manyofushave thevisceralmemory of a single dubiousdish tto hat still leaves usvaguely reluctant to return toa restaurant. All of us tenseupwhenwe approach a spotinwhichanunpleasanteventoccurred, evenwhen there isno reason to expect it to

happen again. For me, onesuchplaceistherampleadingto the San Francisco airport,where years ago a driver inthe throes of road ragefollowed me from thefreeway, rolled down hiswindow, and hurledobscenities at me. I neverknewwhatcausedhishatred,but I remember his voicewhenever I reach that pointonmywaytotheairport.My memory of the airport

incident is conscious and itfully explains the emotionthat comeswith it. Onmanyoccasions,however,youmayfeel uneasy in a particularplaceorwhensomeoneusesaparticular turn of phrasewithout having a consciousmemory of the triggeringevent. In hindsight, you willlabel that unease an intuitionif it is followed by a badexperience. This mode ofemotional learning is closely

related to what happened inPavlov’sfamousconditioningexperiments, in which thedogslearnedtorecognizethesound of the bell as a signalthat food was coming. WhatPavlov’sdogs learnedcanbedescribed as a learned hope.Learned fears are even moreeasilyacquired.Fear can also be learned—

quite easily, in fact—bywords rather than byexperience.The firemanwho

had the “sixth sense” ofdanger had certainly hadmany occasions to discussandthinkabouttypesoffireshewasnotinvolvedin,andtorehearseinhismindwhatthecues might be and how heshould react. As I rememberfrom experience, a youngplatoon commander with noexperience of combat willtenseupwhileleadingtroopsthrough a narrowing ravine,because he was taught to

identify the terrain asfavoring an ambush. Littlerepetition is needed forlearning.Emotional learning may be

quick, but what we consideras“expertise”usuallytakesalong time to develop. Theacquisition of expertise incomplex tasks such as high-level chess, professionalbasketball, or firefighting isintricate and slow becauseexpertiseinadomainisnota

single skill but rather a largecollection of miniskills.Chessisagoodexample.Anexpert player can understanda complex position at aglance, but it takes years todevelop that level of ability.Studiesofchessmastershaveshown that at least 10,000hours of dedicated practice(about 6 years of playingchess 5 hours a day) arerequired to attain the highestlevelsofperformance.During

those hours of intenseconcentration,aseriouschessplayer becomes familiarwiththousands of configurations,each consisting of anarrangementof relatedpiecesthat can threaten or defendeachother.Learning high-level chess

can be compared to learningto read.A first graderworkshardatrecognizingindividualletters and assembling theminto syllables andwords, but

agoodadultreaderperceivesentire clauses. An expertreader has also acquired theability to assemble familiarelementsinanewpatternandcan quickly “recognize” andcorrectly pronounce a wordthat she has never seenbefore. In chess, recurrentpatterns of interacting piecesplay the roleof letters,andachessposition isa longwordorasentence.Askilledreaderwhoseesit

for the first timewillbeableto read theopeningstanzaofLewis Carroll’s“Jabberwocky” with perfectrhythm and intonation, aswellaspleasure:

’Twasbrillig,and theslithytovesDid gyre and gimble in the

wabe:All mimsy were the

borogoves,

And the mome rathsoutgrabe.

Acquiring expertise in chessis harder and slower thanlearningtoreadbecausethereare many more letters in the“alphabet” of chess andbecause the “words” consistof many letters. Afterthousands of hours ofpractice, however, chess

masters are able to read achess situation at a glance.The fewmoves that come totheirmind are almost alwaysstrong and sometimescreative.Theycandealwitha“word” they have neverencountered, and they canfindanewway to interpretafamiliarone.

TheEnvironmentofSkill

KleinandIquicklyfoundthatweagreedbothon thenatureof intuitive skill and on howitisacquired.Westillneededtoagreeonourkeyquestion:When can you trust a self-confident professional whoclaimstohaveanintuition?We eventually concluded

that our disagreement wasdueinparttothefactthatwehaddifferentexpertsinmind.Klein had spent much time

withfiregroundcommanders,clinical nurses, and otherprofessionals who have realexpertise. I had spent moretime thinking aboutclinicians, stock pickers, andpolitical scientists trying tomake unsupportable long-term forecasts. Notsurprisingly, his defaultattitudewastrustandrespect;minewasskepticism.Hewasmore willing to trust expertswho claim an intuition

because, as he told me, trueexperts know the limits oftheirknowledge.Iarguedthatthere are many pseudo-expertswhohavenoideathatthey do not know what theyare doing (the illusion ofvalidity),andthatasageneralproposition subjectiveconfidence is commonly toohighandoftenuninformative.Earlier I traced people’s

confidence in a belief to tworelatedimpressions:cognitive

ease and coherence. We areconfident when the story wetellourselvescomeseasilytomind, with no contradictionand no competing scenario.But ease and coherence donot guarantee that a beliefheld with confidence is true.The associative machine isset to suppress doubt and toevoke ideas and informationthat are compatible with thecurrently dominant story. AmindthatfollowsWYSIATI

will achieve high confidencemuch too easily by ignoringwhat it does not know. It istherefore not surprising thatmanyofusarepronetohavehighconfidenceinunfoundedintuitions. Klein and Ieventually agreed on animportant principle: theconfidence that people havein their intuitions is not areliable guide to theirvalidity. In other words, donot trust anyone—including

yourself—to tell you howmuch you should trust theirjudgment.If subjective confidence is

nottobetrusted,howcanweevaluatetheprobablevalidityof an intuitive judgment?When do judgments reflecttrueexpertise?Whendotheydisplay an illusion ofvalidity? The answer comesfromthetwobasicconditionsforacquiringaskill:

an environment that issufficientlyregulartobepredictablean opportunity to learnthese regularitiesthrough prolongedpractice

When both these conditionsare satisfied, intuitions arelikely to be skilled. Chess isan extreme example of aregular environment, but

bridgeandpokeralsoproviderobust statistical regularitiesthat can support skill.Physicians, nurses, athletes,and firefighters also facecomplex but fundamentallyorderly situations. Theaccurate intuitions that GaryKleinhasdescribedareduetohighly valid cues that es theexpert’sSystem1haslearnedto use, even if System 2 hasnot learned toname them. Incontrast, stock pickers and

political scientists whomakelong-termforecastsoperateina zero-validity environment.Theirfailuresreflectthebasicunpredictability of the eventsthattheytrytoforecast.Some environments are

worse than irregular. RobinHogarth described “wicked”environments, in whichprofessionals are likely tolearn thewrong lessons fromexperience.Heborrows fromLewis Thomas the example

of a physician in the earlytwentieth century who oftenhad intuitions about patientswho were about to developtyphoid. Unfortunately, hetestedhishunchbypalpatingthe patient’s tongue, withoutwashing his hands betweenpatients. When patient afterpatient became ill, thephysician developed a senseof clinical infallibility. Hispredictions were accurate—but not because he was

exercising professionalintuition!

Meehl’s clinicians were notineptandtheirfailurewasnotdue to lack of talent. Theyperformed poorly becausetheywereassigned tasks thatdid not have a simplesolution. The clinicians’predicamentwaslessextremethan the zero-validityenvironment of long-term

political forecasting,but theyoperated in low-validitysituations that did not allowhighaccuracy.Weknow thisto be the case because thebest statistical algorithms,although more accurate thanhuman judges, were neververy accurate. Indeed, thestudies by Meehl and hisfollowers never produced a“smoking gun”demonstration, a case inwhich clinicians completely

missedahighlyvalidcuethatthe algorithm detected. Anextremefailureofthiskindisunlikely because humanlearningisnormallyefficient.If a strong predictive cueexists, human observers willfind it, given a decentopportunity to do so.Statistical algorithms greatlyoutdo humans in noisyenvironments for tworeasons: they aremore likelythan human judges to detect

weakly valid cues and muchmore likely to maintain amodest level of accuracy byusingsuchcuesconsistently.Itiswrongtoblameanyone

for failing to forecastaccuratelyinanunpredictableworld.However,itseemsfairto blame professionals forbelievingtheycansucceedinan impossible task. Claimsfor correct intuitions in anunpredictable situation areself-delusional at best,

sometimes worse. In theabsence of valid cues,intuitive“hits”aredueeitherto luckor to lies. Ifyou findthis conclusion surprising,you still have a lingeringbelief that intuition ismagic.Remember this rule: intuitioncannot be trusted in theabsence of stable regularitiesintheenvironment.

Feedbackand

PracticeSome regularities in theenvironment are easier todiscover and apply thanothers. Think of how youdevelopedyourstyleofusingthe brakes on your car. Asyou were mastering the skillof taking curves, yougradually learnedwhen to letgo of the accelerator andwhenandhowhardtousethebrakes.Curvesdiffer,andthe

variability you experiencedwhile learning ensures thatyouarenowreadytobrakeattherighttimeandstrengthforanycurveyouencounter.Theconditions for learning thisskill are ideal, because youreceive immediate andunambiguous feedback everytime you go around a bend:the mild reward of acomfortable turn or the mildpunishmentofsomedifficultyin handling the car if you

brake either too hard or notquite hard enough. Thesituations that face a harborpilotmaneuveringlargeshipsarenolessregular,butskillismuch more difficult toacquire by sheer experiencebecause of the long delaybetween actions and theirmanoticeable outcomes.Whetherprofessionalshaveachance to develop intuitiveexpertise depends essentiallyon the quality and speed of

feedback, as well as onsufficient opportunity topractice.Expertise is not a single

skill; it is a collection ofskills, and the sameprofessional may be highlyexpertinsomeofthetasksinherdomainwhileremaininganovice inothers.By the timechessplayersbecomeexperts,they have “seen everything”(or almost everything), butchess is an exception in this

regard.Surgeonscanbemuchmore proficient in someoperations than in others.Furthermore,someaspectsofany professional’s tasks aremuch easier to learn thanothers. Psychotherapists havemany opportunities toobserve the immediatereactions of patients to whatthey say. The feedbackenables them to develop theintuitive skill to find thewords and the tone that will

calmanger,forgeconfidence,or focus the patient’sattention. On the other hand,therapists do not have achance to identify whichgeneral treatmentapproach ismost suitable for differentpatients. The feedback theyreceive from their patients’long-termoutcomesissparse,delayed, or (usually)nonexistent, and in any casetoo ambiguous to supportlearningfromexperience.

Among medical specialties,anesthesiologistsbenefitfromgood feedback, because theeffects of their actions arelikely to be quickly evident.In contrast, radiologistsobtainlittleinformationabouttheaccuracyofthediagnosesthey make and about thepathologiestheyfailtodetect.Anesthesiologists aretherefore in a better positionto develop useful intuitiveskills. If an anesthesiologist

says, “I have a feelingsomething is wrong,”everyone in the operatingroom should be prepared foranemergency.Hereagain,asinthecaseof

subjective confidence, theexperts may not know thelimits of their expertise. Anexperienced psychotherapistknows that she is skilled inworkingoutwhatisgoingoninherpatient’smindandthatshehasgood intuitionsabout

whatthepatientwillsaynext.It is tempting for her toconclude that she can alsoanticipate how well thepatientwill donextyear, butthisconclusion isnotequallyjustified. Short-termanticipation and long-termforecasting are differenttasks, and the therapist hashad adequate opportunity tolearn one but not the other.Similarly, a financial expertmay have skills in many

aspectsofhistradebutnotinpicking stocks, andanexpertin the Middle East knowsmany things but not thefuture. The clinicalpsychologist, the stockpicker, and the pundit dohave intuitive skills in someof their tasks, but they havenot learned to identify thesituations and the tasks inwhich intuition will betraythem. The unrecognizedlimits of professional skill

help explainwhy experts areoftenoverconfident.

EvaluatingValidityAt the end of our journey,GaryKleinandIagreedonageneral answer to our initialquestion:Whencanyoutrustan experienced professionalwho claims to have anintuition?Ourconclusionwasthat for the most part it ispossible to distinguish

intuitionsthatarelikelytobevalid from those that arelikely to be bogus.As in thejudgment of whether a workof art is genuine or a fake,youwill usuallydobetterbyfocusing on its provenancethan by looking at the pieceitself. If the environment issufficiently regularand if thejudge has had a chance tolearn its regularities, theassociative machinery willrecognize situations and

generate quick and accuratepredictions and decisions.You can trust someone’sintuitions if these conditionsaremet.Unfortunately,

associativentu memory alsogenerates subjectivelycompelling intuitions that arefalse. Anyone who haswatchedthechessprogressofa talented youngster knowswell that skill does notbecome perfect all at once,

and that on the way to nearperfection somemistakes aremade with great confidence.When evaluating expertintuition you should alwaysconsider whether there wasan adequate opportunity tolearn the cues, even in aregularenvironment.In a less regular, or low-

validity, environment, theheuristics of judgment areinvoked. System 1 is oftenable to produce quick

answers todifficultquestionsby substitution, creatingcoherence where there isnone. The question that isanswered is not the one thatwas intended, but the answeris produced quickly andmaybe sufficiently plausible topass the lax and lenientreviewofSystem2.Youmaywant to forecast thecommercial future of acompany, for example, andbelieve that this is what you

are judging, while in factyour evaluation is dominatedby your impressions of theenergyandcompetenceofitscurrent executives. Becausesubstitution occursautomatically, you often donot know the origin of ajudgment that you (yourSystem2)endorseandadopt.If it is the only one thatcomes to mind, it may besubjectivelyundistinguishable from valid

judgments that you makewith expert confidence. Thisis why subjective confidenceis not a good diagnostic ofaccuracy: judgments thatanswer the wrong questioncan also be made with highconfidence.You may be asking, Why

didn’tGaryKleinandIcomeup immediatelywith the ideaof evaluating an expert’sintuition by assessing theregularityof the environment

and the expert’s learninghistory—mostly setting asidetheexpert’s confidence?Andwhatdidwethinktheanswercould be? These are goodquestions because thecontoursof thesolutionwereapparent from the beginning.We knew at the outset thatfireground commanders andpediatricnurseswouldendupon one side of the boundaryofvalidintuitionsandthatthespecialties studied by Meehl

would be on the other, alongwith stock pickers andpundits.It is difficult to reconstruct

whatitwasthattookusyears,long hours of discussion,endless exchanges of draft sand hundreds of e-mailsnegotiating over words, andmorethanoncealmostgivingup. But this is what alwayshappenswhen a project endsreasonably well: once youunderstand the main

conclusion, it seems it wasalwaysobvious.As the title of our article

suggests, Klein and Idisagreed less than we hadexpected and accepted jointsolutions of almost all thesubstantive issues that wereraised. However, we alsofound that our earlydifferences were more thanan intellectual disagreement.We had different attitudes,emotions, and tastes, and

those changed remarkablylittle over the years. This ismostobviousinthefactsthatwe find amusing andinteresting.Klein stillwinceswhen the word bias ismentioned,andhestillenjoysstoriesinwhichalgorithmsorformal procedures lead toobviously absurd decisions. Itend to view the occasionalfailures of algorithms asopportunities to improvethem. On the other hand, I

findmorepleasurethanKleindoes in the come-uppance ofarrogant experts who claimintuitive powers in zero-validitysituations.Inthelongrun, however, finding asmuch intellectual agreementas we did is surely moreimportant than the persistentemotional differences thatremained.

SpeakingofExpert

Intuition

“How much expertisedoes she have in thisparticular task? Howmuch practice has shehad?”

“Does he really believethat the environment ofstart-ups is sufficientlyregular to justify anintuition that goes

againstthebaserates?”

“Sheisveryconfidentinher decision, butsubjective confidence isa poor index of theaccuracyofajudgment.”

“Did he really have anopportunity to learn?How quick and howclear was the feedbackhe received on his

judgments?”

P

TheOutsideView

A few years after mycollaboration with Amosbegan, I convinced someofficials in the IsraeliMinistry of Education of theneedforacurriculumtoteachjudgment and decisionmaking in high schools. Theteam that I assembled todesign the curriculum and

write a textbook for itincluded several experiencedteachers, some of mypsychology students, andSeymour Fox, then dean ofthe Hebrew University’sSchool of Education, whowas an expert in curriculumdevelopment.After meeting every Friday

afternoonforaboutayear,wehad constructed a detailedoutline of the syllabus, hadwritten a couple of chapters,

and had run a few samplelessons in the classroom.Weall felt that we had madegood progress. One day, aswe were discussingprocedures for estimatinguncertain quantities, the ideaof conducting an exerciseoccurred to me. I askedeveryone to write down anestimateofhowlongitwouldtake us to submit a finisheddraft of the textbook to theMinistry of Education. Iwas

followingaprocedurethatwealready planned toincorporate into ourcurriculum:theproperwaytoelicit information from agroupisnotbystartingwithapublic discussion but byconfidentially collecting eachperson’s judgment. Thisproceduremakesbetteruseofthe knowledge available tomembers of the group thanthecommonpracticeofopendiscussion. I collected the

estimates and jotted theresults on the blackboard.Theywerenarrowlycenteredaroundtwoyears;thelowendwas one and a half, the highendtwoandahalfyears.Then I had another idea. I

turned to Seymour, ourcurriculum expert, and askedwhether he could think ofother teams similar to oursthat had developed acurriculumfromscratch.Thiswas a time when several

pedagogical innovations like“new math” had beenintroduced,andSeymoursaidhecouldthinkofquiteafew.Ithenaskedwhetherheknewthe history of these teams insomedetail,anditturnedoutthat he was familiar withseveral. I asked him to thinkoftheseteamswhentheyhadmadeasmuchprogressaswehad. How long, from thatpoint, did it take them tofinishtheirtextbookprojects?

He fell silent. When hefinallyspoke,itseemedtomethat he was blushing,embarrassed by his ownanswer: “You know, I neverrealized this before, but infact not all the teams at astagecomparabletoourseverdid complete their task. Asubstantial fraction of theteams ended up failing tofinishthejob.”Thiswasworrisome;wehad

never considered the

possibilitythatwemightfail.My anxiety rising, I askedhow large he estimated thatfractionwas.Rwl剢sideringt20;About 40%,” heanswered. By now, a pall ofgloom was falling over theroom.Thenext questionwasobvious: “Those whofinished,”Iasked.“Howlongdid it take them?” “I cannotthink of any group thatfinished in less than sevenyears,” he replied, “nor any

thattookmorethanten.”Igraspedatastraw:“When

you compare our skills andresourcestothoseoftheothergroups, how good are we?How would you rank us incomparison with theseteams?” Seymour did nothesitate long this time.“We’re below average,” hesaid,“butnotbymuch.”Thiscame as a complete surpriseto all of us—includingSeymour, whose prior

estimatehadbeenwellwithinthe optimistic consensus ofthe group. Until I promptedhim,therewasnoconnectionin his mind between hisknowledge of the history ofother teams and his forecastofourfuture.Our stateofmindwhenwe

heard Seymour is not welldescribedby statingwhatwe“knew.” Surely all of us“knew” that a minimum ofseven years and a 40%

chanceof failurewasamoreplausible forecast of the fateof our project than thenumbers we had written onour slips of paper a fewminutes earlier. But we didnot acknowledge what weknew. The new forecast stillseemed unreal, because wecould not imagine how itcouldtakesolongtofinishaproject that looked somanageable. No crystal ballwas available to tell us the

strange sequence of unlikelyeventsthatwereinourfuture.All we could see was areasonable plan that shouldproduce a book in about twoyears, conflicting withstatistics indicating thatotherteamshadfailedorhadtakenan absurdly long time tocomplete theirmission.Whatwe had heard was base-rateinformation, from which weshouldhave inferredacausalstory: if so many teams

failed, and if those thatsucceeded took so long,writing a curriculum wassurely much harder than wehad thought. But such aninference would haveconflicted with our directexperience of the goodprogress we had beenmaking. The statistics thatSeymour provided weretreatedasbaseratesnormallyare—noted and promptly setaside.

We should have quit thatday. None of us was willingto invest six more years ofworkinaprojectwitha40%chance of failure. Althoughwe must have sensed thatpersevering was notreasonable, the warning didnot provide an immediatelycompelling reason to quit.After a few minutes ofdesultorydebate,wegatheredourselvestogetherandcarriedon as if nothing had

happened. The book waseventually completed eight(!)yearslater.BythattimeIwasno longer living in Israelandhad long since ceased to bepart of the team, whichcompletedthetaskaftermanyunpredictable vicissitudes.Theinitialenthusiasmfortheidea in the Ministry ofEducation had waned by thetime the text was deliveredanditwasneverused.This embarrassing episode

remains one of the mostinstructiveexperiencesofmyprofessional life. I eventuallylearned three lessons from it.The first was immediatelyapparent:Ihadstumbledontoa distinction between twoprofoundly differentapproaches to forecasting,which Amos and I laterlabeled the inside view andtheoutsideview.The secondlesson was that our initialforecasts of about two years

for the completion of theproject exhibited a planningfallacy. Our estimates werecloser toabest-case scenariothantoarealisticassessment.I was slower to accept thethird lesson, which I callirrational perseverance: thefollywedisplayedthatdayinfailingtoabandontheproject.Facing a choice,we gave uprationalityratherthangiveuptheenterprise.

DrawntotheInsideView

On that long-ago Friday, ourcurriculum expert made twojudgments about the sameproblem and arrived at verydifferent answers. The insideview is theone thatallofus,including Seymour,spontaneously adopted toassess the future of ourproject. We focused on ourspecific circumstances and

searched for evidence in ourown experiences. We had asketchy plan: we knew howmanychaptersweweregoingtowrite, andwe had an ideaofhowlongithadtakenustowrite the two that we hadalready done. The morecautious among us probablyadded a few months to theirestimateasamarginoferror.Extrapolating was a

mistake.Wewereforecastingbased on the information in

front of us—WYSIATI—butthe chapters we wrote firstwere probably easier thanothers, and our commitmentto the project was probablythenatitspeak.Butthemainproblemwasthatwefailedtoallow for what DonaldRumsfeldfamouslycalledthe“unknownunknowns.”Therewasnowayforustoforesee,that day, the succession ofevents that would cause theproject to drag out for so

long. The divorces, theillnesses, the crises ofcoordination withbureaucraciesthatdelayedthework could not beanticipated. Such events notonly cause the writing ofchapters to slow down, theyalso produce long periodsduring which little or noprogress is made at all. Thesamemusthavebeentrue,ofcourse, for the other teamsthat Seymour knew about.

Themembers of those teamswere also unable to imaginethe events that would causethem to spendsevenyears tofinish, or ultimately fail tofinish, a project that theyevidently had thought wasvery feasible. Like us, theydid not know the odds theywere facing. There aremanywaysforanyplantofail,andalthoughmostofthemaretooimprobable to be anticipated,the likelihood that something

willgowronginabigprojectishigh.ThesecondquestionIasked

Seymour directed hisattention away from us andtoward a class of similarcases.Seymourestimated thebase rate of success in thatreference class: 40% failureand seven to ten years forcompletion. His informalsurvey was surely not up toscientific standards ofevidence, but it provided a

reasonable basis for abaseline prediction: theprediction you make about acase if you know nothingexcept the category towhichitbelongs.Aswesawearlier,thebaselinepredictionshouldbe the anchor for furtheradjustments. Ifyouareaskedto guess the height of awoman about whom youknow only that she lives inNewYorkCity,yourbaselineprediction is your best guess

of the average height ofwomeninthecity.Ifyouarenow given case-specificinformation, forexample thatthe woman’s son is thestarting center of his highschool basketball team, youwill adjust your estimateaway from the mean in theappropriate direction.Seymour’scomparisonofourteam toothers suggested thatthe forecast of our outcomewas slightly worse than the

baseline prediction, whichwasalreadygrim.Thespectacularaccuracyof

the outside-view forecast inour problem was surely aflukeandshouldnotcountasevidence for the validity ofthe outside view. Theargumentfortheoutsideviewshould be made on generalgrounds:ifthereferenceclassis properly chosen, theoutside view will give anindication of where the

ballpark is, and it maysuggest,as itdid inourcase,that the inside-view forecastsarenotevenclosetoit.For a psychologist, the

discrepancy betweenSeymour’s two judgments isstriking. He had in his headall theknowledgerequiredtoestimate the statistics of anappropriate reference class,but he reached his initialestimate without ever usingthat knowledge. Seymour’s

forecast from his insidethaaview was not an adjustmentfrom the baseline prediction,which had not come to hismind. It was based on theparticular circumstances ofour efforts. Like theparticipants in the Tom Wexperiment, Seymour knewthe relevantbase ratebutdidnotthinkofapplyingit.UnlikeSeymour, therestof

usdidnothaveaccess to theoutside view and could not

have produced a reasonablebaseline prediction. It isnoteworthy,however,thatwedid not feel we neededinformationaboutotherteamsto make our guesses. Myrequest for the outside viewsurprised all of us, includingme! This is a commonpattern: people who haveinformation about anindividualcaserarelyfeeltheneedtoknowthestatisticsofthe class to which the case

belongs.When we were eventually

exposed to the outside view,wecollectivelyignoredit.Wecanrecognizewhathappenedto us; it is similar to theexperimentthatsuggestedthefutility of teachingpsychology.Whentheymadepredictions about individualcasesaboutwhichtheyhadalittle information(abriefandbland interview),Nisbett andBorgida’s students

completely neglected theglobal results they had justlearned. “Pallid” statisticalinformation is routinelydiscarded when it isincompatible with one’spersonal impressions of acase. In the competitionwiththe inside view, the outsideviewdoesn’tstandachance.The preference for the

insideviewsometimescarriesmoralovertones.Ionceaskedmy cousin, a distinguished

lawyer, a question about areference class: “What is theprobability of the defendantwinning in cases like thisone?” His sharp answer that“every case is unique” wasaccompanied by a look thatmade it clear he found myquestion inappropriate andsuperficial.Aproudemphasison theuniquenessofcases isalso common inmedicine, inspite of recent advances inevidence-basedmedicine that

point the otherway.Medicalstatistics and baselinepredictions come up withincreasing frequency inconversations betweenpatients and physicians.However, the remainingambivalence about theoutside view in the medicalprofession is expressed inconcerns about theimpersonality of proceduresthat are guided by statisticsandchecklists.

ThePlanningFallacyIn light of both the outside-view forecast and theeventual outcome, theoriginal estimates we madethat Friday afternoon appearalmost delusional. Thisshouldnotcomeasasurprise:overly optimistic forecasts ofthe outcome of projects arefoundeverywhere.AmosandI coined the term planningfallacy to describe plans and

forecaststhat

are unrealistically closetobest-casescenarioscould be improved byconsulting the statisticsofsimilarcases

Examples of the planning

fallacy abound in theexperiences of individuals,governments, andbusinesses.

The list of horror stories isendless.

In July 1997, theproposed new ScottishParliament building inEdinburgh wasestimated to cost up to£40 million. By June1999, thebudget for thebuilding was £109million. In April 2000,legislators imposed a

£195 million “cap oncosts.” By November2001, theydemandedanestimate of “final cost,”which was set at £241million. That estimatedfinal cost rose twice in2002,ending theyearat£294.6 million. It rosethree times more in2003, reaching £375.8million by June. Thebuilding was finallycomanspleted in2004at

an ultimate cost ofroughly£431million.A 2005 study examinedrail projects undertakenworldwide between1969 and1998. Inmorethan 90% of the cases,the number ofpassengers projected touse the system wasoverestimated. Eventhough these passengershortfalls were widelypublicized, forecasts did

not improve over thosethirty years; on average,planners overestimatedhowmanypeoplewouldusethenewrailprojectsby 106%, and theaverage cost overrunwas 45%. As moreevidence accumulated,the experts did notbecome more reliant onit.In 2002, a survey ofAmerican homeowners

whohadremodeledtheirkitchens found that, onaverage, they hadexpected the job to cost$18,658; in fact, theyended up paying anaverageof$38,769.

Theoptimismofplannersanddecision makers is not theonly cause of overruns.Contractors of kitchenrenovations and of weapon

systemsreadilyadmit(thoughnot to their clients) that theyroutinely make most of theirprofit on additions to theoriginal plan. The failures offorecasting in these casesreflect the customers’inability to imagine howmuch their wishes willescalate over time. They endup paying much more thantheywould if they hadmadearealisticplanandstucktoit.Errors in the initial budget

are not always innocent. Theauthors of unrealistic plansareoftendrivenbythedesireto get the plan approved—whether by their superiors orbyaclient—supportedbytheknowledge that projects arerarely abandoned unfinishedmerelybecauseofoverrunsincosts or completion times. Insuch cases, the greatestresponsibilityforavoidingtheplanning fallacy lieswith thedecisionmakerswhoapprove

the plan. If they do notrecognize the need for anoutside view, they commit aplanningfallacy.

MitigatingthePlanningFallacy

The diagnosis of and theremedy for the planningfallacy have not changedsince that Friday afternoon,buttheimplementationoftheidea has come a long way.

The renowned Danishplanning expert BentFlyvbjerg, now at OxfordUniversity,offereda forcefulsummary:

The prevalent tendencytounderweightorignoredistributionalinformation is perhapsthemajorsourceoferrorin forecasting. Plannersshould therefore makeeveryefforttoframethe

forecasting problem soas to facilitate utilizingall the distributionalinformation that isavailable.

This may be considered thesingle most important pieceof advice regarding how toincrease accuracy inforecasting through improvedmethods. Using suchdistributional informationfromotherventuressimilarto

thatbeingforecastediscalledtakingan“outsideview”andis the cure to the planningfallacy.The treatment for the

planning fallacy has nowacquired a technical name,reference class forecasting,and Flyvbjerg has applied itto transportation projects inseveralcountries.Theoutsideviewisimplementedbyusinga large database, whichprovides information on both

plans and outcomes forhundreds of projects all overtheworld,andcanbeusedtoprovidestatisticalinformationabout the likely overruns ofcost and time, and about thelikely underperformance ofprojectsofdifferenttypes.Theforecastingmethodthat

Flyvbjergappliesissimilartothe practices recommendedfor overcoming base-rateneglect:

1. Identify an appropriatereference class (kitchenrenovations, largerailwayprojects,etc.).

2. Obtain the statistics ofthe reference class (intermsofcostpermileofrailway, or of thepercentage by whichexpenditures exceededbudget). Use thestatistics to generate abaselineprediction.

3. Usespecific information

about the case to adjustthe baseline prediction,if there are particularreasons to expect theoptimistic bias to bemoreorlesspronouncedin this project than inothersofthesametype.

Flyvbjerg’s analyses areintended to guide theauthorities that commissionpublic projects, by providing

the statistics of overruns insimilar projects. Decisionmakers need a realisticassessment of the costs andbenefits of a proposal beforemaking the final decision toapprove it. They may alsowish to estimate the budgetreserve that they need inanticipation of overruns,although such precautionsoften become self-fulfillingprophecies. As one officialtold Flyvbjerg, “A budget

reserve is to contractors asredmeat is to lions,andtheywilldevourit.”Organizations face the

challenge of controlling thetendency of executivescompeting for resources topresent overly optimisticplans. A well-runorganization will rewardplannersforpreciseexecutionand penalize them for failingto anticipate difficulties, andfor failing to allow for

difficultiesthattheycouldnothave anticipated—theunknownunknowns.

DecisionsandErrorsThat Friday afternoonoccurred more than thirtyyears ago. I often thoughtabout it and mentioned it inlectures several times eachyear.Someofmyfriendsgotbored with the story, but Ikept drawing new lessons

from it. Almost fifteen yearsafter I first reported on theplanningfallacywithAmos,IreturnedtothetopicwithDanLovallo. Together wesketcheda theoryofdecisionmaking in which theoptimisticbiasisasignificantsource of risk taking. In thestandard rational model ofeconomics, people take risksbecause the odds arefavorable—they accept someprobabilityofacostly failure

because the probability ofsuccess is sufficient. Weproposedanalternativeidea.When forecasting the

outcomes of risky projects,executives too easily fallvictimtotheplanningfallacy.In its grip, they makedecisionsbasedondelusionaloptimism rather than on arational weighting of gains,losses, and probabilities.They overestimate benefitsand underestimate costs.

They spin scenarios ofsuccesswhileoverlookingthepotential for mistakes andmiscalculations. As a result,theypursueinitiativesthatareunlikelytocomeinonbudgetor on time or to deliver theexpected returns—or even tobecompleted.In this view, people often

(butnotalways)takeonriskyprojects because they areoverly optimistic about theoddstheyface.Iwillreturnto

this ideaseveral times in thisbook—itprobablycontributesto an explanation of whypeoplelitigate,whytheystartwars, and why they opensmallbusinesses.

FailingaTestFor many years, I thoughtthat the main point of thecurriculum story was what Ihad learned about my friendSeymour: that his best guess

aboutthefutureofourprojectwasnot informedbywhatheknewaboutsimilarprojects.Icame off quite well in mytelling of the story, ir InwhichIhadtheroleofcleverquestioner and astutepsychologist. I only recentlyrealized that I had actuallyplayed the roles of chiefdunceandineptleader.The project was my

initiative,anditwasthereforemy responsibility to ensure

that it made sense and thatmajor problems wereproperly discussed by theteam, but I failed that test.My problem was no longerthe planning fallacy. I wascured of that fallacy as soonas I heard Seymour’sstatistical summary. Ifpressed, I would have saidthat our earlier estimates hadbeen absurdly optimistic. Ifpressedfurther, Iwouldhaveadmitted that we had started

theprojectonfaultypremisesand that we should at leastconsider seriously the optionofdeclaringdefeatandgoinghome. But nobody pressedme and there was nodiscussion; we tacitly agreedto go on without an explicitforecast of how long theeffort would last. This waseasy to do because we hadnot made such a forecast tobegin with. If we had had areasonable baseline

prediction when we started,wewouldnothavegone intoit, but we had alreadyinvestedagreatdealofeffort—an instance of the sunk-cost fallacy, which we willlook at more closely in thenext part of the book. Itwould have beenembarrassing for us—especiallyforme—togiveupat that point, and thereseemed to be no immediatereasontodoso.Itiseasierto

change directions in a crisis,butthiswasnotacrisis,onlysome new facts about peoplewedidnotknow.Theoutsideview was much easier toignore than bad news in ourown effort. I can bestdescribeourstateasaformoflethargy—anunwillingnesstothink about what hadhappened. So we carried on.Therewasno furtherattemptat rational planning for therest of the time I spent as a

member of the team—aparticularly troublingomissionforateamdedicatedtoteachingrationality.IhopeI amwiser today, and Ihaveacquired a habit of lookingfor the outside view. But itwillneverbethenaturalthingtodo.

SpeakingoftheOutsideView

“He’s taking an inside

view. He should forgetabout his own case andlook for what happenedinothercases.”

“She is the victim of aplanning fallacy. She’sassuming a best-casescenario, but there aretoomanydifferentwaysfor the plan to fail, andshe cannot foresee themall.”

“Suppose you did notknow a thing about thisparticular legal case,only that it involves amalpractice claim by anindividual against asurgeon.Whatwouldbeyour baselineprediction? How manyofthesecasessucceedincourt?Howmanysettle?What are the amounts?

Is the case we arediscussing stronger orweaker than similarclaims?”

“We are making anadditional investmentbecausewe do notwantto admit failure. This isan instance of the sunk-costfallacy.”

P

TheEngineofCapitalism

The planning fallacy is onlyoneofthemanifestationsofapervasive optimistic bias. sidto adtions of aMost of usview the world as morebenign than it really is, ourown attributes as morefavorable than they truly are,and the goals we adopt as

moreachievablethantheyarelikely to be.We also tend toexaggerate our ability toforecast the future, whichfosters optimisticoverconfidence. In terms ofits consequences fordecisions, the optimistic biasmay well be the mostsignificant of the cognitivebiases. Because optimisticbias can be both a blessingandarisk,youshouldbebothhappy and wary if you are

temperamentallyoptimistic.

OptimistsOptimism is normal, butsome fortunate people aremore optimistic than the restof us. If you are geneticallyendowed with an optimisticbias, you hardly need to betold that you are a luckyperson—you already feelfortunate. An optimisticattitude is largely inherited,

and it is part of a generaldisposition for well-being,which may also include apreference for seeing thebright side of everything. Ifyou were allowed one wishfor your child, seriouslyconsider wishing him or heroptimism. Optimists arenormallycheerfulandhappy,and therefore popular; theyare resilient in adapting tofailures and hardships, theirchancesofclinicaldepression

are reduced, their immunesystem is stronger, they takebetter care of their health,theyfeelhealthierthanothersand are in fact likely to livelonger. A study of peoplewho exaggerate theirexpected life span beyondactuarial predictions showedthat they work longer hours,are more optimistic abouttheir future income,aremorelikelytoremarryafterdivorce(theclassic“triumphofhope

over experience”), and aremore prone to bet onindividual stocks. Of course,theblessingsofoptimismareoffered only to individualswho are only mildly biasedand who are able to“accentuate the positive”without losing track ofreality.Optimistic individuals play

a disproportionate role inshaping our lives. Theirdecisions make a difference;

they are the inventors, theentrepreneurs, the politicaland military leaders—notaverage people. They got towhere they are by seekingchallenges and taking risks.They are talented and theyhave been lucky, almostcertainly luckier than theyacknowledge. They areprobably optimistic bytemperament; a survey offounders of small businessesconcluded that entrepreneurs

are more sanguine thanmidlevelmanagers about lifein general. Their experiencesof success have confirmedtheir faith in their judgmentand in their ability to controlevents. Their self-confidenceis reinforced by theadmiration of others. Thisreasoning leads to ahypothesis: the people whohavethegreatestinfluenceonthe lives of others are likelyto be optimistic and

overconfident, and to takemorerisksthantheyrealize.

Theevidencesuggeststhatanoptimisticbiasplaysarole—sometimes the dominant role—whenever individuals orinstitutions voluntarily takeon significant risks. Moreoften than not, risk takersunderestimate the odds theyface, and do invest sufficienteffort to find out what the

odds are. Because theymisread the risks, optimisticentrepreneurs often believethey are prudent, even whentheyarenot.Theirconfidencein their future successsustains a positivemood thathelps them obtain resourcesfromothers, raise themoraleof their employees, andenhance their prospects ofprevailing. When action isneeded,optimism,evenofthemildly delusional variety,

maybeagoodthing.

EntrepreneurialDelusions

The chances that a smallbusiness will thesurvive forfiveyearsintheUnitedStatesare about 35%. But theindividuals who open suchbusinessesdonotbelievethatthestatisticsapplytothem.Asurvey found that Americanentrepreneurs tend to believe

theyareinapromisinglineofbusiness: their averageestimate of the chances ofsuccessfor“anybusinesslikeyours” was 60%—almostdouble the true value. Thebias was more glaring whenpeople assessed the odds oftheirownventure.Fully81%of theentrepreneursput theirpersonaloddsofsuccessat7outof10orhigher,and33%said their chance of failingwaszero.

The direction of the bias isnot surprising. If youinterviewed someone whorecently opened an Italianrestaurant, you would notexpect her to haveunderestimated her prospectsfor successor tohaveapoorview of her ability as arestaurateur. But you mustwonder:Wouldshestillhaveinvested money and time ifshe had made a reasonableefforttolearntheodds—or,if

she did learn the odds (60%of new restaurants are out ofbusiness after three years),paid attention to them? Theidea of adopting the outsideviewprobablydidn’toccurtoher.One of the benefits of an

optimistic temperament isthat it encouragespersistencein the face of obstacles. Butpersistencecanbecostly.Animpressive series of studiesby Thomas Åstebro sheds

light on what happens whenoptimists receive bad news.He drew his data from aCanadian organization—theInventor’s AssistanceProgram—which collects asmallfeetoprovideinventorswith an objective assessmentof the commercial prospectsof their idea.Theevaluationsrelyoncarefulratingsofeachinvention on 37 criteria,including need for theproduct, cost of production,

and estimated trend ofdemand. The analystssummarize their ratings by aletter grade, where D and Epredict failure—a predictionmade for over 70% of theinventions they review. Theforecasts of failure areremarkably accurate: only 5of 411 projects that weregiven the lowest gradereached commercialization,andnonewassuccessful.Discouraging news led

abouthalfof the inventors toquit after receiving a gradethat unequivocally predictedfailure. However, 47% ofthem continued developmentefforts even after being toldthat their project washopeless, and on averagethesepersistent (orobstinate)individuals doubled theirinitiallossesbeforegivingup.Significantly, persistenceafterdiscouragingadvicewasrelatively common among

inventors who had a highscore on a personalitymeasure of optimism—onwhich inventors generallyscoredhigherthanthegeneralpopulation. Overall, thereturn on private inventionwas small, “lower than thereturn on private equity andonhigh-risksecurities.”Moregenerally, the financialbenefits of self-employmentaremediocre:giventhesamequalifications,peopleachieve

higher average returns byselling their skills toemployersthanbysettingouton their own. The evidencesuggests that optimism iswidespread, stubborn, andcostly.Psychologists have

confirmed that most peoplegenuinely believe that theyaresuperiortomostothersonmost desirable traits—theyare willing to bet smallamounts of money on these

beliefs in the laboratory. Inthemarket,of course,beliefsin one’s superiority havesignificant consequences.Leaders of large businessessometimesmakehugebetsinexpensive mergers andacquisitions, acting on themistaken belief that they canmanage the assets of anothercompany better than itscurrentownersdo.The stockmarket commonly respondsby downgrading the value of

the acquiring firm, becauseexperience has shown thateffortstointegratelargefirmsfail more often than theysucceed. The misguidedacquisitions have beenexplained by a “hubrishypothesis”:theeivxecutivesof the acquiring firm aresimply less competent thantheythinktheyare.The economists Ulrike

Malmendier and GeoffreyTate identified optimistic

CEOs by the amount ofcompany stock that theyowned personally andobserved that highlyoptimistic leaders tookexcessive risks. Theyassumed debt rather thanissue equity and were morelikelythanothersto“overpayfor target companies andundertake value-destroyingmergers.” Remarkably, thestock of the acquiringcompany suffered

substantiallymoreinmergersif the CEO was overlyoptimistic by the authors’measure.Thestockmarket isapparently able to identifyoverconfident CEOs. Thisobservation exonerates theCEOs from one accusationeven as it convicts them ofanother: the leaders ofenterprises who makeunsound bets do not do sobecause theyarebettingwithotherpeople’smoney.Onthe

contrary, they take greaterrisks when they personallyhave more at stake. Thedamage caused byoverconfident CEOs iscompounded when thebusiness press anoints themas celebrities; the evidenceindicates that prestigiouspress awards to theCEO arecostly to stockholders. Theauthors write, “We find thatfirms with award-winningCEOs subsequently

underperform, in terms bothof stock and of operatingperformance. At the sametime, CEO compensationincreases, CEOs spend moretime on activities outside thecompany such as writingbooks and sitting on outsideboards, and they are morelikely to engage in earningsmanagement.”

Manyyearsago,mywifeand

I were on vacation onVancouverIsland,lookingforaplace to stay.We foundanattractive but deserted motelonalittle-traveledroadinthemiddle of a forest. Theowners were a charmingyoung couple who neededlittlepromptingtotellustheirstory. They had beenschoolteachers in theprovinceofAlberta;theyhaddecided to change their lifeandused their life savings to

buy this motel, which hadbeen built a dozen yearsearlier. They told us withoutirony or self-consciousnessthattheyhadbeenabletobuyit cheap, “because six orseven previous owners hadfailed to make a go of it.”Theyalsotoldusaboutplansto seek a loan to make theestablishmentmore attractiveby building a restaurant nextto it. They felt no need toexplainwhytheyexpectedto

succeed where six or sevenothershad failed.Acommonthread of boldness andoptimism linksbusinesspeople, from motelownerstosuperstarCEOs.Theoptimisticrisktakingof

entrepreneurs surelycontributes to the economicdynamism of a capitalisticsociety, even if most risktakers end up disappointed.However, Marta Coelho ofthe London School of

Economics has pointed outthedifficultpolicyissuesthatarisewhen founders of smallbusinesses ask thegovernment to support themin decisions that are mostlikely to end badly. Shouldthegovernmentprovideloansto would-be entrepreneurswho probably will bankruptthemselves in a few years?Many behavioral economistsare comfortable with the“libertarian paternalistic”

procedures that help peopleincrease their savings ratebeyond what they would doontheirown.Thequestionofwhetherandhowgovernmentshouldsupportsmallbusinessdoes not have an equallysatisfyinganswer.

CompetitionNeglectIt is tempting to explainentrepreneurial optimism bywishfulthinking,butemotion

is only part of the story.Cognitive biases play animportant role, notably theSystem1featureWYSIATI.

We focus on our goal,anchor on our plan, andneglect relevant baserates,exposingourselvesto tnesehe planningfallacy.We focus on what wewant to do and can do,

neglecting the plans andskillsofothers.Both in explaining thepast and in predictingthe future, we focus onthe causal role of skilland neglect the role ofluck. We are thereforeprone to an illusion ofcontrol.We focus on what weknow and neglect whatwe do not know, whichmakes us overly

confidentinourbeliefs.

The observation that “90%

of drivers believe they arebetterthanaverage”isawell-established psychologicalfinding that has become partof the culture, and it oftencomesupasaprimeexampleof a more general above-average effect. However, theinterpretation of the findinghas changed in recent years,

fromself-aggrandizementtoacognitivebias.Considerthesetwoquestions:

Areyouagooddriver?Are you better thanaverageasadriver?

Thefirstquestioniseasyandthe answer comes quickly:most drivers say yes. Thesecond question is muchharder and for mostrespondents almost

impossible to answerseriously and correctly,because it requires anassessment of the averagequality of drivers. At thispoint in thebook itcomesasno surprise that peoplerespondtoadifficultquestionby answering an easier one.They compare themselves tothe average without everthinking about the average.The evidence for thecognitiveinterpretationofthe

above-average effect is thatwhen people are asked abouta task they find difficult (formanyofusthiscouldbe“Areyou better than average instarting conversations withstrangers?”),theyreadilyratethemselvesasbelowaverage.Theupshotisthatpeopletendto be overly optimistic abouttheir relativestandingonanyactivity in which they domoderatelywell.Ihavehadseveraloccasions

to ask founders andparticipants in innovativestart-upsaquestion:Towhatextent will the outcome ofyour effort depend on whatyou do in your firm?This isevidently an easy question;theanswercomesquicklyandin my small sample it hasnever been less than 80%.Evenwhen they are not suretheywill succeed, these boldpeople think their fate isalmost entirely in their own

hands. They are surelywrong:theoutcomeofastart-up depends as much on theachievements of itscompetitors and on changesin the market as on its ownefforts.However,WYSIATIplays its part, andentrepreneurs naturally focuson what they know best—their plans and actions andthe most immediate threatsandopportunities,suchastheavailability of funding. They

know less about theircompetitors and thereforefind it natural to imagine afuture in which thecompetitionplayslittlepart.Colin Camerer and Dan

Lovallo, who coined theconcept of competitionneglect, illustrated it with aquotefromthethenchairmanof Disney Studios. Askedwhy somany expensive big-budget movies are releasedon the same days (such as

Memorial Day andIndependence Day), hereplied:

Hubris. Hubris. If youonly think about yourownbusiness,youthink,“I’ve got a good storydepartment, I’ve got agood marketingdepartment,we’re goingto go out and do this.”Andyoudon’tthinkthateverybody else is

thinking the same way.Inagivenweekend inayear you’ll have fivemoviesopen,andthere’scertainly not enoughpeopletogoaround.re

The candid answer refers tohubris, but it displays noarrogance, no conceit ofsuperiority to competingstudios. The competition issimply not part of thedecision, inwhich a difficult

question has again beenreplaced by an easier one.The question that needs ananswer is this: Consideringwhat others will do, howmany people will see ourfilm?Thequestionthestudioexecutives considered issimpler and refers toknowledgethatismosteasilyavailable to them: Do wehaveagood filmandagoodorganization to market it?The familiar System 1

processes ofWY SIATI andsubstitution produce bothcompetition neglect and theabove-average effect. Theconsequence of competitionneglect is excess entry:morecompetitors enter the marketthanthemarketcanprofitablysustain, so their averageoutcome is a loss. Theoutcome is disappointing forthe typical entrant in themarket, but the effect on theeconomy as a whole could

well be positive. In fact,Giovanni Dosi and DanLovallo call entrepreneurialfirms that failbut signalnewmarkets to more qualifiedcompetitors “optimisticmartyrs”—good for theeconomy but bad for theirinvestors.

OverconfidenceFor a number of years,professorsatDukeUniversity

conducted a survey in whichthe chief financial officers oflarge corporations estimatedthereturnsof theStandard&Poor’s index over thefollowing year. The Dukescholars collected 11,600such forecasts and examinedtheir accuracy. Theconclusion wasstraightforward: financialofficers of large corporationshad no clue about the short-term future of the stock

market; the correlationbetween their estimates andthe true value was slightlyless than zero! When theysaid the market would godown, it was slightly morelikely than not that it wouldgoup.Thesefindingsarenotsurprising. The truly badnewsisthattheCFOsdidnotappear to know that theirforecastswereworthless.In addition to their best

guess aboutS&P returns, the

participants provided twoother estimates: a value thattheywere90%surewouldbetoo high, and one that theywere 90% surewould be toolow. The range between thetwovalues iscalledan“80%confidence interval” andoutcomesthatfalloutsidetheinterval are labeled“surprises.” An individualwhosetsconfidence intervalsonmultipleoccasionsexpectsabout20%oftheoutcomesto

be surprises. As frequentlyhappens in such exercises,there were far too manysurprises; their incidencewas67%, more than 3 timeshigher than expected. Thisshows that CFOs weregrossly overconfident abouttheir ability to forecast themarket. Overconfidence isanother manifestation ofWYSIATI:whenweestimatea quantity, we rely oninformation that comes to

mindandconstructacoherentstory in which the estimatemakes sense. Allowing forthe information that does notcome to mind—perhapsbecause one never knew it—isimpossible.The authors calculated the

confidence intervals thatwould have reduced theincidenceofsurprisesto20%.The resultswere striking. Tomaintain the rateof surprisesatthedesiredlevel,theCFOs

should have said, year afteryear, “There is an 80%chance that the S&P returnnext year will be between –10% and +30%.” Theconfidence interval thatproperly reflects the CFOs’knowledge (more precisely,their ignorance) ismore than4 times wider than theintervalstheyactuallystated.Social psychology comes

into thepicturehere,becausethe answer that a truthful

CFO would offer is plainlyridiculous. A CFO whoinforms his colleagues that“th%">iere is a good chancethat the S&P returns will bebetween –10% and +30%”can expect to be laughed outof the room. The wideconfidence interval is aconfession of ignorance,which is not sociallyacceptable for someone whois paid to be knowledgeablein financial matters. Even if

they knew how little theyknow, the executives wouldbepenalized for admitting it.President Truman famouslyasked for a “one-armedeconomist”whowouldtakeaclear stand; he was sick andtiredofeconomistswhokeptsaying, “On the otherhand…”Organizations that take the

wordofoverconfidentexpertscan expect costlyconsequences. The study of

CFOsshowedthat thosewhowere most confident andoptimistic about the S&Pindexwerealsooverconfidentand optimistic about theprospects of their own firm,which went on to take morerisk than others. As NassimTaleb has argued, inadequateappreciation of theuncertainty of theenvironment inevitably leadseconomicagentstotakerisksthey should avoid. However,

optimism is highly valued,socially and in the market;people and firms reward theproviders of dangerouslymisleading information morethantheyrewardtruthtellers.One of the lessons of thefinancialcrisis that led to theGreat Recession is that thereare periods in whichcompetition, among expertsand among organizations,creates powerful forces thatfavoracollectiveblindnessto

riskanduncertainty.The social and economic

pressures that favoroverconfidence are notrestricted to financialforecasting. Otherprofessionals must deal withthefactthatanexpertworthyof the name is expected todisplay high confidence.Philip Tetlock observed thatthe most overconfidentexperts were the most likelyto be invited to strut their

stuff in news shows.Overconfidence also appearstobeendemicinmedicine.Astudyofpatientswhodied inthe ICU compared autopsyresultswiththediagnosisthatphysicians had providedwhile the patients were stillalive. Physicians alsoreported their confidence.The result: “clinicians whowere ‘completely certain’ ofthe diagnosis antemortemwere wrong 40% of the

time.” Here again, expertoverconfidenceisencouragedbytheirclients:“Generally,itisconsideredaweaknessanda sign of vulnerability forclinicians to appear unsure.Confidence is valued overuncertainty and there is aprevailing censure againstdisclosing uncertainty topatients.” Experts whoacknowledge the full extentoftheirignorancemayexpectto be replaced by more

confident competitors, whoare better able to gain thetrust of clients. An unbiasedappreciation of uncertainty isacornerstoneof rationality—but it is notwhat people andorganizations want. Extremeuncertainty is paralyzingunder dangerouscircumstances, and theadmission that one is merelyguessing is especiallyunacceptablewhen thestakesarehigh.Actingonpretended

knowledge is often thepreferredsolution.When they come together,

the emotional, cognitive, andsocial factors that supportexaggerated optimism are aheady brew, whichsometimes leads people totake risks that they wouldavoid if they knew the odds.Thereisnoevidencethatrisktakers in the economicdomain have an unusualappetite for gambles on high

stakes; they are merely lessaware of risks than moretimidpeopleare.DanLovalloandIcoinedthephrase“boldforecastsandtimiddecisions”todescribethebackgroundofrisktaking.

Theeffectsofhighoptimismon decision making are, atbest,amixedblessing,butthecontribution of optimism togood implementation is

certainly positive. The mainbenefit of optimism isresilience in the face ofsetbacks. According toMartinSeligman,thefounderof potelsitive psychology, an“optimisticexplanationstyle”contributes to resilience bydefending one’s self-image.In essence, the optimisticstyle involves taking creditfor successes but little blameforfailures.Thisstylecanbetaught, at least to some

extent, and Seligman hasdocumented the effects oftraining on variousoccupations that arecharacterized by a high rateof failures, such as cold-callsalesofinsurance(acommonpursuit in pre-Internet days).Whenonehasjusthadadoorslammed in one’s face by anangry homemaker, thethought that “she was anawful woman” is clearlysuperior to “I am an inept

salesperson.” I have alwaysbelieved that scientificresearch is another domainwhere a form of optimism isessential to success: I haveyet to meet a successfulscientistwholackstheabilityto exaggerate the importanceof what he or she is doing,and I believe that someonewho lacks a delusional senseofsignificancewillwiltintheface of repeated experiencesofmultiplesmallfailuresand

rare successes, the fate ofmostresearchers.

ThePremortem:APartialRemedy

Can overconfident optimismbe overcome by training? Iamnotoptimistic.Therehavebeen numerous attempts totrain people to stateconfidence intervals thatreflect the imprecision oftheir judgments, with only a

few reports of modestsuccess. An often citedexample is that geologists atRoyal Dutch Shell becameless overconfident in theirassessments of possibledrilling sites after trainingwith multiple past cases forwhich the outcome wasknown. In other situations,overconfidencewasmitigated(but not eliminated) whenjudges were encouraged toconsider competing

hypotheses. However,overconfidence is a directconsequence of features ofSystem1thatcanbetamed—butnotvanquished.Themainobstacle is that subjectiveconfidence is determined bythecoherenceofthestoryonehas constructed, not by thequality and amount of theinformationthatsupportsit.Organizationsmaybebetter

able to tame optimism andindividuals than individuals

are. The best idea for doingso was contributed by GaryKlein, my “adversarialcollaborator” who generallydefends intuitive decisionmakingagainstclaimsofbiasand is typically hostile toalgorithms. He labels hisproposal thepremortem. Theprocedureissimple:whentheorganizationhasalmostcometo an important decision buthas not formally committeditself, Klein proposes

gatheringforabriefsessionagroup of individualswho areknowledgeable about thedecision. The premise of thesession is a short speech:“Imagine that we are a yearinto the future. Weimplemented the plan as itnowexists.Theoutcomewasadisaster.Pleasetake5to10minutes to write a briefhistoryofthatdisaster.”Gary Klein’s idea of the

premortem usually evokes

immediate enthusiasm. AfterI described it casually at asession in Davos, someonebehind me muttered, “It wasworth coming to Davos justfor this!” (I laternoticed thatthespeakerwastheCEOofamajor internationalcorporation.) The premortemhas two main advantages: itovercomes the groupthinkthat affectsmany teamsoncea decision appears to havebeen made, and it unleashes

the imagination ofknowledgeable individuals inamuch-neededdirection.As a team converges on a

decision—and especiallywhentheleadertipsherhand—public doubts about thewisdomof theplannedmoveare gradually suppressed andeventuallycometobetreatedasevidenceofflawedloyaltyto the team and its leaders.The suppression of doubtcontributes tooverconfidence

in a group where onlysupporters of the decisionhave a v filepos-id="filepos726557"> naceaand does not providecomplete protection againstnasty surprises, but it goessome way toward reducingthe damage of plans that aresubject to the biases of WYSIATI and uncriticaloptimism.

Speakingof

Optimism

“They have an illusionof control. Theyseriously underestimatetheobstacles.”

“They seem to sufferfrom an acute case ofcompetitorneglect.”

“This is a case ofoverconfidence. They

seem to believe theyknow more than theyactuallydoknow.”

“We should conduct apremortem session.Someone may come upwith a threat we haveneglected.”

P

Part4

P

ChoicesP

Bernoulli’sErrors

One day in the early 1970s,Amos handed me amimeographed essay by aSwiss economist namedBruno Frey,which discussedthe psychologicalassumptions of economictheory. I vividly rememberthe color of the cover: darkred.BrunoFreybarelyrecalls

writing the piece, but I canstill recite its first sentence:“The agent of economictheoryisrational,selfish,andhistastesdonotchange.”I was astonished. My

economist colleaguesworkedinthebuildingnextdoor,butI had not appreciated theprofound difference betweenour intellectual worlds. To apsychologist,itisself-evidentthat people are neither fullyrational nor completely

selfish, and that their tastesare anything but stable. Ourtwo disciplines seemed to bestudying different species,which the behavioraleconomist Richard Thalerlater dubbed Econs andHumans.Unlike Econs, the Humans

thatpsychologistsknowhaveaSystem1.Theirviewoftheworld is limited by theinformation that is availableat a given moment

(WYSIATI), and thereforethey cannot be as consistentand logical as Econs. Theyare sometimes generous andoftenwilling to contribute tothe group to which they areattached.Andtheyoftenhavelittle idea of what they willlike next year or eventomorrow. Here was anopportunity for an interestingconversation across theboundariesof thedisciplines.I did not anticipate that my

career would be defined bythatconversation.Soon after he showed me

Frey’s article, Amossuggested that we make thestudyofdecisionmakingournext project. I knew next tonothing about the topic, butAmos was an expert and astar of the field, and heMathematical Psychology,and he directed me to a fewchapters that he thoughtwouldbeagoodintroduction.

I soon learned that oursubject matter would bepeople’s attitudes to riskyoptions and that we wouldseek to answer a specificquestion: What rules governpeople’s choices betweendifferent simple gambles andbetween gambles and surethings?Simple gambles (such as

“40% chance to win $300”)are to students of decisionmakingwhatthefruitflyisto

geneticists. Choices betweensuch gambles provide asimple model that sharesimportant features with themore complex decisions thatresearchers actually aim tounderstand. Gamblesrepresent the fact that theconsequences of choices arenevercertain.Evenostensiblysure outcomes are uncertain:whenyousignthecontracttobuyanapartment,youdonotknow the price atwhich you

latermay have to sell it, nordo you know that yourneighbor’ssonwillsoontakeupthetuba.Everysignificantchoicewemakeinlifecomeswith some uncertainty—which is why students ofdecision making hope thatsomeofthelessonslearnedinthe model situation will beapplicabletomoreinterestingeveryday problems. But ofcourse the main reason thatdecision theorists study

simplegambles is that this iswhat other decision theoristsdo.The field had a theory,

expectedutilitytheory,whichwas the foundation of therational-agentmodelandistothis day the most importanttheory in the social sciences.Expected utility theory wasnot intended as apsychologicalmodel;itwasalogic of choice, based onelementary rules (axioms) of

rationality. Consider thisexample:

Ifyoupreferanappletoabanana,thenyou also prefer a 10%chance to win an appletoa10%chancetowinabanana.

The apple and the bananastand for any objects ofchoice (including gambles),

and the 10% chance standsfor any probability. Themathematician John vonNeumann, one of the giantintellectual figures of thetwentieth century, and theeconomist OskarMorgenstern had derivedtheirtheoryofrationalchoicebetween gambles from a fewaxioms. Economists adoptedexpected utility theory in adual role: as a logic thatprescribes how decisions

should be made, and as adescription of how Econsmake choices. Amos and Iwerepsychologists, however,andwe set out to understandhow Humans actually makerisky choices, withoutassuminganythingabouttheirrationality.We maintained our routine

ofspendingmanyhourseachday in conversation,sometimes in our offices,sometimes at restaurants,

often on long walks throughthe quiet streets of beautifulJerusalem. As we had donewhen we studied judgment,we engaged in a carefulexamination of our ownintuitive preferences. Wespent our time inventingsimpledecisionproblemsandasking ourselves how wewouldchoose.Forexample:

Whichdoyouprefer?A. Toss a coin. If it

comesupheadsyouwin$100,andifitcomesuptailsyouwinnothing.B.Get$46forsure.

Wewere not trying to figureout the mos BineithWe trational or advantageouschoice;wewantedtofindtheintuitive choice, the one thatappeared immediatelytempting. We almost alwaysselected the same option. Inthis example, both of us

would have picked the surething, and you probablywoulddothesame.Whenweconfidently agreed on achoice, we believed—almostalways correctly, as it turnedout—thatmost peoplewouldshare our preference, andwemoved on as ifwe had solidevidence. We knew, ofcourse,thatwewouldneedtoverify our hunches later, butby playing the roles of bothexperimenters and subjects

we were able to movequickly.Five years after we began

our study of gambles, wefinally completed an essaythat we titled “ProspectTheory: An Analysis ofDecision under Risk.” Ourtheory was closely modeledonutility theorybutdepartedfrom it in fundamentalways.Most important, our modelwas purely descriptive, anditsgoalwastodocumentand

explain systematic violationsoftheaxiomsofrationalityinchoicesbetweengambles.Wesubmitted our essay toEconometrica, a journal thatpublishes significanttheoretical articles ineconomics and in decisiontheory. The choice of venueturnedout tobe important; ifwe had published theidentical paper in apsychological journal, itwould likely have had little

impact on economics.However, our decision wasnot guided by a wish toinfluence economics;Econometrica just happenedto be where the best papersondecisionmakinghadbeenpublishedinthepast,andwewere aspiring to be in thatcompany.Inthischoiceasinmany others,wewere lucky.Prospect theory turnedout tobe the most significant workweeverdid,andourarticleis

amongthemostoftencitedinthesocialsciences.Twoyearslater,wepublishedinScienceanaccountofframingeffects:the large changes ofpreferences that aresometimes caused byinconsequential variations inthe wording of a choiceproblem.During the first five years

we spent looking at howpeople make decisions, weestablished a dozen facts

about choices between riskyoptions.Severalofthesefactswere in flat contradiction toexpectedutility theory.Somehad been observed before, afew were new. Then weconstructed a theory thatmodified expected utilitytheory justenough toexplainour collection ofobservations. That wasprospecttheory.Our approach to the

problemwasinthespiritofa

field of psychology calledpsychophysics, which wasfounded and named by theGerman psychologist andmystic Gustav Fechner(1801–1887). Fechner wasobsessed with the relation ofmindandmatter.Ononesidethere is a physical quantitythat can vary, such as theenergy of a light, thefrequency of a tone, or anamount of money. On theothersidethereisasubjective

experience of brightness,pitch,orvalue.Mysteriously,variations of the physicalquantity cause variations inthe intensityorqualityof thesubjective experience.Fechner’sprojectwas to findthe psychophysical laws thatrelate the subjective quantityin theobserver’smind to theobjective quantity in thematerial world. He proposedthatformanydimensions,thefunction is logarithmic—

which simply means that anincrease of stimulus intensityby a given factor (say, times1.5ortimes10)alwaysyieldsthe same increment on thepsychologicalscale.Ifraisingtheenergyof thesoundfrom10 to 100 units of physicalenergy increasespsychological intensity by 4units, then a further increaseof stimulus intensity from100 to 1,000 will alsoincrease psychological

intensityby4units.

Bernoulli’sErrorAs Fechner well knew, hewasnotthefirsttolookforafunction that relBinepitze="4">utility)andtheactual amount of money. Hearguedthatagiftof10ducatshas the same utility tosomeonewhoalreadyhas100ducats as a gift of 20 ducatsto someone whose current

wealth is 200 ducats.Bernoulli was right, ofcourse:wenormallyspeakofchangesofincomeintermsofpercentages, aswhenwe say“she got a 30% raise.” Theidea is that a 30% raisemayevoke a fairly similarpsychological response forthe rich and for the poor,which an increase of $100will not do. As in Fechner’slaw, the psychologicalresponse to a change of

wealth is inverselyproportional to the initialamount ofwealth, leading totheconclusionthatutilityisalogarithmic function ofwealth. If this function isaccurate, the samepsychological distanceseparates $100,000 from $1million,and$10millionfrom$100million.Bernoulli drew on his

psychological insight into theutilityofwealth toproposea

radicallynewapproachtotheevaluation of gambles, animportant topic for themathematicians of his day.Prior to Bernoulli,mathematicians had assumedthat gambles are assessed bytheir expected value: aweighted average of thepossible outcomes, whereeachoutcome isweightedbyits probability. For example,theexpectedvalueof:

80%chancetowin$100and 20% chance to win$10 is$82 (0.8×100+0.2×10).

Now ask yourself thisquestion: Which would youprefertoreceiveasagift,thisgamble or $80 for sure?Almost everyone prefers thesure thing. If people valueduncertain prospects by theirexpected value, they wouldprefer the gamble, because

$82 is more than $80.Bernoulli pointed out thatpeopledonotinfactevaluategamblesinthisway.Bernoulli observed that

most people dislike risk (thechance of receiving thelowest possible outcome),and if they are offered achoicebetweenagambleandan amount equal to itsexpectedvaluetheywillpickthe sure thing. In fact a risk-averse decision maker will

choose a sure thing that isless than expected value, ineffect paying a premium toavoid the uncertainty. Onehundred years beforeFechner, Bernoulli inventedpsychophysics to explain thisaversiontorisk.Hisideawasstraightforward: people’schoices are based not ondollar values but on thepsychological values ofoutcomes, their utilities. Thepsychological value of a

gamble is therefore not theweighted average of itspossibledollaroutcomes;itisthe averageof theutilitiesofthese outcomes, eachweightedbyitsprobability.Table 3 shows a version of

the utility function thatBernoulli calculated; itpresents the utility ofdifferent levels of wealth,from1million to10million.You can see that adding 1million to a wealth of 1

millionyieldsanincrementof20utilitypoints,butadding1million to a wealth of 9million adds only 4 points.Bernoulli proposed that thediminishingmarginalvalueofwealth(inthemodernjargon)iswhatexplainsriskaversion—the common preferencethat people generally showfor a sure thing over afavorable gamble of equal orslightly higher expectedvalue.Considerthischoice:

Table3

The expected value of thegamble and the “sure thing”are equal in ducats (4million), but thepsychological utilities of the

two options are different,because of the diminishingutility of wealth: theincrement of utility from 1million to 4 million is 50units,butanequalincrement,from4to7million,increasesthe utility of wealth by only24 units. The utility of thegamble is 94/2 = 47 (theutility of its two outcomes,each weighted by itsprobabilityof1/2).Theutilityof4millionis60.Because60

ismorethan47,anindividualwith this utility functionwillprefer the sure thing.Bernoulli’s insightwasthatadecision maker withdiminishing marginal utilityforwealthwillberiskaverse.Bernoulli’s essay is a

marvel of concise brilliance.He applied his new conceptof expected utility (which hecalled“moralexpectation”)tocompute how much amerchant in St. Petersburg

would be willing to pay toinsure a shipment of spicefrom Amsterdam if “he iswell awareof the fact that atthis time of year of onehundred ships which sailfrom Amsterdam toPetersburg, five are usuallylost.” His utility functionexplained why poor peoplebuyinsuranceandwhyricherpeoplesellittothem.Asyoucan see in the table, the lossof1millioncausesalossof4

pointsofutility (from100 to96) to someone who has 10million and a much largerlossof18points (from48 to30)tosomeonewhostartsoffwith 3 million. The poorerman will happily pay apremium to transfer the riskto the richer one, which iswhat insurance is about.Bernoulli also offered asolution to the famous “St.Petersburg paradox,” inwhichpeoplewhoareoffered

a gamble that has infiniteexpectedvalue(inducats)arewilling to spend only a fewducats for it. Mostimpressive, his analysis ofrisk attitudes in terms ofpreferences for wealth hasstood the test of time: it isstill current in economicanalysis almost 300 yearslater.The longevity of the theory

is all the more remarkablebecauseitisseriouslyflawed.

The errors of a theory arerarelyfoundinwhatitassertsexplicitly;theyhideinwhatitignores or tacitly assumes.For an example, take thefollowingscenarios:

TodayJackandJilleachhave a wealth of 5million.Yesterday, Jack had 1million and Jill had 9million.Are theyequallyhappy?

(Do they have the sameutility?)

Bernoulli’s theory assumesthattheutilityoftheirwealthiswhatmakespeoplemoreorlesshappy.JackandJillhavethe same wealth, and thetheory therefore asserts thattheyshouldbeequallyhappy,butyoudonotneedadegreein psychology to know thattoday Jack is elated and Jilldespondent.Indeed,weknow

that Jack would be a greatdeal happier than Jill even ifhe had only 2 million todaywhile she has 5. SoBernoulli’s theory must bewrong.ThehappinessthatJackand

Jill experience is determinedby the recent change in theirwealth, relative to thedifferentstatesofwealth thatdefine their reference points(1million for Jack,9millionfor Jill). This reference

dependence is ubiquitous insensationandperception.Thesame sound will beexperienced as very loud orquite faint, depending onwhether itwasprecededbyawhisper or by a roar. Topredict the subjectiveexperience of loudness, it isnot enough to know itsabsolute energy; you alsoneed to Bineli&r quite faknow the reference sound towhich it is automatically

compared. Similarly, youneed to know about thebackground before you canpredict whether a gray patchonapagewillappeardarkorlight.Andyou need to knowthe reference before you canpredict the utility of anamountofwealth.For another example of

what Bernoulli’s theorymisses,considerAnthonyandBetty:

Anthony’s currentwealthis1million.Betty’scurrentwealth is4million.

They are both offered achoicebetweenagambleandasurething.

The gamble: equalchances to end upowning 1 million or 4millionOR

The sure thing: own 2millionforsure

In Bernoulli’s account,Anthony and Betty face thesame choice: their expectedwealth will be 2.5 million ifthey take the gamble and 2millioniftheypreferthesure-thingoption.BernoulliwouldthereforeexpectAnthonyandBetty to make the samechoice, but this prediction isincorrect. Here again, the

theory fails because it doesnot allow for the differentreference points from whichAnthony and Betty considertheir options. If you imagineyourself in Anthony’s andBetty’s shoes, you willquickly see that currentwealth matters a great deal.Hereishowtheymaythink:

Anthony (who currentlyowns 1 million): “If Ichoose the sure thing,

my wealth will doublewith certainty. This isvery attractive.Alternatively, I can takea gamble with equalchancestoquadruplemywealth or to gainnothing.”

Betty (who currentlyowns 4 million): “If Ichoose the sure thing, Ilose half of my wealth

with certainty, which isawful. Alternatively, Ican take a gamble withequal chances to losethree-quarters of mywealth or to losenothing.”

YoucansensethatAnthony

and Betty are likely tomakedifferent choices because thesure-thingoptionofowning2million makes Anthonyhappy and makes Betty

miserable.Notealsohow thesureoutcomediffersfromtheworstoutcomeofthegamble:for Anthony, it is thedifference between doublinghis wealth and gainingnothing; for Betty, it is thedifference between losinghalf her wealth and losingthree-quarters of it. Betty ismuchmore likely to takeherchances, as others do whenfaced with very bad options.As I have told their story,

neither Anthony nor Bettythinks in terms of states ofwealth: Anthony thinks ofgains and Betty thinks oflosses. The psychologicaloutcomes they assess areentirely different, althoughthe possible states of wealththeyfacearethesame.Because Bernoulli’s model

lacks the idea of a referencepoint, expected utility theorydoes not represent theobviousfactthattheoutcome

that is good for Anthony isbad for Betty. His modelcould explainAnthony’s riskaversion,butitcannotexplainBetty’s risk-seekingpreference for the gamble, abehavior that is oftenobservedinentrepreneursandin generals when all theiroptionsarebad. All this is rather obvious,isn’t it? One could easilyimagine Bernoulli himselfconstructingsimilarexamples

and developing a morecomplex theory toaccommodatethem;forsomereason,hedidnot.Onecouldalsoimaginecolleaguesofhistimedisagreeingwithhim,orlater scholars objecting astheyreadhisessay; forsomereason,theydidnoteither.The mystery is how a

conception of the utility ofoutcomesthatisvulnerabletosuch obviouscounterexamplessurvivedfor

so long. Icanexplain itonlyby a weakness of thescholarly mind that I haveoften observed in myself. Icall it theory-inducedblindness: once you haveaccepteda theoryandused itasatoolinyourthinking,itisextraordinarily difficult tonotice its flaws. If you comeuponanobservationthatdoesnotseemtofitthemodel,youassume that there must be aperfectly good explanation

that you are somehowmissing.Yougive the theorythe benefit of the doubt,trusting the community ofexpertswhohaveacceptedit.Many scholars have surelythoughtatonetimeoranotherof stories such as those ofAnthony and Betty, or Jackand Jill, and casually notedthat these storiesdidnot jibewith utility theory. But theydidnotpursuetheideatothepoint of saying, “This theory

is seriouslywrong because itignores the fact that utilitydepends on the history ofone’s wealth, not only onpresent wealth.” As thepsychologist Daniel Gilbertobserved,disbelievingishardwork, andSystem2 is easilytired.

SpeakingofBernoulli’sErrors

“He was very happy

with a $20,000 bonusthree years ago, but hissalary has gone up by20% since, so he willneed a higher bonus togetthesameutility.”

“Both candidates arewilling to accept thesalary we’re offering,but they won’t beequallysatisfiedbecausetheirreferencepointsare

different. She currentlyhas a much highersalary.”

“She’s suing him foralimony. She wouldactuallyliketosettle,buthepreferstogotocourt.That’s not surprising—she can only gain, soshe’sriskaverse.He,onthe other hand, facesoptions that are all bad,

so he’d rather take therisk.”

P

ProspectTheory

Amos and I stumbled on thecentral flaw in Bernoulli’stheory by a luckycombination of skill andignorance. At Amos’ssuggestion,Ireadachapterinhis book that describedexperiments in whichdistinguished scholars hadmeasuredtheutilityofmoney

by asking people to makechoices about gambles inwhich the participant couldwin or lose a few pennies.The experimenters weremeasuring the utility ofwealth, by modifying wealthwithin a range of less than adollar. This raised questions.Is it plausible to assume thatpeople evaluate the gamblesbytinydifferencesinwealth?Howcouldonehope to learnabout the psychophysics of

wealth by studying reactionsto gains and losses ofpennies? Recentdevelopments inpsychophysical theorysuggested that ifyouwant tostudy the subjective value ofwealth, you shouClth"ld askdirectquestionsaboutwealth,notaboutchangesofwealth.Idid not know enough aboututilitytheorytobeblindedbyrespect for it, and I waspuzzled.

When Amos and I met thenext day, I reported mydifficulties as a vaguethought,notasadiscovery. Ifully expected him to setmestraight and to explain whythe experiment that hadpuzzledmemade sense afterall, buthedidnothingof thekind—the relevance of themodern psychophysics wasimmediately obvious to him.He remembered that theeconomist Harry Markowitz,

who would later earn theNobel Prize for his work onfinance, had proposed atheoryinwhichutilitieswereattachedtochangesofwealthratherthantostatesofwealth.Markowitz’s idea had beenaround for a quarter of acenturyandhadnotattractedmuch attention, but wequickly concluded that thiswas the way to go, and thatthe theory we were planningto develop would define

outcomesasgainsandlosses,not as states of wealth.Knowledgeofperceptionandignorance about decisiontheory both contributed to alarge step forward in ourresearch.We soon knew thatwe had

overcome a serious case oftheory-induced blindness,because the idea we hadrejectednowseemednotonlyfalse but absurd. We wereamused to realize that we

were unable to assess ourcurrentwealthwithin tens ofthousandsofdollars.Theideaof deriving attitudes to smallchanges from the utility ofwealth now seemedindefensible. You know youhave made a theoreticaladvance when you can nolonger reconstruct why youfailed for so long to see theobvious.Still,ittookusyearstoexploretheimplicationsofthinking about outcomes as

gainsandlosses.In utility theory, the utility

of a gain is assessed bycomparingtheutilitiesoftwostatesofwealth.Forexample,the utility of getting an extra$500whenyourwealth is$1million is the differencebetween the utility of$1,000,500 and the utility of$1 million. And if you ownthe larger amount, thedisutility of losing $500 isagain the difference between

the utilities of the two statesof wealth. In this theory, theutilities of gains and lossesare allowed to differ only intheirsign(+or–).Thereisnoway to represent thefact thatthe disutility of losing $500could be greater than theutility of winning the sameamount—though of course itis.Asmightbeexpectedinasituation of theory-inducedblindness, possibledifferencesbetweengainsand

losses were neither expectednor studied. The distinctionbetweengainsandlosseswasassumed not to matter, sothere was no point inexaminingit.Amos and I did not see

immediatelythatourfocusonchangesofwealthopenedtheway to an exploration of anew topic. We were mainlyconcerned with differencesbetweengambleswithhighorlow probability of winning.

One day, Amos made thecasual suggestion, “Howabout losses?” and wequickly found that ourfamiliar risk aversion wasreplaced by risk seekingwhenweswitchedour focus.Considerthesetwoproblems:

Problem 1: Which doyouchoose?Get $900 for sure OR90% chance to get$1,000

Problem 2: Which doyouchoose?Lose $900 for sure OR90% chance to lose$1,000

You were probably riskaverseinproblem1,asisthegreatmajorityofpeople.Thesubjective value of a gain of$900 is certainly more than90% of the value of a gaBlth"itueofaginof$1,000.

Therisk-aversechoiceinthisproblem would not havesurprisedBernoulli.Now examine your

preference in problem 2. Ifyou are like most otherpeople,youchosethegamblein this question. Theexplanation for this risk-seeking choice is the mirrorimage of the explanation ofrisk aversion in problem 1:the(negative)valueoflosing$900ismuchmorethan90%

of the (negative) value oflosing $1,000. The sure lossis very aversive, and thisdrives you to take the risk.Later, we will see that theevaluations of theprobabilities (90% versus100%) also contributes tobothriskaversioninproblem1 and the preference for thegambleinproblem2.We were not the first to

notice that people becomerisk seeking when all their

options are bad, but theory-induced blindness hadprevailed. Because thedominant theory did notprovide a plausible way toaccommodate differentattitudestoriskforgainsandlosses, the fact that theattitudes differed had to beignored. In contrast, ourdecisiontoviewoutcomesasgains and losses led us tofocus precisely on thisdiscrepancy. The observation

ofcontrastingattitudestoriskwith favorable andunfavorable prospects soonyieldedasignificantadvance:we found a way todemonstrate the central errorin Bernoulli’s model ofchoice.Havealook:

Problem3:Inadditiontowhatever you own, youhavebeengiven$1,000.You are now asked tochoose one of these

options:50% chance to win$1,000ORget $500 forsure

Problem4:Inadditiontowhatever you own, youhavebeengiven$2,000.You are now asked tochoose one of theseoptions:50% chance to lose$1,000ORlose$500for

sureYoucaneasilyconfirmthat

in terms of final states ofwealth—all that matters forBernoulli’stheory—problems3and4are identical. Inbothcases you have a choicebetween the same twooptions: you can have thecertaintyofbeing richer thanyou currently are by $1,500,or accept a gamble in whichyouhaveequalchancestobe

richer by $1,000 or by$2,000.InBernoulli’stheory,therefore, the two problemsshould elicit similarpreferences. Check yourintuitions, and you willprobably guess what otherpeopledid.

In the first choice, alarge majority ofrespondents preferredthesurething.

In the second choice, alarge majority preferredthegamble.

The finding of different

preferencesinproblems3and4 was a decisivecounterexample to the keyidea of Bernoulli’s theory. Iftheutilityofwealthisallthatmatters, then transparentlyequivalent statements of thesame problem should yield

identical choices. Thecomparison of the problemshighlights the all-importantrole of the reference pointfrom which the options areevaluated. The referencepoint is higher than currentwealth by $1,000 in problem3, by $2,000 in problem 4.Being richer by $1,500 istherefore a gain of $500 inproblem 3 and a loss inproblem 4. Obviously, otherexamples of the same kind

are easy to generate. Thestory of Anthony and Bettyhadasimilarstructure.

How much attention didyoupay to thegiftof$1,000or $2,000 that you were“given”prior tomakingyourchoice? If you are like mostpeople,youbarelynoticed it.Indeed, there was no reasonfor you to attend to it,becausethegiftisincludedinthe reference point, andreferencepointsaregenerally

ignored. You knowsomething about yourpreferences that utilitytheorists do not—that yourattitudestoriskwouldnotbedifferent if your net worthwerehigherorlowerbyafewthousand dollars (unless youare abjectly poor). And youalso know that your attitudesto gains and losses are notderived fromyour evaluationof your wealth. The reasonyou like the idea of gaining

$100 and dislike the idea oflosing $100 is not that theseamounts changeyourwealth.You just like winning anddislike losing—and youalmostcertainlydislikelosingmorethanyoulikewinning.Thefourproblemshighlight

the weakness of Bernoulli’smodel. His theory is toosimple and lacks a movingpart. The missing variable isthereferencepoint,theearlierstate relative to which gains

and losses are evaluated. InBernoulli’s theory you needto know only the state ofwealthtodetermineitsutility,but in prospect theory youalso need to know thereference state. Prospecttheory is therefore morecomplex than utility theory.In science complexity isconsideredacost,whichmustbe justified by a sufficientlyrich set of new and(preferably) interesting

predictions of facts that theexisting theory cannotexplain. This was thechallengewehadtomeet.AlthoughAmos and Iwere

not working with the two-systems model of the mind,it’s clear now that there arethreecognitivefeaturesattheheartofprospecttheory.Theyplay an essential role in theevaluation of financialoutcomesandarecommontomany automatic processes of

perception, judgment, andemotion.TheyshouldbeseenasoperatingcharacteristicsofSystem1.

Evaluation is relative toaneutralreferencepoint,which is sometimesreferred to as an“adaptation level.” Youcan easily set up acompellingdemonstration of this

principle. Place threebowls of water in frontof you. Put ice waterinto the left-hand bowlandwarmwaterintotheright-hand bowl. Thewaterinthemiddlebowlshould be at roomtemperature. Immerseyour hands in the coldand warm water foraboutaminute, thendipbothinthemiddlebowl.You will experience the

sametemperatureasheatin one hand and cold inthe other. For financialoutcomes, the usualreference point is thestatus quo, but it canalsobetheoutcomethatyou expect, or perhapsthe outcome to whichyou feel entitled, forexample, the raise orbonus that yourcolleagues receive.Outcomes thatarebetter

thanthereferencepointsare gains. Below thereference point they arelosses.A principle ofdiminishing sensitivityapplies to both sensorydimensions and theevaluationofchangesofwealth. Turning on aweak light has a largeeffect in a dark room.The same increment oflight may be

undetectable in abrightly illuminatedroom. Similarly, thesubjective differencebetween $900 and$1,000 is much smallerthan the differencebetween$100and$200.The third principle isloss aversion. Whendirectly compared orweighted against eachother,lossesloomlargerthan gains. This

asymmetry between thepower of positive andnegative expectations orexperiences has anevolutionary history.Organisms that treatthreats as more urgentthanopportunitieshaveabetter chance to surviveandreproduce.

The three principles that

governthevalueofoutcomes

are illustrated by figure 1Blth" wagure 0. If prospecttheory had a flag, this imagewould be drawn on it. Thegraph shows thepsychological value of gainsand losses, which are the“carriers”ofvalueinprospecttheory (unlike Bernoulli’smodel, in which states ofwealth are the carriers ofvalue). The graph has twodistinctparts,totherightandto the left of a neutral

reference point. A salientfeature is that it is S-shaped,which represents diminishingsensitivity forbothgainsandlosses.Finally,thetwocurvesof theSarenot symmetrical.The slope of the functionchanges abruptly at thereference point: the responseto losses is stronger than theresponse to correspondinggains.Thisislossaversion.

Figure10

LossAversion

Manyof the optionswe faceinlifeare“mixed”:thereisarisk of loss and anopportunity for gain, and wemust decide whether toacceptthegambleorrejectit.Investors who evaluate astart-up,lawyerswhowonderwhether to file a lawsuit,

wartime generals whoconsider an offensive, andpoliticians who must decidewhether to run for office allface the possibilities ofvictory or defeat. For anelementary example of amixed prospect, examineyour reaction to the nextquestion.

Problem 5: You areoffered a gamble on thetossofacoin.

If the coin shows tails,youlose$100.If thecoin showsheads,youwin$150.Isthisgambleattractive?Wouldyouacceptit?

To make this choice, youmust balance thepsychological benefit ofgetting $150 against thepsychological cost of losing$100.Howdoyoufeelaboutit? Although the expected

value of the gamble isobviously positive, becauseyou stand to gain more thanyou can lose, you probablydislike it—most people do.The rejection of this gambleisanactofSystem2,butthecritical inputs are emotionalresponses that are generatedby System 1. For mostpeople, the fear of losing$100ismoreintensethanthehope of gaining $150. Weconcluded from many such

observations that “lossesloom larger than gains” andthatpeoplearelossaverse.Youcanmeasuretheextent

ofyour aversion to lossesbyasking yourself a question:WhatisthesmallestgainthatI need to balance an equalchance to lose $100? Formany people the answer isabout$200,twiceasmuchasthe loss. The “loss aversionratio” has been estimated inseveral experiments and is

usually in therangeof1.5 to2.5. This is an average, ofcourse; some people aremuch more loss averse thanothers. Professional risktakersinthefinancialmarketsare more tolerant of losses,probablybecausetheydonotrespond emotionally to everyfluctuation. Whenparticipants in an experimentwere instructed to“think likea trader,” they became lessloss averse and their

emotional reaction to losses(measuredbyaphysiologicalindex of emotional arousal)wassharplyreduced.In order to examine your

loss aversion ratio fordifferent stakes, consider thefollowing questions. Ignoreany social considerations, donot try to appear either boldBlth"vioher or cautious, andfocus only on the subjectiveimpact of the possible lossandtheoffsettinggain.

Consider a 5 0–5 0gambleinwhichyoucanlose $10. What is thesmallestgain thatmakesthegambleattractive? Ifyou say $10, then youare indifferent to risk. Ifyou give a number lessthan $10, you seek risk.If your answer is above$10,youarelossaverse.What about a possible

loss of $500 on a cointoss?Whatpossiblegaindoyourequiretooffsetit?What about a loss of$2,000?

As you carried out thisexercise, you probably foundthat your loss aversioncoefficient tends to increasewhen the stakes rise, but notdramatically.Allbetsareoff,

ofcourse, if thepossible lossis potentially ruinous, or ifyour lifestyle is threatened.The loss aversion coefficientis very large in such casesand may even be infinite—there are risks that you willnotaccept, regardlessofhowmany millions you mightstandtowinifyouarelucky.Another look at figure 10

may help prevent a commonconfusion. In this chapter Ihavemadetwoclaims,which

some readers may view ascontradictory:

In mixed gambles,wherebothagainandaloss are possible, lossaversion causesextremely risk-aversechoices.In bad choices, where asure loss is compared toa larger loss that ismerely probable,

diminishing sensitivitycausesriskseeking.

There is no contradiction. Inthe mixed case, the possibleloss looms twice as large asthe possible gain, as you cansee by comparing the slopesof the value function forlosses and gains. In the badcase,thebendingofthevaluecurve (diminishingsensitivity) causes risk

seeking. The pain of losing$900ismorethan90%ofthepain of losing $1,000. Thesetwo insights are the essenceofprospecttheory.

Figure 10 shows an abruptchange in the slope of thevalue function where gainsturnintolosses,becausethereis considerable loss aversionevenwhentheamountatriskis minuscule relative to your

wealth. Is it plausible thatattitudes to states of wealthcould explain the extremeaversiontosmallrisks?Itisastriking example of theory-induced blindness that thisobvious flaw in Bernoulli’stheory failed to attractscholarlynoticeformorethan250 years. In 2000, thebehavioral economistMatthewRabinfinallyprovedmathematically that attemptsto explain loss aversion by

the utility of wealth areabsurd and doomed to fail,and his proof attractedattention. Rabin’s theoremshows that anyone whorejects a favorable gamblewith small stakes ismathematically committed toa foolish level of riskaversion for some largergamble. For example, henotes that most Humansrejectthefollowinggamble:

50%chancetolose$100and 50% chance to win$200

Hethenshowsthataccordingtoutilitytheory,anindividualwho rejects that gamble willalso turn down the followinggamble:

50%chancetolose$200and 50% chance to win$20,000

Butofcoursenooneinhisor

herrightmindwillrejectthisgamble! In an exuberantarticle they wrote aboBlth"ins>Perhaps carried away by

their enthusiasm, theyconcluded their article byrecalling the famous MontyPython sketch in which afrustrated customer attemptstoreturnadeadparrottoapetstore. The customer uses along series of phrases todescribe the stateof thebird,

culminating in“this isanex-parrot.” Rabin and Thalerwentontosaythat“itistimefor economists to recognizethatexpectedutility isanex-hypothesis.” Manyeconomists saw this flippantstatement as little short ofblasphemy. However, thetheory-induced blindness ofacceptingtheutilityofwealthasanexplanationofattitudestosmalllossesisalegitimatetarget for humorous

comment.

BlindSpotspfProspectTheory

SofarinthispartofthebookIhave extolled thevirtuesofprospecttheoryandcriticizedthe rational model andexpected utility theory. It istimeforsomebalance.Most graduate students in

economics have heard aboutprospect theory and loss

aversion,butyouareunlikelyto find these terms in theindex of an introductory textin economics. I amsometimes pained by thisomission, but in fact it isquite reasonable, because ofthe central role of rationalityinbasiceconomictheory.Thestandard concepts and resultsthatundergraduatesaretaughtare most easily explained byassuming that Econs do notmake foolish mistakes. This

assumptionistrulynecessary,and it would be underminedbyintroducingtheHumansofprospect theory, whoseevaluations of outcomes areunreasonablyshort-sighted.There are good reasons for

keeping prospect theory outof introductory texts. Thebasic concepts of economicsareessentialintellectualtools,which are not easy to graspeven with simplified andunrealistic assumptions about

the nature of the economicagents who interact inmarkets. Raising questionsabouttheseassumptionsevenas they are introducedwouldbe confusing, and perhapsdemoralizing.It isreasonableto put priority on helpingstudents acquire the basictools of the discipline.Furthermore, the failure ofrationality that is built intoprospect theory is oftenirrelevant to the predictions

of economic theory, whichworkoutwithgreatprecisionin some situations andprovide good approximationsin many others. In somecontexts, however, thedifference becomessignificant: the Humansdescribed by prospect theoryare guided by the immediateemotional impact of gainsand losses, not by long-termprospects of wealth andglobalutility.

I emphasized theory-induced blindness in mydiscussion of flaws inBernoulli’s model thatremained unquestioned formore than two centuries.Butof course theory-inducedblindness is not restricted toexpected utility theory.Prospect theory has flaws ofits own, and theory-inducedblindness to these flaws hascontributed to its acceptanceas the main alternative to

utilitytheory.Consider the assumption of

prospect theory, that thereference point, usually thestatus quo, has a value ofzero. This assumption seemsreasonable, but it leads tosome absurd consequences.Have a good look at thefollowing prospects. Whatwoulditbeliketoownthem?

A.onechanceinamilliontowin$1million

B.90%chancetowin$12and 10% chance to winnothing

C.90%chance towin$1million and 10% chance towinnothingWinningnothingisapossibleoutcomeinallthreegambles,and prospect theory assignsthe same value to thatoutcome in the three cases.Winning nothing is thereference point and its value

is zero. Do these statementscorrespond to yourexperience? Of course not.Winning nothing is anonevent in the first twocases,andassigningitavalueofzeromakesgoodsense.Incontrast, failing towin in thethird scenario is intenselydisappointing. Like a salaryincrease that has beenpromisedinformally,thehighprobability of winning thelarge sum sets up a tentative

newreferencepoint.Relativetoyourexpectations,winningnothing will be experiencedas a large loss. Prospecttheory cannot cope with thisfact,becauseitdoesnotallowthe value of an outcome (inthiscase,winningnothing)tochange when it is highlyunlikely, or when thealternative is very valuable.In simple words, prospecttheory cannot deal withdisappointment.

Disappointment and theanticipation ofdisappointment are real,however, and the failure toacknowledge them is asobvious a flow as thecounterexamples that Iinvoked to criticizeBernoulli’stheory.Prospect theory and utility

theory also fail to allow forregret.Thetwotheoriessharethe assumption that availableoptions in a choice are

evaluated separately andindependently, and that theoptionwith thehighestvalueis selected. This assumptionis certainly wrong, as thefollowingexampleshows.

Problem 6: Choosebetween 90% chance towin $1 million OR $50withcertainty.

Problem 7: Choosebetween 90% chance to

win $1 million OR$150,000withcertainty.

Compare theanticipatedpainof choosing the gamble andnotwinninginthetwocases.Failing to win is adisappointment in both, butthe potential pain iscompoundedinproblem7byknowing that if you choosethegambleand loseyouwillregret the “greedy” decisionyoumadebyspurninga sure

gift of $150,000. In regret,theexperienceofanoutcomedepends on an option youcould have adopted but didnot.Several economists and

psychologists have proposedmodels of decision makingthat are based on theemotions of regret anddisappointment. It is fair tosay that these models havehad less influence thanprospect theory, and the

reason is instructive. Theemotions of regret anddisappointment are real, anddecision makers surelyanticipate these emotionswhen making their choices.The problem is that regrettheories make few strikingpredictions that woulddistinguish them fromprospect theory, which hasthe advantage of beingsimpler. The complexity ofprospect theory was more

acceptable in the competitionwith expected utility theorybecause it did predictobservations that expectedutility theory could notexplain.Richer and more realistic

assumptionsdonotsufficetomake a theory successful.Scientists use theories as abag of working tools, andthey will not take on theburden of a heavier bagunless thenewtoolsarevery

useful. Prospect theory wasaccepted by many scholarsnot because it is “true” butbecause the concepts that itadded to utility theory,notably the reference pointandlossaversion,wereworththe trouble; theyyieldednewpredictions that turned out tobetrue.Wewerelucky.

SpeakingofProspectTheory

“Hesuffersfromextremelossaversion, which makes himturn down very favorableopportunities.”

“Consideringhervastwealth,her emotional response totrivialgainsandlossesmakesnosense.”

“He weighs losses abouttwice as much as gains,

whichisnormal.”

P

TheEndowmentEffect

You have probably seenfigure11oraclosecousinofit even if you never had aclassineconomics.Thegraphdisplays an individual’s“indifference map” for twogoods.

Figure11

Students learn in

introductory economicsclassesthateachpointonthemap specifies a particularcombination of income andvacation days. Each“indifferencecurve”connectsthe combinations of the twogoods that are equallydesirable—they have thesame utility. The curves

would turn into parallelstraight lines if people werewilling to “sell” vacationdays for extra income at thesameprice regardlessofhowmuch income and howmuchvacation time theyhave.Theconvex shape indicatesdiminishing marginal utility:themoreleisureyouhave,thelessyoucareforanextradayof it, and each added day isworth less than the onebefore. Similarly, the more

incomeyouhave,thelessyoucare for an extra dollar, andtheamountyouarewillingtogive up for an extra day ofleisureincreases.All locations on an

indifferencecurveareequallyattractive. This is literallywhatindifferencemeans:youdon’t care where you are onanindifferencecurve.SoifAand B are on the sameindifference curve for you,you are indifferent between

them and will need noincentivetomovefromonetothe other, or back. Someversion of this figure hasappeared in every economicstextbook written in the lasthundred years, and manymillions of students havestaredatit.Fewhavenoticedwhat is missing. Here again,the power and elegance of atheoretical model haveblinded students and scholarstoaseriousdeficiency.

What is missing from thefigure is an indication of theindividual’s current incomeand leisure. If you are asalaried employee, the termsofyouremploymentspecifyasalary and a number ofvacation days, which is apointonthemap.Thisisyourreference point, your statusquo, but the figure does notshowit.Byfailingtodisplayit,thetheoristswhodrawthisfigure invite you to believe

that the reference point doesnot matter, but by now youknow that of course it does.This is Bernoulli’s error allover again. Therepresentationof indifferencecurvesimplicitlyassumesthatyour utility at any givenmoment is determinedentirely by your presentsituation, that the past isirrelevant, and that yourevaluation of a possible jobdoesnotdependontheterms

of your current job. Theseassumptions are completelyunrealistic in thiscaseand inmanyothers.TheomissionoftherefCon

serence point from theindifference map is asurprising case of theory-induced blindness, becausewe so often encounter casesin which the reference pointobviously matters. In labornegotiations, it is wellunderstoodbybothsidesthat

the reference point is theexisting contract and that thenegotiations will focus onmutual demands forconcessions relative to thatreference point. The role oflossaversion inbargaining isalsowellunderstood:makingconcessions hurts. You havemuch personal experience ofthe roleof referencepoint. Ifyou changed jobs orlocations, or even consideredsuch a change, you surely

rememberthatthefeaturesofthe newplacewere coded aspluses or minuses relative towhere you were. You mayalso have noticed thatdisadvantages loomed largerthan advantages in thisevaluation—loss aversionwasatwork. It isdifficult toacceptchangesfortheworse.For example, the minimalwage that unemployedworkers would accept fornew employment averages

90% of their previous wage,anditdropsbylessthan10%overaperiodofoneyear.Toappreciatethepowerthat

the reference point exerts onchoices, consider Albert andBen, “hedonic twins” whohave identical tastes andcurrently hold identicalstarting jobs, with littleincomeandlittleleisuretime.Their current circumstancescorrespond to the pointmarked 1 in figure 11. The

firm offers them twoimprovedpositions,AandB,andletsthemdecidewhowillget a raise of $10,000(positionA)andwhowillgetanextradayofpaidvacationeach month (position B). Astheyarebothindifferent,theytoss a coin. Albert gets theraise, Ben gets the extraleisure. Some time passes asthe twins get accustomed totheir positions. Now thecompany suggests they may

switchjobsiftheywish.The standard theory

represented in the figureassumes that preferences arestable over time. PositionsAand B are equally attractivefor both twins and they willneed little or no incentive toswitch. In sharp contrast,prospect theory asserts thatboth twins will definitelyprefer to remain as they are.Thispreferencefor thestatusquo is a consequence of loss

aversion.Let us focus on Albert. He

was initially in position 1 onthe graph, and from thatreference point he foundthesetwoalternativesequallyattractive:

Go to A: a raise of$10,000ORGotoB:12extradaysofvacation

Taking position A changesAlbert’s reference point, andwhen he considers switchingto B, his choice has a newstructure:

StayatA:nogainandnolossORMovetoB:12extradaysof vacation and a$10,000salarycut

You just had the subjective

experience of loss aversion.Youcouldfeelit:asalarycutof$10,000 isverybadnews.Evenifagainof12vacationdays was as impressive as again of $10,000, the sameimprovementofleisureisnotsufficienttocompensateforaloss of $10,000. Albert willstay at A because thedisadvantage of movingoutweighstheadvantage.Thesame reasoning applies toBen, who will also want to

keep his present job becausethe loss of now-preciousleisure outweighs the benefitoftheextraincome.Thisexamplehighlightstwo

aspects of choice that the stBonsAestBonandardmodelof indifference curves doesnot predict. First, tastes arenot fixed; they varywith thereference point. Second, thedisadvantages of a changeloom larger than itsadvantages, inducing a bias

that favors the statusquo.Ofcourse,lossaversiondoesnotimplythatyouneverprefertochange your situation; thebenefits of an opportunitymay exceed evenoverweighted losses. Lossaversion implies only thatchoicesarestronglybiasedinfavor of the referencesituation (and generallybiased to favor small ratherthanlargechanges).Conventional indifference

maps and Bernoulli’srepresentationofoutcomesasstates of wealth share amistaken assumption: thatyour utility for a state ofaffairs depends only on thatstate and is not affected byyour history. Correcting thatmistake has been one of theachievements of behavioraleconomics.

TheEndowment

EffectThe question of when anapproach or amovement gotits start is often difficult toanswer,buttheoriginofwhatis now known as behavioraleconomics can be specifiedprecisely. In the early 1970s,Richard Thaler, then agraduate student in the veryconservative economicsdepartment of the Universityof Rochester, began having

heretical thoughts. Thaleralwayshadasharpwitandanironic bent, and as a studenthe amused himself bycollecting observations ofbehavior that the model ofrational economic behaviorcould not explain. He tookspecial pleasure in evidenceof economic irrationalityamonghisprofessors, andhefound one that wasparticularlystriking.Professor R (now revealed

to be Richard Rosett, whowent on to become the deanof the University of ChicagoGraduateSchoolofBusiness)was a firm believer instandard economic theory aswell as a sophisticated winelover. Thaler observed thatProfessor R was veryreluctant tosellabottle fromhis collection—even at thehigh price of $100 (in 1975dollars!). Professor R boughtwine at auctions, but would

neverpaymorethan$35forabottle of that quality. Atpricesbetween$35and$100,hewouldneitherbuynorsell.The large gap is inconsistentwith economic theory, inwhich the professor isexpected to have a singlevalue for the bottle. If aparticularbottle isworth$50to him, then he should bewilling to sell it for anyamountinexcessof$50.Ifhedid not own the bottle, he

should bewilling to pay anyamount up to $50 for it. Thejust-acceptable selling priceand the just-acceptablebuying price should havebeenidentical,but infact theminimumpricetosell($100)was much higher than themaximum buying price of$35. Owning the goodappearedtoincreaseitsvalue.RichardThaler foundmany

examples of what he calledthe endowment effect,

especially for goods that arenotregularlytraded.Youcaneasily imagine yourself in asimilar situation. Supposeyouholdatickettoasold-outconcert by a popular band,which you bought at theregular price of $200. Youare an avid fan and wouldhave been willing to pay upto $500 for the ticket. Nowyouhaveyourticketandyoulearn on the Internet thatricherormoredesperate fans

are offering $3,000. Wouldyou sell? If you resemblemostof theaudienceat sold-out events you do not sell.Your lowest selling price isabove $3,000 and yourmaximum buying price is$500. This is an example ofan endowment effect, and abelieverinstandardeconomictheory would be puzzled byit. Thalerwas looking for anaccount that could explainpuzzlesofthiskind.

Chance intervened whenThalermetoneofourformerstudents at a conference andobtained an early draft ofprospect theory. He reportsthat he read the manuscriptwithconsiderableBonsAbleBonexcitement, because hequicklyrealizedthattheloss-averse value function ofprospecttheorycouldexplainthe endowment effect andsome other puzzles in hiscollection. The solution was

to abandon the standard ideathatProfessorRhadauniqueutility for the state ofhavinga particular bottle. Prospecttheory suggested that thewillingness tobuyor sell thebottle depends on thereference point—whether ornot the professor owns thebottle now. If he owns it, heconsiders the pain of givingup the bottle. If he does notown it, he considers thepleasureofgetting thebottle.

The values were unequalbecause of loss aversion:giving up a bottle of nicewine is more painful thangettinganequallygoodbottleispleasurable.Rememberthegraph of losses and gains inthe previous chapter. Theslope of the function issteeper in the negativedomain;theresponsetoalossis stronger than the responsetoacorrespondinggain.Thiswas the explanation of the

endowmenteffect thatThalerhad been searching for. Andthe first application ofprospect theory to aneconomicpuzzlenowappearsto have been a significantmilestone in thedevelopmentofbehavioraleconomics.Thaler arranged to spend a

year at Stanford when heknewthatAmosandIwouldbe there. During thisproductiveperiod,welearnedmuch from each other and

became friends. Seven yearslater, he and I had anotheropportunity to spend a yeartogether and to continue theconversation betweenpsychology and economics.TheRussellSageFoundation,whichwasforalongtimethemain sponsor of behavioraleconomics, gave one of itsfirst grants to Thaler for thepurpose of spending a yearwith me in Vancouver.During that year,weworked

closely with a localeconomist, Jack Knetsch,withwhomwesharedintenseinterest in the endowmenteffect, the rules of economicfairness, and spicy Chinesefood.The starting point for our

investigation was that theendowment effect is notuniversal. If someone asksyou to change a $5 bill forfive singles, you hand overthe five ones without any

sense of loss. Nor is theremuchlossaversionwhenyoushopforshoes.Themerchantwho gives up the shoes inexchangeformoneycertainlyfeels no loss. Indeed, theshoesthathehandsoverhavealways been, from his pointofview,acumbersomeproxyformoneythathewashopingto collect from someconsumer. Furthermore, youprobably do not experiencepayingthemerchantasaloss,

because youwere effectivelyholdingmoneyasaproxyforthe shoes you intended tobuy. These cases of routinetrading are not essentiallydifferent from the exchangeof a $5 bill for five singles.There is no loss aversion oneither side of routinecommercialexchanges.What distinguishes these

market transactions fromProfessor R’s reluctance tosell his wine, or the

reluctance of Super Bowlticketholderstosellevenatavery high price? Thedistinctivefeatureisthatboththe shoes the merchant sellsyouandthemoneyyouspendfrom your budget for shoesareheld“forexchange.”Theyare intended to be traded forother goods. Other goods,suchaswineandSuperBowltickets, areheld “foruse,” tobe consumed or otherwiseenjoyed. Your leisure time

andthestandardoflivingthatyourincomesupportsarealsonot intended for sale orexchange.Knetsch, Thaler, and I set

out to design an experimentthat would highlight thecontrast between goods thatare held for use and forexchange. We borrowed oneaspect of the design of ourexperiment from VernonSmith, the founder ofexperimentaleconomics,with

whomIwouldshareaNobelPrizemanyyearslater.Inthismethod, a limited number oftokens are distributed to theparticipants in a “market.”Any participants who own atokenattheendBonsAendBon of the experiment canredeem it for cash. Theredemption values differ fordifferent individuals, torepresent the fact that thegoods traded in markets aremorevaluabletosomepeople

than to others. The sametoken may be worth $10 toyou and $20 to me, and anexchange at any pricebetween these values will beadvantageoustobothofus.Smith created vivid

demonstrations of how wellthe basic mechanisms ofsupply and demand work.Individuals would makesuccessive public offers tobuyorsellatoken,andotherswouldrespondpubliclytothe

offer.Everyonewatchestheseexchanges and sees the priceat which the tokens changehands. The results are asregular as those of ademonstration in physics.Asinevitably as water flowsdownhill, those who own atoken that isof littlevalue tothem (because theirredemption values are low)endupsellingtheirtokenataprofittosomeonewhovaluesit more. When trading ends,

thetokensareinthehandsofthose who can get the mostmoney for them from theexperimenter. The magic ofthe markets has worked!Furthermore, economictheorycorrectlypredictsboththe final price at which themarket will settle and thenumber of tokens that willchange hands. If half theparticipants in the marketwere randomly assignedtokens, the theory predicts

that half of the tokens willchangehands.We used a variation on

Smith’s method for ourexperiment. Each sessionbeganwith several rounds oftrades for tokens, whichperfectly replicated Smith’sfinding. The estimatednumber of trades wastypically very close oridentical to the amountpredicted by the standardtheory.Thetokens,ofcourse,

had value only because theycould be exchanged for theexperimenter’scash;theyhadno value for use. Then weconducted a similar marketforanobjectthatweexpectedpeople to value for use: anattractive coffee mug,decoratedwith the universityinsigniaofwhereverwewereconducting the experiments.The mug was then worthabout$6(andwouldbeworthabout double that amount

today).Mugsweredistributedrandomly to half theparticipants. The Sellers hadtheir mug in front of them,and the Buyers were invitedto look at their neighbor’smug;allindicatedthepriceatwhich theywould trade. TheBuyers had to use their ownmoneytoacquireamug.Theresults were dramatic: theaverage selling price wasabout double the averagebuying price, and the

estimated number of tradeswas less than half of thenumberpredictedbystandardtheory. The magic of themarket did not work for agood that the ownersexpectedtouse.We conducted a series of

experimentsusingvariantsofthe same procedure, alwayswith the same results. Myfavorite is one in which weadded to the Sellers andBuyers a third group—

Choosers.Unlike theBuyers,who had to spend their ownmoney to acquire the good,the Choosers could receiveeither a mug or a sum ofmoney,andtheyindicatedtheamountofmoneythatwasasdesirable as receiving thegood.Theseweretheresults:Sellers $7.12Choosers $3.12Buyers $2.87

The gap between Sellers andChoosers is remarkable,becausetheyactuallyfacethesame choice! If you are aSelleryoucangohomewitheither a m Bon s A a mBonug ormoney, and if youare a Chooser you haveexactlythesametwooptions.The long-term effects of thedecision are identical for thetwo groups. The onlydifferenceisintheemotionof

the moment. The high pricethat Sellers set reflects thereluctance to give up anobject that they alreadyown,a reluctance that can be seenin babies who hold onfiercely to a toy and showgreat agitation when it istaken away. Loss aversion isbuilt into the automaticevaluationsofSystem1.Buyers and Choosers set

similar cash values, althoughtheBuyershavetopayforthe

mug, which is free for theChoosers. This is what wewouldexpectifBuyersdonotexperience spending moneyon the mug as a loss.Evidence frombrain imagingconfirms the difference.Sellinggoods thatonewouldnormally use activatesregions of the brain that areassociated with disgust andpain. Buying also activatestheseareas,butonlywhentheprices are perceived as too

high—when you feel that aseller is taking money thatexceeds the exchange value.Brainrecordingsalsoindicatethat buying at especially lowpricesisapleasurableevent.The cash value that the

Sellerssetonthemugisabitmore than twice as high asthevaluesetbyChoosersandBuyers. The ratio is veryclose to the loss aversioncoefficientinriskychoice,aswemight expect if the same

value function for gains andlossesofmoney isapplied toboth riskless and riskydecisions. A ratio of about2:1hasappearedinstudiesofdiverse economic domains,including the response ofhouseholds to price changes.Aseconomistswouldpredict,customers tend to increasetheir purchases of eggs,orange juice, or fish whenprices drop and to reducetheir purchases when prices

rise; however, in contrast tothe predictions of economictheory, the effect of priceincreases (losses relative tothe reference price) is abouttwiceaslargeastheeffectofgains.The mugs experiment has

remained the standarddemonstration of theendowmenteffect,alongwithan even simpler experimentthat JackKnetsch reportedataboutthesametime.Knetsch

askedtwoclassestofilloutaquestionnaire and rewardedthemwithagiftthatremainedin front of them for thedurationoftheexperiment.Inonesession, theprizewasanexpensive pen; in another, abarofSwisschocolate.Attheend of the class, theexperimenter showed thealternative gift and allowedeveryone to trade his or hergift for another. Only about10%oftheparticipantsopted

to exchange their gift. Mostofthosewhohadreceivedthepen stayedwith the pen, andthose who had received thechocolate did not budgeeither.

ThinkingLikeaTrader

The fundamental ideas ofprospect theory are thatreference points exist, andthat losses loom larger than

corresponding gains.Observations in real marketscollected over the yearsillustrate the power of theseconcepts. A study of themarket for condo apartmentsinBoston during a downturnyielded particularly clearresults. The authors of thatstudy compared the behaviorof owners of similar unitswho had bought theirdwellings at different prices.For a rational agent, the

buying price is irrelevanthistory—the current marketvalue is all thatmatters. Notso for Humans in a downmarket for housing. Ownerswho have a high referencepoint and thus face higherlosses set a higher price ontheirdwelling,spendalongertimetryingtoselltheirhome,and eventually receive moremoney.The original demonstration

of an asymmetry between

selling prices and buyingprices(or,moreconvincingly,between selling andchoosing)wasveryimportantintheinitialacceptanceoftheideas of reference point andloss aversi Bon s AersiBonon. However, it is wellunderstood that referencepointsarelabile,especiallyinunusual laboratory situations,and that the endowmenteffect can be eliminated bychangingthereferencepoint.

No endowment effect isexpected when owners viewtheir goods as carriers ofvalue for future exchanges, awidespreadattitudeinroutinecommerce and in financialmarkets. The experimentaleconomistJohnList,whohasstudied trading at baseballcard conventions, found thatnovice traders were reluctantto part with the cards theyowned, but that thisreluctance eventually

disappeared with tradingexperience. Moresurprisingly, List found alarge effect of tradingexperienceontheendowmenteffectfornewgoods.At a convention, List

displayedanoticethatinvitedpeople to takepart in a shortsurvey,forwhichtheywouldbe compensatedwith a smallgift: a coffee mug or achocolate bar of equal value.The gift s were assigned at

random. As the volunteerswereabouttoleave,Listsaidto each of them, “We gaveyouamug[orchocolatebar],but you can trade for achocolate bar [or mug]instead, if you wish.” In anexact replication of JackKnetsch’s earlier experiment,List found that only 18% ofthe inexperienced traderswere willing to exchangetheir gift for the other. Insharp contrast, experienced

tradersshowednotraceofanendowment effect: 48% ofthem traded! At least in amarketenvironment inwhichtrading was the norm, theyshowed no reluctance totrade.Jack Knetsch also

conducted experiments inwhich subtle manipulationsmade the endowment effectdisappear. Participantsdisplayed an endowmenteffect only if they had

physical possession of thegood for a while before thepossibility of trading it wasmentioned.Economistsofthestandard persuasionmight betempted to say that Knetschhadspenttoomuchtimewithpsychologists, because hisexperimental manipulationshowed concern for thevariables that socialpsychologists expect to beimportant. Indeed, thedifferent methodological

concerns of experimentaleconomistsandpsychologistshave been much in evidencein the ongoing debate abouttheendowmenteffect.Veteran traders have

apparently learned to ask thecorrect question, which is“HowmuchdoIwanttohavethat mug, compared withother things I could haveinstead?”ThisisthequestionthatEcons ask, andwith thisquestion there is no

endowment effect, becausethe asymmetry between thepleasure of getting and thepain of giving up isirrelevant.Recent studies of the

psychology of “decisionmaking under poverty”suggest that the poor areanothergroupinwhichwedonot expect to find theendowment effect. Beingpoor, in prospect theory, isliving below one’s reference

point. There are goods thatthe poor need and cannotafford,sotheyarealways“inthelosses.”Smallamountsofmoney that they receive aretherefore perceived as areduced loss, not as a gain.Themoneyhelpsoneclimbalittle toward the referencepoint, but the poor alwaysremain on the steep limb ofthevaluefunction.People who are poor think

liketraders,butthedynamics

are quite different. Unliketraders, the poor are notindifferent to the differencesbetween gaining and givingup. Their problem is that alltheir choices are betweenlosses.Moneythatisspentonone good is the loss ofanothergood that couldhavebeen purchased instead. Forthepoor,costsarelosses.We all know people for

whom spending is painful,although they are objectively

quite well-off. There mayalsobeculturaldifferencesinthe attitude toward money,and especially toward thespendingofmoneyonwhimsBon sAhimsBonandminorluxuries,suchasthepurchaseof a decorated mug. Such adifference may explain thelargediscrepancybetweentheresultsofthe“mugsstudy”inthe United States and in theUK. Buying and sellingpricesdivergesubstantiallyin

experiments conducted insamples of students of theUnited States, but thedifferences are much smalleramong English students.Much remains to be learnedabouttheendowmenteffect.

SpeakingOfTheEndowmentEffect

“She didn’t care whichof the two offices shewould get, but a day

after the announcementwas made, she was nolonger willing to trade.Endowmenteffect!”

“These negotiations aregoing nowhere becauseboth sides find itdifficult to makeconcessions, even whenthey can get somethingin return. Losses loomlargerthangains.”

“When they raised theirprices, demand driedup.”

“Hejusthatestheideaofsellinghishouseforlessmoney than he paid forit. Loss aversion is atwork.”

“Heisamiser,andtreatsanydollarhespendsasa

loss.”

P

BadEvents

The concept of loss aversionis certainly the mostsignificant contribution ofpsychology to behavioraleconomics. This is odd,because the idea that peopleevaluate many outcomes asgains and losses, and thatlossesloomlargerthangains,surprisesnoone.AmosandI

often joked that we wereengagedinstudyingasubjectabout which ourgrandmothers knew a greatdeal. In fact, however, weknow more than ourgrandmothers did and cannow embed loss aversion inthecontextofabroader two-systems model of the mind,and specifically a biologicaland psychological view inwhich negativity and escapedominate positivity and

approach. We can also tracethe consequences of lossaversion in surprisinglydiverse observations: onlyout-of-pocket losses arecompensatedwhengoodsarelost in transport; attempts atlarge-scalereformsveryoftenfail; and professional golfersputt more accurately for parthan for a birdie. Clever asshe was, my grandmotherwouldhavebeensurprisedbythe specific predictions from

ageneral ideasheconsideredobvious.

NegativityDominance

Figure12

Your heartbeat acceleratedwhen you looked at the left-

hand figure. It acceleratedeven before you could labelwhat is so eerie about thatpicture.After some time youmayhaverecognizedtheeyesofaterrifiedperson.Theeyesontheright,narrowedbytheCrroraisedcheeksofasmile,express happiness—and theyare not nearly as exciting.The two pictures werepresentedtopeoplelyinginabrain scanner. Each picture

wasshownforlessthan2/100of a second and immediatelymasked by “visual noise,” arandom display of dark andbright squares. None of theobservers ever consciouslyknew that he had seenpicturesofeyes,butonepartoftheirbrainevidentlyknew:the amygdala, which has aprimary role as the “threatcenter”ofthebrain,althoughit is also activated in other

emotional states. Images ofthe brain showed an intenseresponseoftheamygdalatoathreatening picture that theviewerdidnotrecognize.Theinformation about the threatprobably traveled via asuperfast neural channel thatfeeds directly into a part ofthe brain that processesemotions, bypassing thevisualcortexthatsupportstheconscious experience of“seeing.” The same circuit

also causes schematic angryfaces(apotentialthreat)tobeprocessed faster and moreefficiently than schematichappy faces. Someexperimenters have reportedthatanangryface“popsout”of a crowd of happy faces,but a single happy face doesnot stand out in an angrycrowd.Thebrainsofhumansand other animals contain amechanismthatisdesignedtogiveprioritytobadnews.By

shavingafewhundredthsofasecond from the time neededto detect a predator, thiscircuit improves the animal’soddsoflivinglongenoughtoreproduce. The automaticoperationsofSystem1reflectthis evolutionary history. Nocomparably rapidmechanismfor recognizing good newshasbeendetected.Ofcourse,we and our animal cousinsarequicklyalertedtosignsofopportunities to mate or to

feed, and advertisers designbillboards accordingly. Still,threats are privileged aboveopportunities, as they shouldbe.The brain responds quickly

even to purely symbolicthreats. Emotionally loadedwords quickly attractattention, and bad words(war, crime) attract attentionfaster than do happy words(peace,love).Thereisnorealthreat,but themere reminder

of a bad event is treated inSystem 1 as threatening. Aswesawearlierwith thewordvomit, the symbolicrepresentation associativelyevokes in attenuated formmany of the reactions to thereal thing, includingphysiological indices ofemotion and even fractionaltendencies to avoid orapproach, recoil or leanforward. The sensitivity tothreats extends to the

processing of statements ofopinions with which westrongly disagree. Forexample, depending on yourattitude to euthanasia, itwould take your brain lessthan one-quarter of a secondto register the “threat” in asentence that starts with “Ithink euthanasia is anacceptable/unacceptable…”The psychologist Paul

Rozin, an expert on disgust,observed that a single

cockroach will completelywrecktheappealofabowlofcherries, but a cherrywill donothing at all for a bowl ofcockroaches. As he pointsout, the negative trumps thepositive in many ways, andloss aversion is one ofmanymanifestations of a broadnegativity dominance. Otherscholars, in a paper titled“Bad Is Stronger ThanGood,” summarized theevidence as follows: “Bad

emotions, bad parents, andbad feedback have moreimpact than good ones, andbad information is processedmore thoroughly than good.Theselfismoremotivatedtoavoid bad self-definitionsthan to pursue good ones.Bad impressions and badstereotypes are quicker toform and more resistant todisconfirmation than goodones.” They cite JohnGottman, the well-known

expert in marital relations,who observed that the long-termsuccessofarelationshipdependsfarmoreonavoidingthe negative than on seekingthe positive. Gottmanestimated that a stablerelationship requires BrroQres Brrthat goodinteractions outnumber badinteractionsbyatleast5to1.Other asymmetries in thesocial domain are evenmorestriking. We all know that a

friendshipthatmaytakeyearstodevelopcanberuinedbyasingleaction.Some distinctions between

good and bad are hardwiredintoourbiology.Infantsentertheworldreadytorespondtopain as bad and to sweet (upto a point) as good. Inmanysituations, however, theboundary between good andbad is a reference point thatchanges over time anddepends on the immediate

circumstances. Imagine thatyouareout in thecountryona cold night, inadequatelydressedforthetorrentialrain,your clothes soaked. Astingingcoldwindcompletesyour misery. As you wanderaround,youfindalargerockthat provides some shelterfromthefuryoftheelements.ThebiologistMichelCabanacwould call the experience ofthat moment intenselypleasurable because it

functions, as pleasurenormallydoes,toindicatethedirection of a biologicallysignificant improvement ofcircumstances. The pleasantreliefwill not last very long,of course, and youwill soonbe shivering behind the rockagain, driven by yourrenewed suffering to seekbettershelter.

GoalsareReference

PointsLoss aversion refers to therelative strength of twomotives: we are drivenmorestrongly to avoid losses thantoachievegains.A referencepoint is sometimes the statusquo,but itcanalsobeagoalin the future:notachievingagoal is a loss, exceeding thegoal is a gain. As we mightexpect from negativitydominance, the two motives

arenotequallypowerful.Theaversion to the failure of notreaching the goal is muchstronger than the desire toexceedit.People often adopt short-

termgoals that they strive toachievebutnotnecessarilytoexceed. They are likely toreducetheireffortswhentheyhave reached an immediategoal, with results thatsometimes violate economiclogic. New York cabdrivers,

for example, may have atarget income for the monthor the year, but the goal thatcontrols their effort istypically a daily target ofearnings.Ofcourse,thedailygoalismucheasiertoachieve(and exceed) on some daysthanonothers.Onrainydays,a New York cab neverremainsfreeforlong,andthedriver quickly achieves histarget; not so in pleasantweather, when cabs often

wastetimecruisingthestreetslooking for fares. Economiclogic implies that cabdriversshould work many hours onrainy days and treatthemselvestosomeleisureonmild days, when they can“buy”leisureatalowerprice.The logic of loss aversionsuggests theopposite:driverswhohaveafixeddailytargetwill work many more hourswhen the pickings are slimandgohomeearlywhenrain-

drenched customers arebegging to be takensomewhere.TheeconomistsDevinPope

and Maurice Schweitzer, atthe University ofPennsylvania, reasoned thatgolf provides a perfectexampleofareferencepoint:par. Every hole on the golfcourse has a number ofstrokesassociatedwithit;thepar number provides thebaseline for good—but not

outstanding—performance.For a professional golfer, abirdie (one stroke under par)is a gain, and a bogey (onestrokeoverpar)isaloss.Theeconomists compared twosituationsaplayermightfacewhennearthehole:

putttoavoidabogeyputttoachieveabirdie

Every stroke counts in golf,andinprofessionalgolfeverystrokecountsalot.Accordingto prospect theory, however,somestrokescountmorethanothers.Failingtomakeparisa los Brro Q los Brrs, butmissing a birdie putt is aforegone gain, not a loss.Pope and Schweitzerreasoned from loss aversionthatplayerswould trya littleharder when putting for par(toavoidabogey)thanwhen

putting for a birdie. Theyanalyzed more than 2.5million putts in exquisitedetailtotestthatprediction.They were right. Whether

the puttwas easy or hard, ateverydistance from thehole,the players were moresuccessful when putting forpar than for a birdie. Thedifference in their rate ofsuccess when going for par(to avoid a bogey) or for abirdie was 3.6%. This

differenceisnottrivial.TigerWoods was one of the“participants” in their study.If in his best years TigerWoods had managed to puttas well for birdies as he didfor par, his averagetournamentscorewouldhaveimproved by one stroke andhis earnings by almost $1million per season. Thesefierce competitors certainlydo not make a consciousdecisiontoslackoffonbirdie

putts, but their intenseaversion to a bogeyapparently contributes toextra concentration on thetaskathand.Thestudyofputtsillustrates

the power of a theoreticalconceptasanaidtothinking.Who would have thought itworthwhile to spend monthsanalyzing putts for par andbirdie? The idea of lossaversion, which surprises noone except perhaps some

economists, generated aprecise and nonintuitivehypothesis and ledresearchers to a finding thatsurprised everyone—including professionalgolfers.

DefendingtheStatusQuo

If you are set to look for it,the asymmetric intensity ofthe motives to avoid losses

and to achieve gains showsupalmosteverywhere.Itisanever-present feature ofnegotiations, especially ofrenegotiations of an existingcontract, the typical situationin labor negotiations and ininternational discussions oftradeorarmslimitations.Theexisting terms definereference points, and aproposed change in anyaspect of the agreement isinevitably viewed as a

concession that one sidemakes to the other. Lossaversion creates anasymmetry that makesagreementsdifficult to reach.Theconcessionsyoumaketomearemygains,buttheyareyour losses; they cause youmuch more pain than theygiveme pleasure. Inevitably,youwillplaceahighervalueon them than Ido.Thesameis true,ofcourse,of theverypainful concessions you

demand fromme,which youdo not appear to valuesufficiently! Negotiationsover a shrinking pie areespecially difficult, becausethey require an allocation oflosses. People tend to bemuch more easygoing whenthey bargain over anexpandingpie.Many of the messages that

negotiators exchange in thecourse of bargaining areattempts to communicate a

reference point and providean anchor to the other side.Themessagesarenotalwayssincere. Negotiators oftenpretend intenseattachment tosome good (perhaps missilesof a particular type inbargaining over armsreductions), although theyactually view that good as abargaining chip and intendultimately to give it away inan exchange. Becausenegotiators are influenced by

a norm of reciprocity, aconcession that is presentedaspainfulcallsforanequallypainful (and perhaps equallyinauthentic) concession fromtheotherside.Animals, including people,

fightharder toprevent lossesthan to achieve gains. In theworld of territorial animals,this principle explains thesuccess of defenders. Abiologistobservedthat“whena territory holder is

challenged by a rival, theowneralmostalwayswinsthecontest—usually within amatterofseconds.”Inhumanaffairs, the same simple ruleexplains much of whathappens when institutionsattempttoreformthemselves,in “reo Brro Q;reoBrrrganizations” and“restructuring”ofcompanies,and inefforts to rationalizeabureaucracy, simplify the taxcode,orreducemedicalcosts.

As initially conceived, plansfor reform almost alwaysproduce many winners andsome losers while achievingan overall improvement. Ifthe affected parties have anypolitical influence, however,potential losers will be moreactive and determined thanpotential winners; theoutcome will be biased intheir favor and inevitablymore expensive and lesseffective than initially

planned. Reforms commonlyinclude grandfather clausesthat protect current stake-holders—for example, whenthe existing workforce isreduced by attrition ratherthan by dismissals, or whencuts in salaries and benefitsapplyonly to futureworkers.Loss aversion is a powerfulconservativeforcethatfavorsminimal changes from thestatusquointhelivesofbothinstitutions and individuals.

Thisconservatismhelpskeepus stable in ourneighborhood, our marriage,and our job; it is thegravitational force that holdsour life together near thereferencepoint.

LossAversionintheLaw

Duringtheyearthatwespentworking together inVancouver, Richard Thaler,

Jack Knetsch, and I weredrawnintoastudyoffairnessin economic transactions,partly because we wereinterestedinthetopicbutalsobecause we had anopportunity as well as anobligation tomake up a newquestionnaire every week.The Canadian government’sDepartment of Fisheries andOceans had a program forunemployed professionals inToronto, who were paid to

administertelephonesurveys.The large team ofinterviewers worked everynightandnewquestionswereconstantlyneededtokeeptheoperation going. ThroughJack Knetsch, we agreed togenerate a questionnaireevery week, in four color-labeled versions. We couldask about anything; the onlyconstraint was that thequestionnaire should includeat least one mention of fish,

to make it pertinent to themission of the department.This went on for manymonths, and we treatedourselves to an orgy of datacollection.We studied public

perceptions of whatconstitutesunfairbehavioronthe part of merchants,employers, and landlords.Ouroverarchingquestionwaswhether the opprobriumattached to unfairness

imposes constraints on profitseeking. We found that itdoes.We also found that themoral rules by which thepublic evaluates what firmsmay or may not do draw acrucial distinction betweenlosses and gains. The basicprinciple is that the existingwage, price, or rent sets areferencepoint,whichhasthenature of an entitlement thatmust not be infringed. It isconsideredunfairforthefirm

to impose losses on itscustomersorworkersrelativeto the reference transaction,unlessitmustdosotoprotectitsownentitlement.Considerthisexample:

A hardware store hasbeen selling snowshovels for $15. Themorning after a largesnowstorm, the storeraisesthepriceto$20.Pleaseratethisactionas:

Completely FairAcceptable Unfair VeryUnfair

The hardware store behavesappropriatelyaccordingtothestandard economic model: itrespondstoincreaseddemandby raising its price. Theparticipants in the surveydidnot agree: 82% rated theactionUnfairorVeryUnfair.They evidently viewed thepre-blizzard price as a

referencepointandtheraisedprice as a loss that the storeimposesonitscustomers,notbecause it must but simplybecauseitcan.Abasicruleoffairness,wefound,iBrroQd,iBrrs that theexploitationofmarket power to imposelosses on others isunacceptable. The followingexampleillustratesthisruleinanother context (the dollarvaluesshouldbeadjustedforabout 100% inflation since

these data were collected in1984):

A small photocopyingshop has one employeewho has worked thereforsixmonthsandearns$9 per hour. Businesscontinues to besatisfactory, but afactory in the area hasclosed andunemployment hasincreased. Other small

shops have now hiredreliableworkersat$7anhour to perform jobssimilar to thosedonebythe photocopy shopemployee.Theownerofthe shop reduces theemployee’swageto$7.

The respondents did notapprove: 83% considered thebehavior Unfair or VeryUnfair. However, a slightvariation on the question

clarifies the nature of theemployer’s obligation. Thebackground scenario of aprofitable store in an area ofhigh unemployment is thesame,butnow

the current employeeleaves, and the ownerdecides to pay areplacement$7anhour.

A large majority (73%)considered this action

Acceptable. It appears thattheemployerdoesnothaveamoralobligationtopay$9anhour. The entitlement ispersonal: the current workerhasa right to retainhiswageeven if market conditionswould allow the employer toimpose a wage cut. Thereplacement worker has noentitlement to the previousworker’sreferencewage,andthe employer is thereforeallowed to reduce pay

without the risk of beingbrandedunfair.The firm has its own

entitlement,whichistoretainitscurrentprofit. If it facesathreat of a loss, it is allowedto transfer the loss to others.A substantial majority ofrespondentsbelievedthatitisnotunfairforafirmtoreduceits workers’ wages when itsprofitability is falling. Wedescribed the rules asdefining dual entitlements to

the firm and to individualswithwhomitinteracts.Whenthreatened,itisnotunfairforthefirmtobeselfish.Itisnotevenexpectedtotakeonpartofthelosses;itcanpassthemon.Different rules governed

what the firm could do toimproveitsprofitsortoavoidreducedprofits.When a firmfaced lowerproductioncosts,the rules of fairness did notrequireittosharethebonanza

witheitheritscustomersoritsworkers. Of course, ourrespondents liked a firmbetter and described it asmore fair if it was generouswhenitsprofitsincreased,buttheydidnotbrandasunfairafirm that did not share.Theyshowed indignation onlywhen a firm exploited itspower to break informalcontracts with workers orcustomers, and to impose aloss on others in order to

increase its profit. Theimportanttaskforstudentsofeconomic fairness is not toidentify idealbehaviorbut tofind the line that separatesacceptable conduct fromactionsthatinviteopprobriumandpunishment.We were not optimistic

whenwesubmittedourreportof this research to theAmerican Economic Review.Our article challenged whatwas then accepted wisdom

amongmany economists thateconomic behavior is ruledby self-interest and thatconcerns for fairness aregenerally irrelevant.We alsorelied on the evidence ofsurvey responses, for whicheconomists generally havelittle respect. However, theeditor of the journal sent ourarticle for evaluation to twoeconomists who were notbound by those conventions(we later learned their

identity; they were the mostfriendlytheeditorcouldhavefound). The editor made thecorrect call. The article isoften cited, and itsconclusions Brro Qions Brrhave stood the test of time.More recent research hassupported theobservationsofreference-dependent fairnessand has also shown thatfairness concerns areeconomically significant, afactwehadsuspectedbutdid

not prove. Employers whoviolate rules of fairness arepunished by reducedproductivity, and merchantswho follow unfair pricingpolicies can expect to losesales. People who learnedfrom a new catalog that themerchant was now chargingless for a product that theyhad recently bought at ahigher price reduced theirfuture purchases from thatsupplier by 15%, an average

lossof$90percustomer.Thecustomers evidentlyperceived the lower price asthe reference point andthought of themselves ashaving sustained a loss bypayingmorethanappropriate.Moreover,thecustomerswhoreacted the most stronglywere thosewhoboughtmoreitems and at higher prices.The losses far exceeded thegains from the increasedpurchases produced by the

lower prices in the newcatalog.Unfairlyimposinglosseson

people can be risky if thevictims are in a position toretaliate. Furthermore,experiments have shown thatstrangers who observe unfairbehavior often join in thepunishment.Neuroeconomists (scientistswhocombineeconomicswithbrain research) have usedMRImachinestoexaminethe

brains of people who areengaged in punishing onestrangerforbehavingunfairlyto another stranger.Remarkably, altruisticpunishment is accompaniedby increased activity in the“pleasure centers” of thebrain. It appears thatmaintaining the social orderand the rules of fairness inthisfashionisitsownreward.Altruistic punishment couldwell be the glue that holds

societies together. However,ourbrainsarenotdesignedtoreward generosity as reliablyas they punish meanness.Hereagain,wefindamarkedasymmetry between lossesandgains.The influence of loss

aversion and entitlementsextends farbeyond the realmof financial transactions.Jurists were quick torecognize their impacton thelawand in theadministration

ofjustice.Inonestudy,DavidCohen and Jack Knetschfound many examples of asharp distinction betweenactual losses and foregonegains in legal decisions. Forexample, a merchant whosegoodswerelostintransitmaybe compensated for costs heactually incurred, but isunlikely to be compensatedfor lost profits. The familiarrule that possession is nine-tenthsofthelawconfirmsthe

moral status of the referencepoint. In a more recentdiscussion,EyalZamirmakestheprovocativepoint that thedistinction drawn in the lawbetween restoring losses andcompensating for foregonegains may be justified bytheir asymmetrical effects onindividual well-being. Ifpeople who lose suffer morethan people who merely failto gain, they may alsodeservemoreprotectionfrom

thelaw.

SpeakingofLosses

“This reform will notpass.Thosewhostandtolose will fight harderthan those who stand togain.”

“Eachofthemthinkstheother’s concessions areless painful. They areboth wrong, of course.

It’s just the asymmetryoflosses.”

“They would find iteasier to renegotiate theagreement if theyrealized the pie wasactually expanding.They’re not allocatinglosses; they areallocatinggains.”

“Rental prices around

herehavegoneuprBrroQup r Brrecently, butour tenants don’t thinkit’s fair that we shouldraise their rent, too.They feel entitled totheircurrentterms.”

“My clients don’t resentthe price hike becausethey know my costshavegoneup, too.Theyaccept my right to stay

profitable.”

P

TheFourfoldPattern

Whenever you form a globalevaluation of a complexobject—a car you may buy,your son-in-law, or anuncertain situation—youassign weights to itscharacteristics.Thisissimplyacumbersomewayofsayingthat some characteristicsinfluence your assessment

more than others do. Theweighting occurs whether ornot you are aware of it; it isan operation of System 1.Your overall evaluation of acar may put more or lessweight on gas economy,comfort,orappearance.Yourjudgment of your son-in-lawmay dependmore or less onhow rich or handsome orreliablehe is.Similarly,yourassessment of an uncertainprospect assigns weights to

the possible outcomes. Theweights are certainlycorrelated with theprobabilities of theseoutcomes: a 50% chance towin a million is much moreattractivethana1%chancetowin the same amount. Theassignment of weights issometimes conscious anddeliberate. Most often,however, you are just anobserver to a globalevaluationthatyourSystem1

delivers.

ChangingChancesOnereasonforthepopularityof the gambling metaphor inthe study of decisionmakingis that it provides a naturalrule for the assignment ofweights to theoutcomesof aprospect: the more probableanoutcome, themoreweightit should have. The expectedvalue of a gamble is the

averageofitsoutcomes,eachweighted by its probability.For example, the expectedvalueof“20%chance towin$1,000 and 75% chance towin$100”is$275.Inthepre-Bernoullidays,gambleswereassessed by their expectedvalue. Bernoulli retained thismethodforassigningweightsto the outcomes, which isknown as the expectationprinciple,butappliedittothepsychological value of the

outcomes. The utility of agamble, in his theory, is theaverage of the utilities of itsoutcomes, each weighted byitsprobability.The expectation principle

does not correctly describehow you think about theprobabilities related to riskyprospects. In the fourexamples below, yourchances of receiving $1millionimproveby5%.Isthenews equally good in each

case?

A.From0to5%B.From5%to10%C.From60%to65%D.From95%to100%

The expectation principleasserts that your utilityincreases in each case byexactly 5% of the utility ofreceiving $1 million. Doesthis prediction describe yourexperiences?Ofcoursenot.

Everyone agrees that 05% and 95% 100% aremore impressive than either5% 10% or 60% 65%.Increasingthechancesfrom0to 5% transforms thesituation, creating apossibility that did not existearlier,ahopeofwinningtheprize. It is a qualitativechange, where 5 10% isonly a quantitativeimprovement. The changefrom5% to10%doubles the

probability of winning, butthere is general agreementthat the psychological valueof the prospect does notdouble.Thelargeimpactof0 5% illustrates the

possibility effect, whichcauses highly unlikelyoutcomes to be weighteddisproportionately more thanthey “deserve.” People whobuy lottery tickets in vastamounts show themselveswilling to pay much more

than expected value for verysmall chances to win a largeprize.Theimprovementfrom95%

to100%isanotherqualitativechange that has a largeimpact, the certainty effect.Outcomes that are almostcertain are given less weightthan their probabilityjustifies. To appreciate thecertainty effect, imagine thatyou inherited $1million, butyour greedy stepsister has

contested the will in court.The decision is expectedtomorrow. Your lawyerassures you that you have astrongcaseandthatyouhavea 95%chance towin, but hetakespainstoremindyouthatjudicial decisions are neverperfectly predictable. Nowyouareapproachedbyarisk-adjustment company, whichoffers to buy your case for$910,000outright—take it orleave it. The offer is lower

(by $40,000!) than theexpectedvalueofwaitingforthe judgment (which is$950,000), but are you quitesureyouwouldwanttorejectit? If such an event actuallyhappens in your life, youshould know that a largeindustry of “structuredsettlements”exists toprovidecertaintyataheftyprice,bytaking advantage of thecertaintyeffect.Possibility and certainty

have similarly powerfuleffects in the domain oflosses. When a loved one iswheeled into surgery, a 5%risk that an amputation willbe necessary is very bad—much more than half as badasa10%risk.Becauseofthepossibility effect, we tend tooverweight small risks andare willing to pay far morethan expected value toeliminate them altogether.The psychological difference

between a 95% risk ofdisaster and the certainty ofdisaster appears to be evengreater;thesliverofhopethateverythingcouldstillbeokaylooms very large.Overweighting of smallprobabilities increases theattractiveness of bothgambles and insurancepolicies.The conclusion is

straightforward: the decisionweights that people assign to

outcomes are not identical tothe probabilities of theseoutcomes, contrary to theexpectation principle.Improbable outcomes areoverweighted—this is thepossibility effect. Outcomesthat are almost certain areunderweighted relative toactual certainty. Theexpectation principle, bywhichvaluesareweightedbytheir probability, is poorpsychology.

Theplot thickens,however,because there is a powerfulargument that a decisionmaker who wishes to berationalmust conform to theexpectation principle. Thiswas the main point of theaxiomatic version of utilitytheorythatvonNeumannandMorgenstern introduced in1944. They proved that anyweighting of uncertainoutcomes that is not strictlyproportional to probability

leads to inconsistencies andother disasters. Theirderivation of the expectationprinciple from axioms ofrational choice wasimmediately recognized as amonumental achievement,whichplacedexpectedutilitytheory at the core of therational agent model ineconomics and other socialsciences. Thirty years later,whenAmosintroducedmetotheirwork,hepresented it as

an object of awe. He alsointroduced me Bima a meBimto a famous challenge tothattheory.

Allais’sParadoxIn1952,afewyearsafterthepublication of von Neumannand Morgenstern’s theory, ameeting was convened inParis to discuss theeconomics of risk. Many ofthe most renowned

economists of the time werein attendance. The Americanguests included the futureNobel laureates PaulSamuelson, Kenneth Arrow,andMiltonFriedman,aswellas the leading statisticianJimmieSavage.Oneoftheorganizersofthe

Paris meeting was MauriceAllais, who would alsoreceive a Nobel Prize someyears later. Allais hadsomething up his sleeve, a

coupleofquestionsonchoicethat he presented to hisdistinguishedaudience.Intheterms of this chapter, Allaisintended to show that hisguests were susceptible to acertainty effect and thereforeviolated expected utilitytheory and the axioms ofrationalchoiceonwhich thattheory rests. The followingset of choices is a simplifiedversion of the puzzle thatAllais constructed. In

problems A and B, whichwouldyouchoose?

A. 61% chance to win$520,000OR63% chance towin$500,000

B. 98% chance to win$520,000OR100%chancetowin$500,000If you are like most otherpeople,youpreferredtheleft-handoptioninproblemAand

you preferred the right-handoption inproblemB. If thesewere your preferences, youhavejustcommittedalogicalsin and violated the rules ofrational choice. Theillustrious economistsassembledinPariscommittedsimilar sins in a moreinvolved version of the“Allaisparadox.”To see why these choices

areproblematic, imagine thatthe outcome will be

determined by a blind drawfromanurnthatcontains100marbles—you win if youdrawaredmarble,youloseifyou draw white. In problemA, almost everybody prefersthe left-hand urn, although ithas fewer winning redmarbles, because thedifference in the size of theprizeismoreimpressivethanthe difference in the chancesof winning. In problem B, alarge majority chooses the

urn that guarantees a gain of$500,000. Furthermore,people are comfortable withboth choices—until they areled through the logic of theproblem.Compare the twoproblems,

andyouwillsee that the twourns of problem B are morefavorableversionsoftheurnsof problemA,with 37whitemarbles replaced by redwinningmarbles ineachurn.The improvement on the left

is clearly superior to theimprovement on the right,since each red marble givesyouachancetowin$520,000ontheleftandonly$500,000ontheright.Soyoustartedinthe first problem with apreference for the left-handurn, which was thenimprovedmorethantheright-hand urn—but now you likethe one on the right! Thispattern of choices does notmake logical sense, but a

psychological explanation isreadily available: thecertainty effect is at work.The2%differencebetweena100% and a 98% chance towin in problem B is vastlymore impressive than thesamedifferencebetween63%and61%inproblemA.As Allais had anticipated,

the sophisticated participantsat themeeting did not noticethattheirpreferencesviolatedutility theory until he drew

their attention to that fact asthemeetingwasabouttoend.Allais had intended thisannouncement to be abombshell: the leadingdecisiontheoristsintheworldhad preferences that wereinconsistent with their ownview of rationality! Heapparently believed that hisaudiencewouldbepersuadedto give up the approach thatBima ahat Bimhe rathercontemptuously labeled “the

American school” and adoptanalternative logicofchoicethat he had developed. Hewastobesorelydisappointed.Economists who were not

aficionadosofdecisiontheorymostly ignored the Allaisproblem. As often happenswhen a theory that has beenwidely adopted and founduseful is challenged, theynoted the problem as ananomalyandcontinuedusingexpected utility theory as if

nothing had happened. Incontrast,decisiontheorists—amixed collection ofstatisticians, economists,philosophers, andpsychologists—took Allais’schallenge very seriously.WhenAmosand Ibeganourwork,oneofourinitialgoalswas to develop a satisfactorypsychological account ofAllais’sparadox.Most decision theorists,

notably including Allais,

maintained their belief inhumanrationalityandtriedtobend the rules of rationalchoice to make the Allaispattern permissible.Over theyears there have beenmultiple attempts to find aplausible justification for thecertainty effect, none veryconvincing. Amos had littlepatience for these efforts; hecalled the theoristswho triedto rationalize violations ofutilitytheory“lawyersforthe

misguided.” We went inanother direction. Weretained utility theory as alogic of rational choice butabandoned the idea thatpeople are perfectly rationalchoosers.Wetookonthetaskofdevelopingapsychologicaltheory that would describethe choices people make,regardlessofwhethertheyarerational. In prospect theory,decision weights would notbeidenticaltoprobabilities.

DecisionWeightsMany years after wepublished prospect theory,Amos and I carried out astudy in which we measuredthe decision weights thatexplained people’spreferences for gambleswithmodestmonetary stakes.Theestimatesforgainsareshownintable4.

Table4

Youcanseethatthedecisionweights are identical to thecorrespondingprobabilitiesattheextremes:bothequal to0when the outcome isimpossible,andbothequalto100 when the outcome is asurething.However,decisionweights depart sharply from

probabilities near thesepoints. At the low end, wefind the possibility effect:unlikely events areconsiderably overweighted.For example, the decisionweight that corresponds to a2% chance is 8.1. If peopleconformed to the axioms ofrational choice, the decisionweight would be 2—so therareeventisoverweightedbya factor of 4. The certaintyeffect at the other end of the

probabilityscaleisevenmorestriking. A 2% risk of notwinningtheprizereducestheutilityofthegambleby13%,from100to87.1.To appreciate the

asymmetry between thepossibility effect and thecertainty effect, imagine firstthatyouhavea1%chancetowin $1 million. You willknowtheoutcometomorrow.Now, imagine that you arealmost certain to win $1

million, but there is a 1%chance that you will not.Again, you will learn theoutcome tomorrow. Theanxiety of the secondsituation appears to be moresalient than the hope in thefirst. The certainty effect isalso more striking than thepossibility effect if theoutcomeisasurgicaldisasterrather than a financial gain.Compare the intensity withwhich you focus on the faint

sliverofhopeinanoperationthat is almost certain to befatal,comparedtothefearofa1%risk.< Bima av> < Bimpheight="0%"width="5%">Thecombination of the certaintyeffect and possibility effectsat the two ends of theprobabilityscale is inevitablyaccompanied by inadequatesensitivity to intermediateprobabilities. You can see

thattherangeofprobabilitiesbetween 5% and 95% isassociated with a muchsmaller range of decisionweights (from 13.2 to 79.3),about two-thirds as much asrationally expected.Neuroscientists haveconfirmedtheseobservations,finding regions of the brainthatrespondtochangesintheprobability of winning aprize.Thebrain’sresponsetovariations of probabilities is

strikingly similar to thedecision weights estimatedfromchoices.Probabilities that are

extremelyloworhigh(below1% or above 99%) are aspecial case. It is difficult toassign a unique decisionweight to very rare events,because they are sometimesignoredaltogether,effectivelyassignedadecisionweightofzero.Ontheotherhand,whenyou do not ignore the very

rareevents,youwillcertainlyoverweight them.Most of usspend very little timeworrying about nuclearmeltdowns or fantasizingabout large inheritances fromunknown relatives.However,when an unlikely eventbecomes the focus ofattention, we will assign itmuch more weight than itsprobability deserves.Furthermore, people arealmostcompletely insensitive

to variations of risk amongsmall probabilities. A cancerrisk of 0.001% is not easilydistinguished from a risk of0.00001%, although theformer would translate to3,000 cancers for thepopulation of the UnitedStates,andthelatterto30.

When you pay attention to athreat, you worry—and thedecision weights reflect how

much youworry.Because ofthe possibility effect, theworry is not proportional tothe probability of the threat.Reducing or mitigating therisk is not adequate; toeliminate the worry theprobability must be broughtdowntozero.The question below is

adapted from a study of therationality of consumervaluations of health risks,which was published by a

team of economists in the1980s. The survey wasaddressed toparentsof smallchildren.

Suppose that youcurrently use an insectspray thatcostsyou$10per bottle and it resultsin 15 inhalationpoisonings and 15 childpoisonings for every10,000 bottles of insectspraythatareused.

You learn of a moreexpensive insecticidethat reduces each of therisks to 5 for every10,000 bottles. Howmuch would you bewillingtopayforit?

The parents were willing topay an additional $2.38, onaverage, to reduce the risksby two-thirds from 15 per10,000 bottles to 5. They

were willing to pay $8.09,more than three times asmuch, to eliminate itcompletely. Other questionsshowed that the parentstreated the two risks(inhalation and childpoisoning) as separateworries and were willing topay a certainty premium forthe complete elimination ofeither one. This premium iscompatible with thepsychology of worry but not

withtherationalmodel.

TheFourfoldPatternWhenAmosand Ibeganourwork on prospect theory, wequickly reached twoconclusions: people attachvalues to gains and lossesratherthantowealth,andthedecision weights that theyassign to outcomes aredifferent from probabilities.Neither idea was completely

new,but incombination theyexplainedadistinctivepatternof preferences that we caBima ae ca Bimlled thefourfold pattern. The namehas stuck. The scenarios areillustratedbelow.

Figure13

Thetoprowineachcellshows an illustrativeprospect.The second rowcharacterizes the focalemotion that theprospectevokes.The third row indicateshowmostpeoplebehavewhen offered a choicebetweenagambleandasure gain (or loss) thatcorresponds to itsexpected value (for

example, between “95%chance to win $10,000”and “$9,500 withcertainty”). Choices aresaid to be risk averse ifthe sure thing ispreferred,riskseekingifthegambleispreferred.Thefourthrowdescribestheexpectedattitudesofa defendant and aplaintiff as they discussa settlement of a civilsuit.

The fourfold pattern ofpreferences isconsideredoneof the core achievements ofprospect theory.Threeof thefour cells are familiar; thefourth (top right) was newandunexpected.

The top left is the onethatBernoullidiscussed:peopleareaversetoriskwhen they consider

prospects with asubstantial chance toachieve a large gain.They are willing toaccept less than theexpected value of agamble to lock inasuregain.The possibility effect inthe bottom left cellexplains why lotteriesare popular. When thetop prize is very large,ticket buyers appear

indifferent to the factthat their chance ofwinning isminuscule.Alottery ticket is theultimate example of thepossibility effect.Without a ticket youcannotwin,withaticketyou have a chance, andwhether the chance istiny or merely smallmatters little.Of course,whatpeopleacquirewitha ticket is more than a

chance to win; it is therighttodreampleasantlyofwinning.The bottom right cell iswhere insurance isbought. People arewilling to pay muchmore for insurance thanexpected value—whichis how insurancecompanies cover theircosts and make theirprofits. Here again,people buy more than

protection against anunlikely disaster; theyeliminate a worry andpurchasepeaceofmind.

The results for the top rightcell initiallysurprisedus.Wewere accustomed to think interms of risk aversion exceptforthebottomleftcell,wherelotteries are preferred. Whenwe looked at our choices forbad options, we quickly

realized that wewere just asriskseekinginthedomainoflossesaswewereriskaversein the domain of gains. Wewere not the first to observerisk seeking with negativeprospects—at least twoauthorshadreportedthatfact,but they had notmademuchof it. However, we werefortunate to have aframework that made thefindingofriskseekingeasytointerpret, and that was a

milestone in our thinking.Indeed, we identified tworeasonsforthiseffect.First, there is diminishing

sensitivity. The sure loss isvery aversive because thereaction to a loss of $900 ismore than 90% as intense asthe reaction to a loss of$1,000. The second factormay be evenmore powerful:the decision weight thatcorresponds to a probabilityof 90% is only about 71,

much lower than theprobability.The result is thatwhen you consider a choicebetween a sure loss and agamble with a highprobabilityoBimaatyoBimfa larger loss, diminishingsensitivity makes the sureloss more aversive, and thecertainty effect reduces theaversiveness of the gamble.The same two factorsenhance the attractiveness ofthe sure thingand reduce the

attractiveness of the gamblewhen the outcomes arepositive.The shape of the value

function and the decisionweightsbothcontributetothepattern observed in the toprowoftable13.Inthebottomrow,however,thetwofactorsoperateinoppositedirections:diminishing sensitivitycontinues to favor riskaversion for gains and riskseeking for losses, but the

overweighting of lowprobabilities overcomes thiseffect and produces theobservedpatternofgamblingfor gains and caution forlosses.Many unfortunate human

situations unfold in the topright cell. This is wherepeople who face very badoptions take desperategambles, accepting a highprobability of making thingsworseinexchangeforasmall

hopeofavoidingalargeloss.Risktakingofthiskindoftenturnsmanageablefailuresintodisasters. The thought ofaccepting the large sure lossistoopainful,andthehopeofcomplete relief too enticing,tomakethesensibledecisionthat it is time to cut one’slosses. This is wherebusinesses that are losingground to a superiortechnology waste theirremaining assets in futile

attemptstocatchup.Becausedefeatissodifficulttoaccept,the losing side in wars oftenfights long past the point atwhichthevictoryoftheotherside is certain, and only amatteroftime.

GamblingintheShadowoftheLaw

The legal scholar ChrisGuthrie has offered acompellingapplicationof the

fourfold pattern to twosituations in which theplaintiff and thedefendant inacivilsuitconsiderapossiblesettlement. The situationsdiffer in the strength of theplaintiff’scase.As in a scenario we saw

earlier,youaretheplaintiffinacivilsuitinwhichyouhavemadeaclaimforalargesumindamages.Thetrialisgoingvery well and your lawyercites expert opinion that you

have a 95% chance to winoutright,butaddsthecaution,“You never really know theoutcomeuntil the jurycomesin.”Yourlawyerurgesyoutoaccept a settlement in whichyou might get only 90% ofyourclaim.Youareinthetopleft cell of the fourfoldpattern, and the question onyour mind is, “Am I willingtotakeevenasmallchanceofgetting nothing at all? Even90% of the claim is a great

dealofmoney,andIcanwalkaway with it now.” Twoemotions are evoked, bothdrivinginthesamedirection:the attraction of a sure (andsubstantial) gain and the fearofintensedisappointmentandregret if you reject asettlement and lose in court.Youcanfeelthepressurethattypically leads to cautiousbehaviorinthissituation.Theplaintiffwithastrongcase islikelytoberiskaverse.

Now step into the shoes ofthe defendant in the samecase. Although you have notcompletely given up hope ofadecision inyour favor, yourealize that the trial is goingpoorly. The plaintiff’slawyers have proposed asettlement in which youwould have to pay 90% oftheir original claim, and it iscleartheywillnotacceptless.Will you settle, or will youpursuethecase?Becauseyou

face a high probability of aloss,yoursituationbelongsinthe top right cell. Thetemptation to fight on isstrong:thesettlementthattheplaintiffhasofferedisalmostas painful as the worstoutcome you face, and thereis still hope of prevailing incourt. Here again, twoemotions are involved: thesurelossisrepugnantandthepossibility of winning incourt is highly attractive. A

defendantwithaweakcaseislikely to be risk seeking,Bima aing, Bim prepared togamble rather than accept avery unfavorable settlement.Intheface-offbetweenarisk-averse plaintiff and a risk-seeking defendant, thedefendant holds the strongerhand.Thesuperiorbargainingposition of the defendantshould be reflected innegotiated settlements, withthe plaintiff settling for less

than thestatisticallyexpectedoutcome of the trial. Thisprediction from the fourfoldpattern was confirmed byexperiments conducted withlaw students and practicingjudges, and also by analysesof actual negotiations in theshadowofciviltrials.Now consider “frivolous

litigation,” when a plaintiffwith a flimsy case files alargeclaimthatismostlikelytofailincourt.Bothsidesare

aware of the probabilities,and both know that in anegotiated settlement theplaintiffwillgetonlyasmallfractionof the amountof theclaim. The negotiation isconducted in the bottom rowof the fourfold pattern. Theplaintiff is in the left-handcell, with a small chance towinavery large amount; thefrivolous claim is a lotteryticket for a large prize.Overweighting the small

chanceofsuccessisnaturalinthis situation, leading theplaintiff to be bold andaggressive in thenegotiation.Forthedefendant,thesuitisanuisancewith a small riskofa very bad outcome.Overweighting the smallchance of a large loss favorsriskaversion,andsettlingfora modest amount isequivalent to purchasinginsuranceagainsttheunlikelyevent of a bad verdict. The

shoeisnowontheotherfoot:the plaintiff is willing togamble and the defendantwants to be safe. Plaintiffswith frivolous claims arelikely to obtain a moregenerous settlement than thestatistics of the situationjustify.The decisions described by

the fourfold pattern are notobviously unreasonable. Youcan empathize in each casewith the feelings of the

plaintiff and the defendantthat lead them to adopt acombative or anaccommodating posture. Inthe long run, however,deviations from expectedvalue are likely to be costly.Consideralargeorganization,the City of New York, andsuppose it faces 200“frivolous” suits each year,eachwitha5%chancetocostthe city $1 million. Supposefurther that in each case the

city could settle the lawsuitfor a payment of $100,000.The city considers twoalternativepoliciesthatitwillapply toall suchcases:settleorgototrial.(Forsimplicity,Iignorelegalcosts.)

If the city litigates all200cases,itwilllose10,for a total loss of $10million.If the city settles every

case for $100,000, itstotal loss will be $20million.

Whenyoutakethelongviewof many similar decisions,you can see that paying apremiumtoavoidasmallriskof a large loss is costly. Asimilar analysis applies toeach of the cells of thefourfold pattern: systematicdeviations from expected

value are costly in the longrun—and this rule applies toboth risk aversion and riskseeking. Consistentoverweighting of improbableoutcomes—a feature ofintuitive decision making—eventually leads to inferioroutcomes.

SpeakingOfTheFourfoldPattern

“He is tempted to settle

this frivolous claim toavoid a freak loss,howeverunlikely.That’soverweighting of smallprobabilities.Sinceheislikely to face manysimilar problems, hewould be better off notyielding.”

“We never let ourvacations hang Bimaaang Bimon a last-

minute deal. We’rewilling to pay a lot forcertainty.”

“They will not cut theirlossessolongasthereisa chance of breakingeven. This is risk-seekinginthelosses.”

“Theyknowtheriskofagas explosion isminuscule,buttheywant

it mitigated. It’s apossibility effect, andthey want peace ofmind.”

P

RareEvents

I visited Israel several timesduring a period in whichsuicide bombings in buseswere relatively common—thoughofcoursequiterareinabsolute terms. There werealtogether 23 bombingsbetweenDecember 2001 andSeptember 2004, which hadcaused a total of 236

fatalities. The number ofdailybus riders in Israelwasapproximately 1.3 million atthat time. For any traveler,the risks were tiny, but thatwas not how the public feltabout it. People avoidedbusesasmuchas theycould,andmanytravelersspenttheirtime on the bus anxiouslyscanning their neighbors forpackages or bulky clothesthatmighthideabomb.I did not have much

occasion to travel on buses,asIwasdrivingarentedcar,but I was chagrined todiscover that my behaviorwasalsoaffected.IfoundthatIdidnotliketostopnexttoabusataredlight,andIdroveawaymorequicklythanusualwhenthelightchanged.Iwasashamed of myself, becauseof course I knew better. Iknew that the risk was trulynegligible,andthatanyeffectat all on my actions would

assign an inordinately high“decision weight” to aminusculeprobability.Infact,I was more likely to beinjured in a driving accidentthan by stopping near a bus.But my avoidance of buseswas not motivated by arational concern for survival.What drove me was theexperience of the moment:beingnext toabusmademethink of bombs, and thesethoughts were unpleasant. I

wasavoidingbusesbecauseIwantedtothinkofsomethingelse.My experience illustrates

howterrorismworksandwhyitissoeffective:itinducesanavailability cascade. Anextremely vivid image ofdeathanddamage,constantlyreinforcedbymediaattentionand frequent conversations,becomes highly accessible,especially if it is associatedwitha specific situation such

as the sight of a bus. Theemotional arousal isassociative, automatic, anduncontrolled, and it producesan impulse for protectiveaction.System2may“know”thattheprobabilityislow,butthis knowledge does noteliminate the self-generateddiscomfort and the wish toavoid it. System 1 cannot beturnedoff.Theemotionisnotonly disproportionate to theprobability, it is also

insensitive to the exact levelof probability. Suppose thattwo cities have been warnedabout thepresenceof suicidebombers. Residents of onecityaretoldthattwobombersare ready to strike.Residentsof another city are told of asingle bomber. Their risk islowerbyhalf,butdotheyfeelmuchsafer?

Many stores in New York

City sell lottery tickets, andbusiness is good. Thepsychology of high-prizelotteries is similar to thepsychology of terrorism.Thethrilling possibility ofwinning the big prize issharedbythecommunityandre Cmuninforced byconversations at work and athome. Buying a ticket isimmediately rewarded bypleasant fantasies, just asavoiding a bus was

immediately rewarded byrelieffromfear.Inbothcases,the actual probability isinconsequential; onlypossibility matters. Theoriginal formulation ofprospect theory included theargument that “highlyunlikely events are eitherignoredoroverweighted,”butit did not specify theconditions under which oneor the other will occur, nordiditproposeapsychological

interpretation of it. Mycurrent view of decisionweights has been stronglyinfluencedby recent researchon the role of emotions andvividnessindecisionmaking.Overweighting of unlikelyoutcomesisrootedinSystem1featuresthatarefamiliarbynow. Emotion and vividnessinfluence fluency,availability,andjudgmentsofprobability—and thusaccount for our excessive

response to the few rareeventsthatwedonotignore.

OverestimationandOverweighting

What is your judgmentof the probability thatthenextpresidentof theUnited States will be athird-partycandidate?

Howmuchwillyoupay

for a bet in which youreceive $1,000 if thenext president of theUnited States is a third-party candidate, and nomoneyotherwise?

The two questions aredifferent but obviouslyrelated. The first asks you toassess the probability of anunlikely event. The secondinvites you to put a decisionweightonthesameevent,by

placingabetonit.How do people make the

judgments and how do theyassign decision weights?Westart from two simpleanswers, then qualify them.Here are the oversimplifiedanswers:

People overestimate theprobabilities of unlikelyevents.People overweight

unlikely events in theirdecisions.

Although overestimation andoverweighting are distinctphenomena, the samepsychological mechanismsare involved inboth: focusedattention, confirmation bias,andcognitiveease.Specificdescriptions trigger

the associative machinery ofSystem1.Whenyou thought

abouttheunlikelyvictoryofathird-party candidate, yourassociative systemworked inits usual confirmatory mode,selectively retrievingevidence, instances, andimages that would make thestatement true. The processwasbiased,but itwasnotanexercise in fantasy. Youlooked for a plausiblescenario that conforms to theconstraintsofreality;youdidnot simply imagine the Fairy

oftheWestinstallingathird-party president. Yourjudgment of probability wasultimately determined by thecognitive ease, or fluency,with which a plausiblescenariocametomind.Youdonotalwaysfocuson

the event you are asked toestimate.Ifthetargeteventisvery likely, you focus on itsalternative. Consider thisexample:

What is the probabilitythatababyborn inyourlocal hospital will bereleased within threedays?

You were asked to estimatethe probability of the babygoing home, but you almostcertainly focused on theevents that might cause ababynottobereleasedwithinthe normal period.Ourmindhas a useful capability to

Bmun q to Bmufocusspontaneouslyonwhatever isodd, different, or unusual.Youquicklyrealizedthatitisnormal for babies in theUnited States (not allcountries have the samestandards) to be releasedwithin two or three days ofbirth,soyourattentionturnedto the abnormal alternative.The unlikely event becamefocal. The availabilityheuristic is likely to be

evoked: your judgment wasprobably determined by thenumber of scenarios ofmedical problems youproduced and by the easewith which they came tomind. Because you were inconfirmatorymode,thereisagood chance that yourestimate of the frequency ofproblemswastoohigh.The probability of a rare

event is most likely to beoverestimated when the

alternative is not fullyspecified. My favoriteexample comes from a studythat the psychologist CraigFox conducted while he wasAmos’s student. Foxrecruited fans of professionalbasketballandelicitedseveraljudgments and decisionsconcerning thewinner of theNBA playoffs. In particular,heaskedthemtoestimatetheprobability that each of theeight participating teams

would win the playoff; thevictory of each team in turnwasthefocalevent.You can surely guess what

happened, but the magnitudeof the effect that Foxobserved may surprise you.Imagine a fan who has beenaskedtoestimatethechancesthat the Chicago Bulls willwinthetournament.Thefocalevent is well defined, but itsalternative—one of the otherseven teams winning—is

diffuse and less evocative.The fan’s memory andimagination, operating inconfirmatorymode,aretryingto construct a victory for theBulls.When thesamepersonis next asked to assess thechances of the Lakers, thesameselectiveactivationwillwork in favor of that team.The eight best professionalbasketball teams in theUnited States are all verygood, and it is possible to

imagine even a relativelyweak team among thememerging as champion. Theresult: the probabilityjudgments generatedsuccessively for the eightteams added up to 240%!This pattern is absurd, ofcourse, because the sum ofthe chances of the eighteventsmust addup to 100%.The absurdity disappearedwhen the same judges wereasked whether the winner

wouldbefromtheEasternorthe Western conference. Thefocaleventanditsalternativewere equally specific in thatquestionandthejudgmentsoftheirprobabilitiesaddedupto100%.To assess decision weights,

Fox also invited thebasketball fans to bet on thetournament result. Theyassignedacashequivalent toeach bet (a cash amount thatwas just as attractive as

playingthebet).Winningthebet would earn a payoff of$160. The sum of the cashequivalents for the eightindividual teams was $287.An average participant whotook all eight bets would beguaranteed a loss of $127!The participants surely knewthattherewereeightteamsinthe tournament and that theaveragepayoff forbettingonall of them could not exceed$160, but they overweighted

nonetheless. The fans notonly overestimated theprobabilityoftheeventstheyfocused on—they were alsomuch too willing to bet onthem.These findings shed new

light on the planning fallacyand other manifestations ofoptimism. The successfulexecutionofaplanisspecificandeasytoimaginewhenonetries to forecast the outcomeof a project. In contrast, the

alternative of failure isdiffuse, because there areinnumerable ways for thingsto go wrong. Entrepreneursand the investors whoevaluate their prospects areprone both to overestimatetheir chances and tooverweighttheirestimates.

VividOutcomesAs we have seen, prospecttheory differs from utility

theory in the rel Bmun q relBmuationship it suggestsbetween probability anddecision weight. In utilitytheory, decision weights andprobabilities are the same.Thedecisionweightofasurething is 100, and the weightthat corresponds to a 90%chanceisexactly90,whichis9 times more than thedecision weight for a 10%chance. In prospect theory,variationsofprobabilityhave

less effect on decisionweights.AnexperimentthatImentioned earlier found thatthedecisionweightfora90%chance was 71.2 and thedecision weight for a 10%chancewas18.6.Theratioofthe probabilitieswas 9.0, butthe ratio of the decisionweights was only 3.83,indicating insufficientsensitivity to probability inthat range. In both theories,the decision weights depend

only on probability, not onthe outcome. Both theoriespredict that the decisionweight for a 90% chance isthe same for winning $100,receiving a dozen roses, orgettinganelectricshock.Thistheoretical prediction turnsouttobewrong.Psychologists at the

University of Chicagopublished an article with theattractive title “Money,Kisses, and Electric Shocks:

On theAffective Psychologyof Risk.” Their finding wasthat thevaluationofgambleswas much less sensitive toprobability when the(fictitious) outcomes wereemotional (“meeting andkissing your favorite moviestar”or“gettingapainful,butnot dangerous, electricshock”) than when theoutcomes were gains orlosses of cash. This was notan isolated finding. Other

researchers had found, usingphysiological measures suchas heart rate, that the fear ofan impending electric shockwas essentially uncorrelatedwith the probability ofreceivingtheshock.Themerepossibility of a shocktriggered the full-blown fearresponse. The Chicago teamproposed that “affect-ladenimagery” overwhelmed theresponse to probability. Tenyears later, a team of

psychologists at Princetonchallengedthatconclusion.The Princeton team argued

that the low sensitivity toprobability that had beenobserved for emotionaloutcomesisnormal.Gambleson money are the exception.The sensitivity to probabilityis relatively high for thesegambles,becausetheyhaveadefiniteexpectedvalue.

What amount of cash is

as attractive as each ofthesegambles?

A. 84% chance to win

$59B.84%chance to receive

onedozenredrosesinaglassvaseWhat do you notice? Thesalient difference is thatquestion A is much easierthanquestionB.Youdidnotstop tocompute theexpected

value of the bet, but youprobablyknewquicklythatitisnotfarfrom$50(infact itis $49.56), and the vagueestimate was sufficient toprovide a helpful anchor asyou searched for an equallyattractive cash gift. No suchanchor is available forquestionB,whichisthereforemuch harder to answer.Respondentsalsoassessedthecash equivalent of gambleswitha21%chancetowinthe

two outcomes. As expected,the difference between thehigh-probability and low-probability gambles wasmuch more pronounced forthemoneythanfortheroses.To bolster their argument

that insensitivity toprobability is not caused byemotion, the Princeton teamcompared willingness to paytoavoidgambles:

21% chance (or 84%

chance) to spend aweekend paintingsomeone’s three-bedroomapartment

21% chance (or 84%chance) to clean threestallsinadormitorybathBmun qbath Bmuroomafteraweekendofuse

Thesecondoutcomeissurelymuch more emotional thanthe first, but the decision

weightsforthetwooutcomesdid not differ. Evidently, theintensityofemotionisnottheanswer.Another experiment yielded

a surprising result. Theparticipants received explicitprice information along withthe verbal description of theprize.Anexamplecouldbe:

84% chance to win: Adozen red roses in aglassvase.Value$59.

21% chance to win: Adozen red roses in aglassvase.Value$59.

It is easy to assess theexpected monetary value ofthese gambles, but adding aspecific monetary value didnot alter the results:evaluations remainedinsensitivetoprobabilityeveninthatcondition.Peoplewhothoughtofthegiftasachance

toget rosesdidnotusepriceinformation as an anchor inevaluating the gamble. Asscientists sometimes say, thisis a surprising finding that istrying to tell us something.What story is it trying to tellus?Thestory,Ibelieve,isthata

rich and vivid representationof the outcome, whether ornot it is emotional, reducesthe role of probability in theevaluation of an uncertain

prospect. This hypothesissuggests a prediction, inwhichIhavereasonablyhighconfidence: adding irrelevantbut vivid details to amonetary outcome alsodisruptscalculation.Compareyour cash equivalents for thefollowingoutcomes:

21%(or84%)chancetoreceive $59 nextMonday

21%(or84%)chancetoreceive a large bluecardboard envelopecontaining $59 nextMondaymorning

The new hypothesis is thatthere will be less sensitivityto probability in the secondcase, because the blueenvelopeevokesa richerandmore fluent representationthan the abstract notion of asum of money. You

constructed theevent inyourmind,and thevivid imageoftheoutcomeexiststhereevenif you know that itsprobability is low. Cognitiveease contributes to thecertaintyeffectaswell:whenyouholdavivid imageofanevent, the possibility of itsnot occurring is alsorepresented vividly, andoverweighted. Thecombination of an enhancedpossibility effect with an

enhanced certainty effectleaveslittleroomfordecisionweights to change betweenchancesof21%and84%.

VividProbabilitiesThe idea that fluency,vividness, and the ease ofimagining contribute todecision weights gainssupport from many otherobservations.Participantsinawell-known experiment are

given a choice of drawing amarblefromoneoftwourns,in which red marbles win aprize:

Urn A contains 10marbles, of which 1 isred.Urn B contains 100marbles, of which 8 arered.

Which urn would youchoose? The chances of

winning are 10% in urn Aand 8% in urnB, somakingthe right choice should beeasy, but it is not: about30%–40%ofstudentschoosetheurnBmunqurnBmuwiththelargernumberofwinningmarbles, rather than the urnthat provides a better chanceofwinning.SeymourEpsteinhas argued that the resultsillustrate the superficialprocessing characteristic ofSystem1 (whichhe calls the

experientialsystem).As you might expect, the

remarkably foolish choicesthat people make in thissituation have attracted theattentionofmanyresearchers.The bias has been givenseveralnames;followingPaulSlovic I will call itdenominator neglect. If yourattention is drawn to thewinningmarbles, you do notassess the number ofnonwinningmarbleswith the

same care. Vivid imagerycontributes to denominatorneglect, at least as Iexperienceit.WhenIthinkofthe small urn, I see a singlered marble on a vaguelydefined background of whitemarbles.When I thinkof thelargerurn,Iseeeightwinningred marbles on an indistinctbackgroundofwhitemarbles,whichcreatesamorehopefulfeeling. The distinctivevividness of the winning

marbles increases thedecisionweightofthatevent,enhancing the possibilityeffect. Of course, the samewill be true of the certaintyeffect.IfIhavea90%chanceofwinning a prize, the eventof not winning will be moresalient if 10 of 100 marblesare “losers” than if 1 of 10marbles yields the sameoutcome.The idea of denominator

neglect helps explain why

different ways ofcommunicating risks vary somuch in their effects. Youread that “a vaccine thatprotectschildren froma fataldisease carries a 0.001% riskofpermanentdisability.”Therisk appears small. Nowconsider another descriptionof the same risk: “One of100,000 vaccinated childrenwill be permanentlydisabled.” The secondstatement does something to

yourmind that the first doesnot: it calls up the image ofan individual child who ispermanently disabled by avaccine; the 999,999 safelyvaccinated children havefadedintothebackground.Aspredicted by denominatorneglect, low-probabilityeventsaremuchmoreheavilyweighted when described interms of relative frequencies(howmany)thanwhenstatedin more abstract terms of

“chances,” “risk,” or“probability”(howlikely).Aswe have seen, System 1 ismuch better at dealing withindividualsthancategories.The effect of the frequency

format is large. Inonestudy,people who saw informationabout “a disease that kills1,286 people out of every10,000” judged it as moredangerous than people whowere told about “a diseasethat kills 24.14% of the

population.”Thefirstdiseaseappearsmorethreateningthanthe second, although theformer risk is only half aslargeasthelatter!Inanevenmore direct demonstration ofdenominator neglect, “adiseasethatkills1,286peopleout of every 10,000” wasjudgedmoredangerousthanadiseasethat“kills24.4outof100.”Theeffectwouldsurelybe reduced or eliminated ifparticipantswere asked for a

direct comparison of the twoformulations, a task thatexplicitly calls for System 2.Life, however, is usually abetween-subjects experiment,in which you see only oneformulation at a time. Itwould take an exceptionallyactive System 2 to generatealternative formulations ofthe one you see and todiscover that they evoke adifferentresponse.Experienced forensic

psychologists andpsychiatrists are not immunetotheeffectsoftheformatinwhich risks are expressed. Inoneexperiment,professionalsevaluatedwhetheritwassafeto discharge from thepsychiatrichospital apatient,Mr. Jones, with a history ofviolence. The informationthey received included anexpert’s assessment of therisk.Thesamestatisticsweredescribedintwoways:

Patients similar to Mr.Jones are estimated tohave a 10% probabilityof committing an act ofviolence against othersduring the first severalmonthsafterdischarge.

Of every 100 patientssimilar toMr. Jones, 10are estimated to commitan act of violenceagainstothersduringthe

firstseveralmonthsafterdischarge.

The professionals who sawthe frequency format werealmosttwiceaslikelytodenythe discharge (41%,compared to 21% in theprobabilityformat).Themorevivid description produces ahigherdecisionweightforthesameprobability.Thepowerofformatcreates

opportunities for

manipulation, which peoplewith an axe to grind knowhowtoexploit.Slovicandhiscolleagues cite an article thatstates that “approximately1,000 homicides a year arecommitted nationwide byseriously mentally illindividuals who are nottaking their medication.”Another way of expressingthe same fact is that “1,000out of 273,000,000Americans will die in this

manner each year.” Anotheris that “the annual likelihoodof being killed by such anindividual is approximately0.00036%.” Still another:“1,000Americanswill die inthismannereachyear,orlessthan one-thirtieth the numberwho will die of suicide andabout one-fourth the numberwho will die of laryngealcancer.”Slovicpointsoutthat“these advocates are quiteopen about their motivation:

they want to frighten thegeneralpublicaboutviolenceby people with mentaldisorder,inthehopethatthisfear will translate intoincreased funding for mentalhealthservices.”Agoodattorneywhowishes

to cast doubt on DNAevidencewillnottellthejurythat “the chance of a falsematch is 0.1%.” Thestatement that “a falsematchoccurs in 1 of 1,000 capital

cases” is far more likely topass the threshold ofreasonable doubt. The jurorshearing those words areinvited togenerate the imageof the man who sits beforethem in the courtroom beingwronglyconvictedbecauseofflawed DNA evidence. Theprosecutor, of course, willfavorthemoreabstractframe—hoping to fill the jurors’mindswithdecimalpoints.

DecisionsfromGlobalImpressions

The evidence suggests thehypothesisthatfocalattentionand salience contribute toboth the overestimation ofunlikely events and theoverweighting of unlikelyoutcomes. Salience isenhancedbymerementionofanevent,byitsvividness,andby the format in whichprobability is described.

There are exceptions, ofcourse, inwhich focusing onan event does not raise itsprobability:casesinwhichanerroneous theory makes aneventappear impossibleevenwhen you think about it, orcasesinwhichaninability toimagine how an outcomemightcomeaboutleavesyouconvinced that it will nothappen. The bias towardoverestimation andoverweighting of salient

eventsisnotanabsoluterule,butitislargeandrobust.There has been much

interest in recent years instudies of choice fromexperience, which followdifferent rules from thechoices from description thatare analyzed in prospecttheory. Participants in atypical experiment face twobuttons. When pressed, eachbutton produces either amonetary reward or nothing,

and the outcome is drawnrandomly according to thespecifications of a prospect(for example, “5% to win$12” or “95% chance towin$1”). The process is trulyrandom,sBmunqm,sBmuothere isnoguarantee that thesample a participant seesexactly represents thestatisticalsetup.Theexpectedvalues associated with thetwo buttons areapproximately equal, but one

isriskier(morevariable)thanthe other. (For example, onebutton may produce $10 on5%ofthetrialsandtheother$1 on 50% of the trials).Choice from experience isimplementedbyexposing theparticipant to many trials inwhich she can observe theconsequencesofpressingonebutton or another. On thecritical trial, she chooses oneof the two buttons, and sheearns the outcome on that

trial.Choicefromdescriptionis realized by showing thesubject theverbaldescriptionof the risky prospectassociated with each button(such as “5% to win $12”)andaskinghertochooseone.As expected from prospecttheory, choice fromdescription yields apossibility effect—rareoutcomes are overweightedrelativetotheirprobability.Insharpcontrast,overweighting

is never observed in choicefrom experience, andunderweightingiscommon.The experimental situation

of choice by experience isintended to represent manysituations in which we areexposedtovariableoutcomesfrom the same source. Arestaurantthatisusuallygoodmay occasionally serve abrilliant or an awful meal.Your friend is usually goodcompany, but he sometimes

turns moody and aggressive.California is prone toearthquakes, but they happenrarely. The results of manyexperiments suggest that rareevents are not overweightedwhenwemakedecisionssuchas choosing a restaurant ortying down the boiler toreduceearthquakedamage.The interpretationof choice

from experience is not yetsettled, but there is generalagreement on one major

cause of underweighting ofrare events, both inexperiments and in the realworld: many participantsnever experience the rareevent! Most Californianshave never experienced amajor earthquake, and in2007 no banker hadpersonally experienced adevastating financial crisis.Ralph Hertwig and Ido Erevnote that “chances of rareevents (such as the burst of

housingbubbles) receive lessimpact than they deserveaccording to their objectiveprobabilities.” They point tothepublic’stepidresponsetolong-term environmentalthreatsasanexample.These examples of neglect

areboth importantandeasilyexplained, butunderweighting also occurswhen people have actuallyexperienced the rare event.Suppose you have a

complicatedquestionthattwocolleagues on your floorcould probably answer. Youhave known them both foryears and have had manyoccasions to observe andexperience their character.Adele is fairlyconsistentandgenerally helpful, though notexceptional on thatdimension.Brian is not quiteas friendly and helpful asAdele most of the time, buton some occasions he has

beenextremelygenerouswithhis time and advice. Whomwillyouapproach?Considertwopossibleviews

ofthisdecision:

It is a choice betweentwo gambles. Adele isclosertoasurething;theprospect of Brian ismore likely to yield aslightly inferioroutcome, with a low

probability of a verygoodone.Therareeventwill be overweighted bya possibility effect,favoringBrian.It is a choice betweenyour global impressionsofAdeleandBrian.Thegood and the badexperiences you havehad are pooled in yourrepresentation of theirnormal behavior.Unlessthe rare event is so

extreme that it comes tomind separately (Brianonce verbally abused acolleaguewhoasked forhis help), the norm willbebiased toward typicaland recent instances,favoringAdele.

In a two-system mind, thesecond interpretationaBmunqon a Bmuppears far moreplausible.System1generates

global representations ofAdele and Brian, whichinclude an emotional attitudeandatendencytoapproachoravoid. Nothing beyond acomparison of thesetendencies is needed todetermine the door onwhichyou will knock. Unless therare event comes to yourmindexplicitly,itwillnotbeoverweighted. Applying thesame idea to theexperimentson choice from experience is

straightforward. As they areobserved generatingoutcomes over time, the twobuttons develop integrated“personalities” to whichemotional responses areattached.Theconditionsunderwhich

rare events are ignored oroverweighted are betterunderstood now than theywere when prospect theorywas formulated. Theprobability of a rare event

will (often, not always) beoverestimated,becauseoftheconfirmatorybiasofmemory.Thinking about that event,youtrytomakeittrueinyourmind. A rare event will beoverweightedifitspecificallyattracts attention. Separateattention is effectivelyguaranteed when prospectsare described explicitly(“99%chance towin$1,000,and 1% chance to winnothing”). Obsessive

concerns (the bus inJerusalem),vivid images(theroses), concreterepresentations (1 of 1,000),and explicit reminders (as inchoice from description) allcontribute to overweighting.And when there is nooverweighting, there will beneglect. When it comes torareprobabilities,ourmindisnot designed to get thingsquite right. For the residentsof a planet that may be

exposedtoeventsnoonehasyet experienced, this is notgoodnews.

SpeakingofRareEvents

“Tsunamis are very rareeven in Japan, but theimage is so vivid andcompelling that touristsare bound tooverestimate theirprobability.”

“It’sthefamiliardisastercycle. Begin byexaggeration andoverweighting, thenneglectsetsin.”

“We shouldn’t focus ona single scenario, or wewill overestimate itsprobability. Let’s set upspecific alternatives andmake the probabilities

addupto100%.”

“Theywantpeopletobeworried by the risk.That’swhytheydescribeit as 1 death per 1,000.They’re counting ondenominatorneglect.”

P

RiskPolicies

Imagine that you face thefollowing pair of concurrentdecisions.First examinebothdecisions, then make yourchoices.

Decision (i): Choosebetween

A.suregainof$240

B. 25% chance to gain$1,000 and 75% chance togainnothing

Decision (ii): Choosebetween

C.surelossof$750D. 75% chance to lose

$1,000 and 25% chance tolosenothingThis pair of choice problems

hasan importantplace in thehistory of prospect theory,and it has new things to tellus about rationality. As youskimmed the two problems,your initial reaction to thesure things (A and C) wasattraction to the first andaversion to the second. Theemotionalevaluationof“suregain” and “sure loss” is anautomatic reaction of System1, which certainly occursbeforethemoreeffortful(and

optional) computation of theexpected values of the twogambles (respectively, a gainof $250 and a loss of $750).Most people’s choicescorrespond to thepredilections of System 1,and largemajorities preferAtoBandDtoC.Asinmanyother choices that involvemoderate or highprobabilities, people tend tobe risk averse in the domainof gains and risk seeking in

the domain of losses. In theoriginal experiment thatAmosand Icarriedout,73%of respondents chose A indecisioniandDindecisioniiand only 3% favored thecombinationofBandC.Youwereaskedtoexamine

both options before makingyour first choice, and youprobably did so. But onething you surely did not do:you did not compute thepossible results of the four

combinations of choices (AandC,AandD,BandC,Band D) to determine whichcombination you like best.Yourseparatepreferencesforthe two problems wereintuitively compelling andtherewasnoreasontoexpectthat they could lead totrouble. Furthermore,combining the two decisionproblems is a laboriousexercise thatyouwouldneedpaperandpenciltocomplete.

You did not do it. Nowconsiderthefollowingchoiceproblem:

AD.25%chance towin$240and75%chancetolose$760BC. 25% chance towin$250and75%chancetolose$750

This choice is easy! OptionBCactuallydominatesoptionAD (the technical term for

one option beingunequivocally better thananother). You already knowwhat comes next. ThedominantoptioninADisthecombination of the tworejected options in the firstpairofdecisionproblems,theone that only 3% ofrespondents favored in ouroriginal study. The inferioroption BC was preferred by73%ofrespondents.

BroadorNarrow?Thissetofchoiceshasalottotell us about the limits ofhuman rationality. For onething, it helps us see thelogicalconsistencyofHumanpreferences for what it is—ahopeless mirage. Haveanother look at the lastproblem,theeasyone.Wouldyou have imagined thepossibility of decomposingthis obvious choice problem

into a pair of problems thatwouldleadalargemajorityofpeople to choose an inferioroption?Thisisgenerallytrue:every simple choiceformulated in terms of gainsand losses can bedeconstructed in innumerableways into a combination ofchoices, yielding preferencesthat are likely to beinconsistent.The example also shows

that it is costly to be risk

averse for gains and riskseeking for losses. Theseattitudesmakeyouwilling topay a premium to obtain asure gain rather than face agamble, and also willing topay a premium (in expectedvalue) to avoid a sure loss.Both payments come out ofthe same pocket, and whenyou face both kinds ofproblems at once, thediscrepant attitudes areunlikelytobeoptimal.

ThereweretwBghthecomeoo ways of construingdecisionsiandii:

narrow framing: asequence of two simpledecisions, consideredseparatelybroad framing: a singlecomprehensive decision,withfouroptions

Broadframingwasobviouslysuperior in this case. Indeed,itwillbesuperior(orat leastnot inferior) in every case inwhichseveraldecisionsaretobe contemplated together.Imagine a longer list of 5simple (binary) decisions tobeconsideredsimultaneously.The broad (comprehensive)frame consists of a singlechoice with 32 options.Narrow framing will yield asequenceof5simplechoices.

The sequence of 5 choiceswillbeoneof the32optionsofthebroadframe.Willitbethe best? Perhaps, but notvery likely. A rational agentwill of course engage inbroad framing, but Humansarebynaturenarrowframers.The ideal of logical

consistency, as this exampleshows, is not achievable byourlimitedmind.Becausewearesusceptible toWYSIATIand averse to mental effort,

wetendtomakedecisionsasproblemsarise,evenwhenweare specifically instructed toconsider them jointly. Wehave neither the inclinationnor the mental resources toenforce consistency on ourpreferences, and ourpreferencesarenotmagicallysettobecoherent,astheyareintherational-agentmodel.

Samuelson’sProblem

ThegreatPaulSamuelson—agiant among the economistsof the twentieth century—famously asked a friendwhether he would accept agamble on the toss of a coinin which he could lose $100or win $200. His friendresponded, “I won’t betbecauseIwouldfeelthe$100lossmorethanthe$200gain.But I’ll take you on if youpromise to let me make 100such bets.” Unless you are a

decision theorist, youprobably share the intuitionof Samuelson’s friend, thatplaying a very favorable butrisky gamble multiple timesreduces the subjective risk.Samuelson foundhis friend’sanswer interesting and wenton to analyze it. He provedthatundersomeveryspecificconditions, a utilitymaximizer who rejects asingle gamble should alsorejecttheofferofmany.

Remarkably,Samuelsondidnotseemtomindthefactthathis proof,which is of coursevalid,ledtoaconclusionthatviolatescommonsense,ifnotrationality: the offer of ahundred gambles is soattractive thatnosanepersonwould reject it. MatthewRabin and Richard Thalerpointed out that “theaggregated gamble of onehundred 50–50 lose$100/gain $200 bets has an

expected return of $5,000,withonlya1/2,300chanceoflosinganymoneyandmerelya 1/62,000 chance of losingmore than $1,000.” Theirpoint, of course, is that ifutility theory can beconsistentwithsuchafoolishpreference under anycircumstances, thensomething must be wrongwith itasamodelof rationalchoice. Samuelson had notseen Rabin’s proof of the

absurd consequences ofseverelossaversionforsmallbets,buthewouldsurelynothavebeensurprisedbyit.Hiswillingness even to considerthepossibilitythatitcouldberational to reject the packagetestifies to the powerful holdoftherationalmodel.Let us assume that a very

simple value functiondescribes the preferences ofSamuelson’s friend (call himSam).Toexpresshisaversion

to losses Sam first rewritesthebet,aftermultiplyingeachlossbyafactorof2.Hethencomputes the expected valueof the rewrittenbet.Herearethe results, for one, two, orthree tosses. They aresufficiently instructive todeservesomeBghticiof2

You can see in the display

that the gamble has anexpected value of 50.However, one toss is worthnothing to Sam because hefeels that thepainof losingadollar is twice as intense asthe pleasure of winning adollar. After rewriting thegamble to reflect his lossaversion, Sam will find thatthevalueofthegambleis0.Now consider two tosses.

The chances of losing havegone down to 25%. The two

extreme outcomes (lose 200or win 400) cancel out invalue;theyareequallylikely,and the losses are weightedtwice as much as the gain.Buttheintermediateoutcome(one loss, one gain) ispositive, and so is thecompound gamble as awhole. Now you can see thecost of narrow framing andthe magic of aggregatinggambles. Here are twofavorable gambles, which

individually are worthnothing to Sam. If heencounters the offer on twoseparate occasions, he willturn it down both times.However, if he bundles thetwo offers together, they arejointlyworth$50!Thingsgetevenbetterwhen

three gambles are bundled.The extreme outcomes stillcancel out, but they havebecome less significant. Thethird toss,althoughworthless

if evaluated on its own, hasadded $62.50 to the totalvalue of the package.By thetime Sam is offered fivegambles, the expected valueof theofferwillbe$250,hisprobabilityoflosinganythingwill be18.75%, andhis cashequivalent will be $203.125.The notable aspect of thisstory is that Sam neverwavers in his aversion tolosses. However, theaggregation of favorable

gambles rapidly reduces theprobability of losing, and theimpactoflossaversiononhispreferences diminishesaccordingly.NowIhaveasermonready

forSamifherejectstheofferof a single highly favorablegamble played once, and foryou if you share hisunreasonable aversion tolosses:

I sympathize with your

aversion to losing anygamble, but it is costingyou a lot of money.Please consider thisquestion: Are you onyour deathbed? Is thisthe last offer of a smallfavorable gamble thatyou will ever consider?Of course, you areunlikely to be offeredexactly this gambleagain,butyouwillhavemany opportunities to

consider attractivegambleswithstakes thatareverysmallrelativetoyourwealth.Youwilldoyourselfalargefinancialfavor if you are able tosee each of thesegambles as part of abundleofsmallgamblesand rehearse the mantrathat will get yousignificantly closer toeconomic rationality:youwina few,you lose

afew.Themainpurposeof the mantra is tocontrol your emotionalresponse when you dolose.Ifyoucantrustittobe effective, you shouldremind yourself of itwhen deciding whetheror not to accept a smallrisk with positiveexpected value.Remember thesequalifications whenusingthemantra:

It works when thegambles are genuinelyindependent of eachother; it does not applyto multiple investmentsin the same industry,which would all go badtogether.It works only when thepossible loss does notcause you to worry

about your total wealth.If you would take theloss as significant badnews about youreconomic future, watchit!It should not be appliedto long shots,where theprobabilityofwinningisverysmallforeachbet.

If you have the

emotionaldiscipline thatthis rule requires,Bght ld for e you will neverconsider a small gamblein isolation or be lossaverse for a smallgamble until you areactually on yourdeathbed—and not eventhen.

This advice is not

impossible to follow.Experienced traders in

financial markets live by itevery day, shieldingthemselves from the pain oflosses by broad framing. Aswas mentioned earlier, wenow know that experimentalsubjects could be almostcured of their loss aversion(in a particular context) byinducingthemto“thinklikeatrader,” just as experiencedbaseball card traders are notas susceptible to theendowment effect as novices

are. Students made riskydecisions (to accept or rejectgambles inwhich they couldlose) under differentinstructions. In the narrow-framing condition, theyweretold to “make each decisionasifitweretheonlyone”andtoaccept their emotions.Theinstructionsforbroadframingof a decision included thephrases “imagine yourself asatrader,”“youdothisall thetime,” and “treat it as one of

many monetary decisions,which will sum together toproduce a ‘portfolio.’” Theexperimenters assessed thesubjects’ emotional responseto gains and losses byphysiological measures,including changes in theelectrical conductance of theskin that are used in liedetection.Asexpected,broadframing blunted theemotional reaction to lossesand increased thewillingness

totakerisks.The combination of loss

aversion and narrow framingis a costly curse. Individualinvestorscanavoidthatcurse,achieving the emotionalbenefits of broad framingwhile also saving time andagony, by reducing thefrequency with which theycheck how well theirinvestments are doing.Closely following dailyfluctuations is a losing

proposition, because the painof the frequent small lossesexceeds the pleasure of theequally frequent small gains.Onceaquarterisenough,andmaybemorethanenoughforindividual investors. Inaddition to improving theemotional quality of life, thedeliberate avoidance ofexposure to short-termoutcomes improves thequality of both decisions andoutcomes. The typical short-

term reaction to bad news isincreased loss aversion.Investorswhoget aggregatedfeedback receive such newsmuchlessoftenandarelikelyto be less risk averse and toend up richer. You are alsolesspronetouselesschurningofyourportfolioifyoudon’tknowhoweverystockinitisdoing every day (or everyweek or even every month).Acommitmentnot tochangeone’s position for several

periods (the equivalent of“locking in” an investment)improves financialperformance.

RiskPoliciesDecision makers who areprone to narrow framingconstruct a preference everytimetheyfaceariskychoice.They would do better byhavingariskpolicy that theyroutinely apply whenever a

relevant problem arises.Familiar examples of riskpolicies are “always take thehighest possible deductiblewhen purchasing insurance”and “never buy extendedwarranties.”Ariskpolicyisabroadframe.Intheinsuranceexamples, you expect theoccasional loss of the entiredeductible, or the occasionalfailure of an uninsuredproduct.Therelevantissueisyour ability to reduce or

eliminate the pain of theoccasionallossbythethoughtthat the policy that left youexposed to it will almostcertainly be financiallyadvantageous over the longrun.Ariskpolicythataggregates

decisions is analogous to theoutside view of planningproblems that I discussedearlier.Theoutsideviewshiftsthefocusfromthespecificsof the current situation to

Bght pecicy tthe statistics ofoutcomes in similarsituations. The outside viewis abroad frame for thinkingaboutplans.Ariskpolicyisabroad frame that embeds aparticularriskychoiceinasetofsimilarchoices.The outside view and the

risk policy are remediesagainst two distinct biasesthat affect many decisions:the exaggerated optimism ofthe planning fallacy and the

exaggerated caution inducedby loss aversion. The twobiases oppose each other.Exaggerated optimismprotects individuals andorganizations from theparalyzing effects of lossaversion; loss aversionprotectsthemfromthefolliesof overconfident optimism.The upshot is rathercomfortable for the decisionmaker.Optimistsbelieve thatthe decisions they make are

moreprudentthantheyreallyare, and loss-averse decisionmakers correctly rejectmarginal propositions thatthey might otherwise accept.There is no guarantee, ofcourse, that thebiases cancelout in every situation. Anorganization that couldeliminate both excessiveoptimism and excessive lossaversion should do so. Thecombination of the outsideviewwithariskpolicyshould

bethegoal.Richard Thaler tells of a

discussion about decisionmaking he had with the topmanagers of the 25 divisionsofalargecompany.Heaskedthem to consider a riskyoption in which, with equalprobabilities, they could losea large amount of the capitaltheycontrolledorearndoublethat amount. None of theexecutiveswaswillingtotakesuch a dangerous gamble.

Thaler then turned to theCEO of the company, whowas also present, and askedfor his opinion. Withouthesitation, the CEOanswered,“Iwouldlikeallofthemtoaccepttheirrisks.”Inthe context of thatconversation, it was naturalfortheCEOtoadoptabroadframe that encompassed all25bets.LikeSamfacing100cointosses,hecouldcountonstatistical aggregation to

mitigatetheoverallrisk.

SpeakingofRiskPolicies

“Tell her to think like atrader! You win a few,youloseafew.”

“I decided to evaluatemyportfolioonlyonceaquarter. I am too lossaverse to make sensible

decisions in the face ofdailypricefluctuations.”

“They never buyextended warranties.That’stheirriskpolicy.”

“Each of our executivesis loss averse in his orher domain. That’sperfectlynatural,buttheresult is that theorganization is not

takingenoughrisk.”

P

KeepingScore

Except for the very poor, forwhom incomecoincideswithsurvival, themainmotivatorsof money-seeking are notnecessarilyeconomic.Forthebillionaire looking for theextra billion, and indeed forthe participant in anexperimental economicsproject looking for the extra

dollar, money is a proxy forpoints on a scale of self-regard and achievement.These rewards andpunishments, promises andthreats, are all in our heads.We carefully keep score ofthem.They shape oCTh5urpreferences andmotivate ouractions, like the incentivesprovided in the socialenvironment.As a result, werefuse to cut losses whendoingsowouldadmitfailure,

wearebiasedagainst actionsthat could lead to regret, andwedrawanillusorybutsharpdistinction between omissionand commission, not doingand doing, because the senseofresponsibilityisgreaterforone than for the other. Theultimate currency thatrewards or punishes is oftenemotional, a form of mentalself-dealing that inevitablycreates conflicts of interestwhentheindividualactsasan

agent on behalf of anorganization.

MentalAccountsRichard Thaler has beenfascinated formany years byanalogies between the worldofaccountingand thementalaccounts that we use toorganize and run our lives,with results that aresometimes foolish andsometimes very helpful.

Mental accounts come inseveralvarieties.Weholdourmoney in different accounts,which are sometimesphysical, sometimes onlymental. We have spendingmoney, general savings,earmarked savings for ourchildren’s education or formedical emergencies. Thereis a clear hierarchy in ourwillingness to draw on theseaccounts to cover currentneeds. We use accounts for

self-control purposes, as inmaking a household budget,limiting the dailyconsumption of espressos, orincreasing the time spentexercising. Often we pay forself-control, for instancesimultaneously puttingmoney in a savings accountand maintaining debt oncreditcards.TheEconsoftherational-agent model do notresort to mental accounting:they have a comprehensive

view of outcomes and aredrivenbyexternalincentives.ForHumans,mentalaccountsareaformofnarrowframing;they keep things undercontrol andmanageable by afinitemind.Mental accounts are used

extensively to keep score.Recall that professionalgolfersputtmoresuccessfullywhen working to avoid abogey than to achieve abirdie. One conclusion we

can draw is that the bestgolfers create a separateaccountforeachhole;theydonot only maintain a singleaccount for their overallsuccess. An ironic examplethatThalerrelatedinanearlyarticleremainsoneofthebestillustrations of how mentalaccountingaffectsbehavior:

Two avid sports fansplantotravel40milestosee a basketball game.

Oneofthempaidforhisticket; the other was onhis way to purchase aticket when he got onefree from a friend. Ablizzard is announcedfor the night of thegame.Whichof the twoticket holders is morelikely to brave theblizzard to see thegame?

Theansweris immediate:we

know that the fan who paidforhisticketismorelikelytodrive. Mental accountingprovides theexplanation.Weassume that both fans set upanaccount for thegame theyhoped to see. Missing thegamewill close the accountswith a negative balance.Regardlessofhowtheycameby their ticket, both will bedisappointed—buttheclosingbalance is distinctly morenegative for the one who

boughtaticketandisnowoutofpocketaswellasdeprivedof thegame.Becausestayinghome is worse for thisindividual, he is moremotivated to see the gameand therefore more likely tomaketheattempttodriveintoa blizzard. These are tacitcalculations of emotionalbalance, of the kind thatSystem 1 performs withoutdeliberation. The emotionsthatpeopleattachtothestate

of their mental accounts arenotacknowledgedinstandardeconomic theory. An Econwould realize that the tickethasalreadybeenpaidforandcannotbereturned.Itscostis“sunk” and the Econ wouldnot care whether he hadbought theticket tothegameorgotitfromafriend(ifEcoBTh5motketnshavefriends).To implement this rationalbehavior, System 2 wouldhave to be aware of the

counterfactual possibility:“Would I still drive into thissnowstormifIhadgottentheticket free from a friend?” Ittakes an active anddisciplinedmindtoraisesuchadifficultquestion.A related mistake afflicts

individual investors whenthey sell stocks from theirportfolio:

You need money tocover the costs of your

daughter’s wedding andwill have to sell somestock. You rememberthe price at which youbought each stock andcan identify it as a“winner,” currentlyworth more than youpaidfor it,orasa loser.Among the stocks youown,BlueberryTilesisawinner; if you sell ittoday you will haveachieved a gain of

$5,000. You hold anequal investment inTiffanyMotors,whichiscurrently worth $5,000lessthanyoupaidforit.Thevalueofbothstockshasbeenstableinrecentweeks. Which are youmorelikelytosell?

Aplausibleway to formulatethe choice is this: “I couldclose the Blueberry Tilesaccount and score a success

formy record as an investor.Alternatively, I could closethe Tiffany Motors accountand add a failure to myrecord.Whichwould I ratherdo?”Iftheproblemisframedas a choice between givingyourselfpleasureandcausingyourself pain, you willcertainly sell Blueberry Tilesand enjoy your investmentprowess. As might beexpected, finance researchhas documented a massive

preferenceforsellingwinnersratherthanlosers—abiasthathas been given an opaquelabel:thedispositioneffect.The disposition effect is an

instance of narrow framing.The investor has set up anaccount for each share thatshebought, and shewants tocloseeveryaccountasagain.Arationalagentwouldhaveacomprehensive view of theportfolio and sell the stockthat is least likely to dowell

in the future, withoutconsidering whether it is awinner or a loser.Amos toldme of a conversation with afinancial adviser, who askedhimforacompletelistofthestocks in his portfolio,including the price at whicheach had been purchased.When Amos asked mildly,“Isn’t it supposed not tomatter?” the adviser lookedastonished.Hehadapparentlyalwaysbelievedthat thestate

of the mental account was avalidconsideration.Amos’s guess about the

financialadviser’sbeliefswasprobably right, but he waswrong to dismiss the buyingprice as irrelevant. Thepurchase price does matterand should be considered,even by Econs. Thedisposition effect is a costlybias because the question ofwhether to sell winners orlosershasaclearanswer,and

it is not that it makes nodifference. If you care aboutyour wealth rather than yourimmediateemotions,youwillsell the loser TiffanyMotorsand hang on to the winningBlueberry Tiles. At least inthe United States, taxesprovide a strong incentive:realizing losses reduces yourtaxes, while selling winnersexposes you to taxes. Thiselementary fact of financiallife is actually known to all

American investors, and itdetermines thedecisions theymakeduringonemonthoftheyear—investors sell morelosers in December, whentaxes are on their mind. Thetax advantage is available allyear, of course, but for 11months of the year mentalaccounting prevails overfinancial common sense.Another argument againstselling winners is the well-documented market anomaly

that stocks that recentlygained in value are likely togo on gaining at least for ashortwhile.The net effect islarge: the expected after-taxextrareturnofsellingTiffanyratherthanBlueberryis3.4%over the next year. Cl BTh5inge liosing a mentalaccount with a gain is apleasure, but it is a pleasureyou pay for. The mistake isnot one that an Econ wouldever make, and experienced

investors,whoareusingtheirSystem2,arelesssusceptibletoitthanarenovices.Arationaldecisionmakeris

interested only in the futureconsequences of currentinvestments.Justifyingearliermistakes is not among theEcon’s concerns. Thedecision to invest additionalresourcesinalosingaccount,when better investments areavailable, is known as thesunk-cost fallacy, a costly

mistake that is observed indecisions large and small.Driving into the blizzardbecauseonepaidforticketsisasunk-costerror.Imagineacompanythathas

already spent $50million ona project.The project is nowbehind schedule and theforecasts of its ultimatereturnsarelessfavorablethanat the initial planning stage.An additional investment of$60 million is required to

givetheprojectachance.Analternative proposal is toinvest the same amount in anew project that currentlylooks likely to bring higherreturns. What will thecompanydo?All too often acompany afflicted by sunkcostsdrives into theblizzard,throwing good money afterbad rather than accepting thehumiliation of closing theaccount of a costly failure.This situation is in the top-

right cell of the fourfoldpattern, where the choice isbetween a sure loss and anunfavorablegamble,whichisoftenunwiselypreferred.The escalation of

commitment to failingendeavors is a mistake fromtheperspectiveofthefirmbutnot necessarily from theperspective of the executivewho “owns” a flounderingproject.Cancelingtheprojectwill leave a permanent stain

ontheexecutive’srecord,andhis personal interests areperhaps best served bygambling further with theorganization’s resources inthe hope of recouping theoriginal investment—or atleast in an attempt topostpone the day ofreckoning. In thepresenceofsunk costs, the manager’sincentives are misalignedwiththeobjectivesofthefirmand its shareholders, a

familiar type of what isknown as the agencyproblem. Boards of directorsare well aware of theseconflicts and often replace aCEO who is encumbered byprior decisions and reluctanttocutlosses.Themembersofthe board do not necessarilybelieve that the new CEO ismorecompetent than theoneshe replaces. They do knowthat she does not carry thesame mental accounts and is

thereforebetterabletoignorethe sunk costs of pastinvestments in evaluatingcurrentopportunities.The sunk-cost fallacykeeps

people for too long in poorjobs,unhappymarriages, andunpromising researchprojects. I have oftenobserved young scientistsstruggling to salvage adoomed project when theywould be better advised todrop it and start a new one.

Fortunately,researchsuggeststhatat least in somecontextsthe fallacy can be overcome.The sunk-cost fallacy isidentified and taught as amistake in both economicsand business courses,apparently to good effect:there is evidence thatgraduate students in thesefields are more willing thanothers to walk away from afailingproject.

RegretRegretisanemotion,anditisalso a punishment that weadminister to ourselves. Thefear of regret is a factor inmany of the decisions thatpeoplemake (“Don’tdo this,you will regret it” is acommon warning), and theactual experience of regret isfamiliar. The emotional statehas been well described bytwo Dutch psychologists,

who noted that regret is“accompanied by feelingsthat one should have knownbetter, by a BTh5="4ncesinkingfeeling,bythoughts about the mistakeone has made and theopportunities lost, by atendency to kick oneself andto correct one’smistake, andbywantingtoundotheeventand to get a second chance.”Intense regret is what youexperience when you can

most easily imagine yourselfdoing something other thanwhatyoudid.Regret is one of the

counterfactual emotions thatare triggered by theavailability of alternatives toreality. After every planecrashtherearespecialstoriesabout passengers who“shouldnot”havebeenontheplane—they got a seat at thelast moment, they weretransferred from another

airline,theyweresupposedtofly a day earlier but had hadto postpone. The commonfeature of these poignantstories is that they involveunusual events—and unusualeventsareeasier thannormalevents to undo inimagination. Associativememory contains arepresentation of the normalworld and its rules. Anabnormal event attractsattention,anditalsoactivates

the idea of the event thatwould have been normalunder the samecircumstances.To appreciate the link of

regret to normality, considerthefollowingscenario:

Mr.Brownalmostneverpicks up hitchhikers.Yesterdayhegaveamanarideandwasrobbed.

Mr. Smith frequently

picks up hitchhikers.Yesterdayhegaveamanarideandwasrobbed.

Who of the two willexperiencegreaterregretovertheepisode?

Theresultsarenotsurprising:88%of respondents saidMr.Brown,12%saidMr.Smith.Regret is not the same as

blame. Other participantswere asked this question

aboutthesameincident:

Who will be criticizedmostseverelybyothers?

Theresults:Mr.Brown23%,Mr.Smith77%.Regret and blame are both

evokedbya comparison to anorm,but the relevant normsare different. The emotionsexperienced by Mr. BrownandMr.Smitharedominatedbywhattheyusuallydoabout

hitchhikers. Taking ahitchhiker is an abnormalevent for Mr. Brown, andmost people therefore expecthim to experience moreintense regret. A judgmentalobserver, however, willcompare both men toconventional norms ofreasonable behavior and islikelytoblameMr.Smithforhabitually takingunreasonable risks. We aretemptedtosaythatMr.Smith

deservedhisfateandthatMr.Brownwasunlucky.ButMr.Brownistheonewhoismorelikely to be kicking himself,because he acted out ofcharacterinthisoneinstance.Decision makers know that

they are prone to regret, andthe anticipation of thatpainful emotion plays a partin many decisions. Intuitionsabout regret are remarkablyuniform and compelling, asthenextexampleillustrates.

Paul owns shares incompany A. During thepast year he consideredswitching to stock incompany B, but hedecided against it. Henowlearnsthathewouldhave been better off by$1,200 if he hadswitched to the stock ofcompanyB.

George owned shares in

company B. During thepast year he sw B Th5neWho feels greaterregret?

The results are clear-cut: 8%ofrespondentssayPaul,92%sayGeorge.This is curious,because the

situationsofthetwoinvestorsare objectively identical.They both now own stockAand both would have been

betteroffbythesameamountif they owned stock B. TheonlydifferenceisthatGeorgegot towhere he is by acting,whereasPaulgot tothesameplace by failing to act. Thisshort example illustrates abroad story:peopleexpect tohave stronger emotionalreactions(includingregret)toan outcome that is producedby action than to the sameoutcomewhen it is producedby inaction. This has been

verified in the context ofgambling:peopleexpecttobehappier if they gamble andwin than if they refrain fromgambling and get the sameamount.Theasymmetry is atleastasstrongforlosses,anditapplies toblameaswellasto regret. The key is not thedifference betweencommissionandomissionbutthe distinction betweendefault options and actionsthat deviate from thedefault.

When you deviate from thedefault, you can easilyimaginethenorm—andifthedefaultisassociatedwithbadconsequences, thediscrepancy between the twocan be the source of painfulemotions. The default optionwhenyouowna stock isnotto sell it, but the defaultoption when you meet yourcolleagueinthemorningistogreethim.Sellingastockandfailingtogreetyourcoworker

are both departures from thedefault option and naturalcandidates for regret orblame.In a compelling

demonstration of the powerof default options,participants played acomputer simulation ofblackjack.Someplayerswereasked “Do youwish to hit?”while otherswere asked “Doyou wish to stand?”Regardless of the question,

saying yes was associatedwith much more regret thansayingnoiftheoutcomewasbad! The question evidentlysuggests a default response,which is, “I don’t have astrongwishtodoit.”Itisthedeparture from the defaultthatproduces regret.Anothersituation in which action isthe default is that of a coachwhoseteamlostbadlyintheirlast game. The coach isexpectedtomakeachangeof

personnel or strategy, and afailure to do so will produceblameandregret.The asymmetry in the risk

of regret favors conventionaland risk-averse choices. Thebias appears in manycontexts.Consumerswhoarereminded that they may feelregret as a result of theirchoices show an increasedpreference for conventionaloptions, favoring brandnames over generics. The

behavior of the managers offinancial funds as the yearapproachesitsendalsoshowsan effect of anticipatedevaluation:theytendtocleanup their portfolios ofunconventionalandotherwisequestionable stocks. Evenlife-or-deathdecisionscanbeaffected. Imagineaphysicianwithagravelyillpatient.Onetreatment fits the normalstandard of care; another isunusual. The physician has

some reason to believe thatthe unconventional treatmentimproves the patient’schances, but the evidence isinconclusive. The physicianwho prescribes the unusualtreatment faces a substantialrisk of regret, blame, andperhaps litigation. Inhindsight, itwill be easier toimagine the normal choice;the abnormal choice will beeasy to undo. True, a goodoutcomewillcontributetothe

reputation of the physicianwho dared, but the potentialbenefit is smaller than thepotentialcostbecausesuccessis generally a more normaloutcomethanisfailure.

ResponsibBTh5onchepotenilityLosses are weighted abouttwice as much as gains inseveral contexts: choicebetween gambles, the

endowment effect, andreactions to price changes.The loss-aversion coefficientis much higher in somesituations. In particular, youmay be more loss averse foraspects of your life that aremore important than money,such as health. Furthermore,your reluctance to “sell”important endowmentsincreases dramatically whendoing so might make youresponsible for an awful

outcome. Richard Thaler’searly classic on consumerbehavior included acompelling example, slightlymodified in the followingquestion:

You have been exposedto a disease which ifcontracted leads to aquickandpainlessdeathwithin a week. Theprobabilitythatyouhavethe disease is 1/1,000.

Thereisavaccinethatiseffectiveonlybeforeanysymptoms appear. Whatis the maximum youwould bewilling to payforthevaccine?

Most people are willing topay a significant but limitedamount. Facing thepossibility of death isunpleasant, but the risk issmall and it seemsunreasonable to ruin yourself

to avoid it. Now consider aslightvariation:

Volunteers are neededfor research on theabovedisease.Allthatisrequired is that youexpose yourself to a1/1,000 chance ofcontracting the disease.What is the minimumyouwouldasktobepaidinorder tovolunteer forthis program? (You

wouldnotbeallowed topurchasethevaccine.)

Asyoumight expect, the feethat volunteers set is farhigher than the price theywere willing to pay for thevaccine. Thaler reportedinformallythatatypicalratiois about 50:1. The extremelyhighsellingpricereflectstwofeatures of this problem. Inthe first place, you are notsupposed to sell your health;

the transaction is notconsidered legitimateand thereluctance to engage in it isexpressed in a higher price.Perhaps most important, youwill be responsible for theoutcome if it is bad. Youknowthatifyouwakeuponemorning with symptomsindicating that youwill soonbe dead, you will feel moreregretinthesecondcasethaninthefirst,becauseyoucouldhave rejected the idea of

selling your health withoutevenstoppingtoconsidertheprice.Youcouldhave stayedwith the default option anddone nothing, and now thiscounterfactualwillhauntyoufortherestofyourlife.The survey of parents’

reactions to a potentiallyhazardous insecticidementioned earlier alsoincludedaquestionaboutthewillingness to acceptincreased risk. The

respondents were told toimagine that they used aninsecticide where the risk ofinhalation and childpoisoningwas 15 per 10,000bottles. A less expensiveinsecticidewas available, forwhichtheriskrosefrom15to16 per 10,000 bottles. Theparents were asked for thediscount that would inducethem to switch to the lessexpensive (and less safe)product.Morethantwo-thirds

of the parents in the surveyresponded that they wouldnotpurchasethenewproductat any price! They wereevidentlyrevoltedbytheveryidea of trading the safety oftheir child for money. Theminority who found adiscount they could acceptdemanded an amount thatwas significantly higher thantheamounttheywerewillingto pay for a far largerimprovement in the safety of

theproduct.Anyonecanunderstandand

sympathize with thereluctanceofparents to tradeevenaminuteincreaseofrisktotheirchildformoney.It isworth noting, however, thatthisattitudeisincoherentandpotentially damaging to thesafety of t B Th5ry tanceofhose we wish to protect.Even themost lovingparentshave finite resources of timeand money to protect their

child (the keeping-my-child-safe mental account has alimitedbudget), and it seemsreasonable to deploy theseresources in a way that putsthem tobestuse.Money thatcouldbesavedbyacceptingaminuteincreaseintheriskofharm from a pesticide couldcertainly be put to better usein reducing the child’sexposure to other harms,perhapsbypurchasingasafercarseatorcovers forelectric

sockets. The taboo tradeoffagainst accepting anyincrease in risk is not anefficientwaytousethesafetybudget.Infact, theresistancemaybemotivatedbyaselfishfearof regretmore thanbyawish to optimize the child’ssafety. The what-if? thoughtthatoccurstoanyparentwhodeliberately makes such atradeisanimageoftheregretand shame he or she wouldfeelintheeventthepesticide

causedharm.The intense aversion to

trading increased risk forsome other advantage playsout on a grand scale in thelaws and regulationsgoverning risk. This trend isespecially strong in Europe,where the precautionaryprinciple,whichprohibitsanyactionthatmightcauseharm,isawidelyaccepteddoctrine.In the regulatory context, theprecautionary principle

imposes the entire burden ofproving safety on anyonewho undertakes actions thatmight harm people or theenvironment. Multipleinternational bodies havespecified that the absence ofscientific evidence ofpotential damage is notsufficient justification fortaking risks. As the juristCass Sunstein points out, theprecautionary principle iscostly, and when interpreted

strictly it can be paralyzing.He mentions an impressivelistofinnovationsthatwouldnot have passed the test,including “airplanes, airconditioning, antibiotics,automobiles, chlorine, themeasles vaccine, open-heartsurgery, radio, refrigeration,smallpox vaccine, and X-rays.” The strong version ofthe precautionary principle isobviously untenable. Butenhanced loss aversion is

embedded in a strong andwidelysharedmoralintuition;itoriginatesinSystem1.Thedilemma between intenselyloss-averse moral attitudesand efficient riskmanagement does not have asimple and compellingsolution.

We spend much of our dayanticipating, and trying toavoid,theemotionalpainswe

inflict on ourselves. Howseriously should we takethese intangible outcomes,the self-administeredpunishments (and occasionalrewards) that we experienceaswe scoreour lives?Econsare not supposed to havethem, and they are costly toHumans.Theyleadtoactionsthat are detrimental to thewealth of individuals, to thesoundness of policy, and tothewelfareofsociety.Butthe

emotionsof regret andmoralresponsibilityarereal,andthefact that Econs do not havethemmaynotberelevant.Is it reasonable, in

particular, to letyourchoicesbe influenced by theanticipation of regret?Susceptibility to regret, likesusceptibility to faintingspells, is a fact of life towhichonemustadjust.Ifyouare an investor, sufficientlyrichandcautiousatheart,you

may be able to afford theluxury of a portfolio thatminimizes the expectation ofregret even if it does notmaximize the accrual ofwealth.You can also take

precautionsthatwillinoculateyou against regret. Perhapsthe most useful is to beexplicitabouttheanticipationof regret. If you canremember when things gobadlythatyouconsideredthe

possibility of regret carefullybefore deciding, you arelikelytoexperiencelessofit.You should also know thatregretandhindsightbiaswillcome together, so anythingyou can do to precludehindsight is likely to behelpful. My personalhindsight-avoiding B Th5heything policy is to be eithervery thorough or completelycasual when making adecision with long-term

consequences. Hindsight isworsewhenyouthinkalittle,just enough to tell yourselflater,“Ialmostmadeabetterchoice.”Daniel Gilbert and his

colleagues provocativelyclaim that people generallyanticipate more regret thantheywillactuallyexperience,because they underestimatethe efficacy of thepsychological defenses theywill deploy—which they

label the “psychologicalimmune system.” Theirrecommendation is that youshould not put too muchweightonregret;even ifyouhave some, it will hurt lessthanyounowthink.

SpeakingofKeepingScore

“He has separatementalaccounts for cash andcredit purchases. I

constantly remind himthatmoneyismoney.”

“We are hanging on tothat stock just to avoidclosing our mentalaccountataloss.It’sthedispositioneffect.”

“We discovered anexcellent dish at thatrestaurant and we nevertry anything else, to

avoidregret.”

“The salespersonshowed me the mostexpensive car seat andsaid it was the safest,and I could not bringmyself to buy thecheaper model. It feltlikeatabootradeoff.”

P

Reversals

You have the task ofsettingcompensationforvictims of violentcrimes.Youconsiderthecase of a man who lostthe use of his right armas a result of a gunshotwound. He was shotwhenhewalked inona

robbery occurring in aconvenience store in hisneighborhood.

Twostoreswere locatednear the victim’s home,one of which hefrequented moreregularly than the other.Considertwoscenarios:

(i)Theburglaryhappened

intheman’sregularstore.

(ii) The man’s regularstorewasclosedforafuneral,sohedidhis shopping in theother store, where he wasshot.

Should the store inwhich themanwas shotmakeadifference tohiscompensation?

You made your judgment injoint evaluation, where youconsider two scenarios at the

same time and make acomparison.Youcanapplyarule. If you think that thesecond scenario deserveshigher compensation, youshould assign it a higherdollarvalue.There is almost universal

agreement on the answer:compensation should be thesame in both situations. Thecompensation is for thecrippling injury, so whyshould the location in which

it occurred make any diffCmakerence? The jointevaluation of the twoscenarios gave you a chanceto examine your moralprinciples about the factorsthat are relevant to victimcompensation. For mostpeople, locationisnotoneofthese factors. As in othersituations that require anexplicit comparison, thinkingwas slow and System 2 wasinvolved.

The psychologists DaleMiller andCathyMcFarland,who originally designed thetwoscenarios,presentedthemto different people for singleevaluation. In their between-subjects experiment, eachparticipant saw only onescenarioandassignedadollarvalue to it. They found, asyou surely guessed, that thevictim was awarded a muchlargersumifhewasshotinastorehe rarelyvisited than if

he was shot in his regularstore. Poignancy (a closecousin of regret) is acounterfactual feeling, whichisevokedbecausethethought“ifonlyhehadshoppedathisregular store…” comesreadily tomind.The familiarSystem 1 mechanisms ofsubstitution and intensitymatching translate thestrength of the emotionalreaction to the story onto amonetary scale, creating a

large difference in dollarawards.The comparison of the two

experiments reveals a sharpcontrast. Almost everyonewho sees both scenariostogether (within-subject)endorses the principle thatpoignancy is not a legitimateconsideration. Unfortunately,the principle becomesrelevant only when the twoscenarios are seen together,and this is not how life

usually works. We normallyexperience life in thebetween-subjects mode, inwhichcontrastingalternativesthatmight change yourmindare absent, and of courseWYSIATI. As aconsequence, the beliefs thatyouendorsewhenyoureflectabout morality do notnecessarily govern youremotional reactions, and themoral intuitions that come toyour mind in different

situations are not internallyconsistent.The discrepancy between

singleand jointevaluationoftheburglaryscenariobelongstoabroadfamilyofreversalsof judgment and choice. Thefirstpreferencereversalswerediscoveredintheearly1970s,and many reversals of otherkinds were reported over theyears.

Challenging

EconomicsPreference reversals have animportantplaceinthehistoryof the conversation betweenpsychologists andeconomists. The reversalsthat attracted attention werereported by SarahLichtensteinandPaulSlovic,two psychologists who haddone their graduate work attheUniversityofMichiganatthesametimeasAmos.They

conducted an experiment onpreferences between bets,which I show in a slightlysimplifiedversion.

Youareofferedachoicebetweentwobets,whichare to be played on aroulette wheel with 36sectors.Bet A: 11/36 to win$160,25/36tolose$15Bet B: 35/36 to win$40,1/36tolose$10

You are asked to choosebetween a safe bet and ariskierone:analmostcertainwinofamodestamount,orasmall chance to win asubstantially larger amountand a high probability oflosing.Safetyprevails,andBis clearly the more popularchoice.Now consider each bet

separately: Ifyouowned thatbet, what is the lowest price

at which you would sell it?Remember that you are notnegotiating with anyone—your task is to determine thelowest price at which youwouldtrulybewillingtogiveup the bet. Try it. You mayfindthattheprizethatcanbewonisBmaktwearenotsalientin this task, and that yourevaluation of what the bet isworth is anchored on thatvalue.Theresultssupportthisconjecture, and the selling

priceishigherforbetAthanforbetB.Thisisapreferencereversal: people choose Bover A, but if they imagineowning only one of them,they set a higher value onAthanonB.Asintheburglaryscenarios, the preferencereversal occurs because jointevaluation focuses attentionon an aspect of the situation—thefact thatbetAismuchless safe than bet B—whichwas less salient in single

evaluation. The features thatcausedthedifferencebetweenthe judgments of the optionsin single evaluation—thepoignancyofthevictimbeingin the wrong grocery storeand the anchoring on theprize—are suppressed orirrelevant when the optionsare evaluated jointly. Theemotional reactions ofSystem 1 are much morelikely to determine singleevaluation; the comparison

thatoccursinjointevaluationalways involves a morecareful and effortfulassessment, which calls forSystem2.The preference reversal can

be confirmed in a within-subject experiment, in whichsubjects set prices on bothsetsaspartofalonglist,andalso choose between them.Participants are unaware ofthe inconsistency, and theirreactions when confronted

withitcanbeentertaining.A1968 interview of aparticipant in theexperiment,conducted by SarahLichtenstein, is an enduringclassic of the field. Theexperimenter talks at lengthwithabewilderedparticipant,who chooses one bet overanotherbut is thenwilling topay money to exchange theitemhejustchosefortheonehe just rejected, and goesthroughthecyclerepeatedly.

Rational Econs wouldsurely not be susceptible topreference reversals, and thephenomenon was therefore achallenge to the rational-agent model and to theeconomic theory that is builton thismodel.The challengecouldhavebeen ignored, butitwas not.A fewyears afterthe preference reversalswerereported, two respectedeconomists, David Gretherand Charles Plott, published

an article in the prestigiousAmerican Economic Review,in which they reported theirown studies of thephenomenon thatLichtenstein and Slovic haddescribed.Thiswasprobablythe first finding byexperimental psychologiststhat ever attracted theattention of economists. Theintroductory paragraph ofGrether and Plott’s articlewas unusually dramatic for a

scholarly paper, and theirintent was clear: “A body ofdata and theory has beendeveloping withinpsychology which should beof interest to economists.Taken at face value the dataare simply inconsistent withpreference theory and havebroad implications aboutresearch priorities withineconomics…. This paperreports the results of a seriesof experiments designed to

discredit the psychologists’works as applied toeconomics.”Grether and Plott listed

thirteen theories that couldexplain the original findingsand reported carefullydesigned experiments thattested these theories. One oftheir hypotheses, which—needless to say—psychologists foundpatronizing, was that theresults were due to the

experiment being carried outbypsychologists!Eventually,only one hypothesis was leftstanding: the psychologistswere right.Grether and Plottacknowledged that thishypothesis is the leastsatisfactoryfromthepointofview of standard preferencetheory, because “it allowsindividual choice to dependon the context in which thechoices are made”—a clearviolation of the coherence

doctrine.You might think that this

surprising outcome wouldcause much anguished soul-searching among economists,asabasicassumptionoftheirtheory had been successfullychallenged.Butthisisnottheway things work in socialscience, including bothpsycholBmak/p>ished soogyand economics. Theoreticalbeliefsarerobust,andittakesmuch more than one

embarrassing finding forestablished theories to beseriously questioned. In fact,GretherandPlott’sadmirablyforthright report had littledirect effect on theconvictions of economists,probably including Gretherand Plott. It contributed,however, to a greaterwillingnessofthecommunityof economists to takepsychological researchseriously and thereby greatly

advanced the conversationacross the boundaries of thedisciplines.

Categories“HowtallisJohn?”IfJohnis5' tall, your answer willdependonhisage;heisverytall if he is 6 years old, veryshortifheis16.YourSystem1 automatically retrieves therelevant norm, and themeaning of the scale of

tallness is adjustedautomatically. You are alsoable to match intensitiesacross categories and answerthequestion,“Howexpensiveis a restaurant meal thatmatchesJohn’sheight?”YouranswerwilldependonJohn’sage: a much less expensivemeal if he is16 than if he is6.Butnowlookatthis:

Johnis6.Heis5'tall.

Jimis16.Heis5'1"tall.In single evaluations,everyonewillagreethatJohnis very tall and Jim is not,becausetheyarecomparedtodifferent norms. If you areasked a directly comparativequestion, “Is John as tall asJim?”youwillanswerthatheis not. There is no surprisehere and little ambiguity. Inothersituations,however, theprocessbywhichobjectsand

events recruit their owncontext of comparison canlead to incoherentchoicesonseriousmatters.You should not form the

impression that single andjoint evaluations are alwaysinconsistent, or thatjudgments are completelychaotic.Ourworld is brokeninto categories for which wehavenorms,suchassix-year-oldboysortables.Judgmentsand preferences are coherent

within categories butpotentially incoherent whentheobjects that areevaluatedbelongtodifferentcategories.For an example, answer thefollowingthreequestions:

Whichdoyoulikemore,applesorpeaches?Whichdoyoulikemore,steakorstew?Whichdoyoulikemore,applesorsteak?

The first and the secondquestions refer to items thatbelong to the same category,and you know immediatelywhich you like more.Furthermore,youwouldhaverecovered the same rankingfromsingleevaluation(“Howmuch do you like apples?”and “Howmuch do you likepeaches?”) because applesandpeachesbothevokefruit.There will be no preferencereversal because different

fruits are compared to thesame norm and implicitlycompared to each other insingle as well as in jointevaluation. In contrast to thewithin-category questions,there is no stable answer forthecomparisonofapplesandsteak. Unlike apples andpeaches,applesandsteakarenot natural substitutes andtheydonotfillthesameneed.You sometimes want steakand sometimes an apple, but

yourarelysaythateitheronewill do just as well as theother.Imaginereceivingane-mail

fromanorganizationthatyougenerally trust, requesting aBmak

Dolphins in manybreeding locations arethreatened by pollution,which is expected toresult inadeclineof thedolphin population. A

special fund supportedby private contributionshas been set up toprovide pollution-freebreeding locations fordolphins.

What associations did thisquestion evoke? Whether ornot you were fully aware ofthem, ideas andmemories ofrelated causes came to yourmind. Projects intended topreserve endangered species

were especially likely to berecalled. Evaluation on theGOOD–BADdimensionisanautomatic operation ofSystem 1, and you formed acrude impression of therankingofthedolphinamongthespeciesthatcametomind.The dolphin is much morecharming than, say, ferrets,snails, or carp—it has ahighly favorable rank in theset of species to which it isspontaneouslycompared.

The question you mustanswer is not whether youlikedolphinsmorethancarp;youhavebeenaskedtocomeup with a dollar value. Ofcourse, you may know fromthe experience of previoussolicitations that you neverrespond to requests of thiskind. For a few minutes,imagine yourself as someonewho does contribute to suchappeals.Like many other difficult

questions, the assessment ofdollarvaluecanbesolvedbysubstitution and intensitymatching.Thedollarquestionis difficult, but an easierquestion is readily available.Because you like dolphins,you will probably feel thatsaving them is a good cause.The next step, which is alsoautomatic, generates a dollarnumber by translating theintensity of your liking ofdolphins onto a scale of

contributions. You have asense of your scale ofprevious contributions toenvironmental causes, whichmay differ from the scale ofyour contributions to politicsortothefootballteamofyouralma mater. You know whatamount would be a “verylarge” contribution for youand what amounts are“large,” “modest,” and“small.”Youalsohavescalesfor your attitude to species

(from “like very much” to“not at all”). You aretherefore able to translateyour attitude onto the dollarscale, moving automaticallyfrom “like a lot” to “fairlylarge contribution” and fromtheretoanumberofdollars.On another occasion, you

are approached with adifferentappeal:

Farmworkers, who areexposed to the sun for

many hours, have ahigher rate of skincancer than the generalpopulation. Frequentmedical check-ups canreduce the risk. A fundwillbesetuptosupportmedical check-ups forthreatenedgroups.

Is this an urgent problem?Which category did it evokeasanormwhenyouassessedurgency?Ifyouautomatically

categorized the problem as apublic-health issue, youprobablyfoundthatthethreatofskincancerinfarmworkersdoes not rank very highamong these issues—almostcertainly lower than the rankof dolphins amongendangered species. As youtranslated your impression oftherelativeimportanceoftheskincancerissueintoadollaramount,youmightwellhavecome up with a smaller

contribution thanyouofferedto protect an endearinganimal. In experiments, thedolphins attracted somewhatlarger contributions in singleevaluation than did thefarmworkers.Next, consider the two

causes in joint evaluation.Whichofthetwo,dolphinsorfarmworkers, deserves alarger dollar contribution?Joint evaluation highlights afeature that was not

noticeableinsiBmakecksiderthe ngle evaluation but isrecognized as decisive whendetected: farmers are human,dolphins are not. You knewthat,ofcourse,butitwasnotrelevant to the judgment thatyou made in singleevaluation. The fact thatdolphins are not human didnot arise because all theissues that were activated inyour memory shared thatfeature. The fact that

farmworkers are human didnotcometomindbecauseallpublic-health issues involvehumans.The narrow framingof single evaluation alloweddolphins to have a higherintensity score, leading to ahigh rate of contributions byintensity matching. Jointevaluation changes therepresentation of the issues:the “human vs. animal”feature becomes salient onlywhen the two are seen

together. In joint evaluationpeople show a solidpreference for thefarmworkers and awillingness to contributesubstantially more to theirwelfarethantotheprotectionof a likable non-humanspecies.Hereagain,as in thecases of the bets and theburglary shooting, thejudgmentsmadeinsingleandinjointevaluationwillnotbeconsistent.

Christopher Hsee, of theUniversity of Chicago, hascontributed the followingexample of preferencereversal, among many othersofthesametype.Theobjectsto be evaluated aresecondhand musicdictionaries.

DictionaryA

DictionaryB

Yearof 1993 1993

publicationNumberofentries 10,000 20,000

Condition Likenew

Covertorn,otherwiselikenew

When the dictionaries arepresented in singleevaluation, dictionary A isvalued more highly, but ofcoursethepreferencechanges

injointevaluation.TheresultillustratesHsee’sevaluabilityhypothesis: The number ofentries is given noweight insingleevaluation,becausethenumbers are not “evaluable”on their own. In jointevaluation, in contrast, it isimmediately obvious thatdictionary B is superior onthis attribute, and it is alsoapparent that the number ofentries is far more importantthan the condition of the

cover.

UnjustReversalsThere is good reason tobelieve that theadministration of justice isinfected by predictableincoherence in severaldomains. The evidence isdrawn in part fromexperiments, includingstudiesofmockjuries,andinpart from observation of

patterns in legislation,regulation,andlitigation.In one experiment, mock

jurors recruited from juryrolls in Texas were asked toassess punitive damages inseveral civil cases.Thecasescameinpairs,eachconsistingof one claim for physicalinjury and one for financialloss. The mock jurors firstassessedoneof the scenariosandthentheywereshownthecasewithwhichitwasBmak

in,eacpairedandwereaskedto compare the two. Thefollowing are summaries ofonepairofcases:

Case1:Achildsufferedmoderateburnswhenhispajamas caught fire ashe was playing withmatches. The firm thatproduced the pajamashad not made themadequatelyfireresistant.

Case 2: Theunscrupulousdealingsofa bank caused anotherbank a loss of $10million.

Half of the participantsjudged case 1 first (in singleevaluation) before comparingthe two cases in jointevaluation.Thesequencewasreversed for the otherparticipants. In singleevaluation, the jurors

awarded higher punitivedamages to the defraudedbankthantotheburnedchild,presumably because the sizeof thefinancial lossprovidedahighanchor.When the cases were

consideredtogether,however,sympathy for the individualvictim prevailed over theanchoring effect and thejurors increased the award tothechildtosurpasstheawardto the bank. Averaging over

several such pairs of cases,awardstovictimsofpersonalinjury were more than twiceaslargeinjointthaninsingleevaluation. The jurors whosaw the case of the burnedchild on its own made anoffer that matched theintensity of their feelings.Theycouldnotanticipatethatthe award to the childwouldappear inadequate in thecontextof a largeaward to afinancial institution. In joint

evaluation, the punitiveaward to the bank remainedanchored on the loss it hadsustained, but the award tothe burned child increased,reflecting the outrage evokedby negligence that causesinjurytoachild.Aswehaveseen,rationality

isgenerallyservedbybroaderand more comprehensiveframes,andjointevaluationisobviouslybroaderthansingleevaluation. Of course, you

should be wary of jointevaluation when someonewho controls what you seehas a vested interest in whatyou choose. Salespeoplequickly learn thatmanipulationofthecontextinwhich customers see a goodcan profoundly influencepreferences. Except for suchcases of deliberatemanipulation, there is apresumption that thecomparativejudgment,which

necessarily involves System2, ismore likely to be stablethan single evaluations,which often reflect theintensity of emotionalresponses of System 1. Wewould expect that anyinstitutionthatwishestoelicitthoughtful judgments wouldseek to provide the judgeswith a broad context for theassessments of individualcases.Iwassurprisedtolearnfrom Cass Sunstein that

jurors who are to assesspunitive damages areexplicitly prohibited fromconsidering other cases. Thelegal system, contrary topsychologicalcommonsense,favorssingleevaluation.In another study of

incoherence in the legalsystem, Sunstein comparedthe administrativepunishments that can beimposed by different U.S.government agencies

including the OccupationalSafety and HealthAdministration and theEnvironmental ProtectionAgency. He concluded that“within categories, penaltiesseem extremely sensible, atleast in the sense that themore serious harms arepunished more severely. Foroccupational safety andhealth violations, the largestpenalties are for repeatedviolations,thenextlargestfor

violations that are bothwillful and serious, and theleast serious for failures toengage in the requisiterecord-keeping.”Itshouldnotsurprise you, however, thatthe size of penalties variedgreatly across agencies, in amanner that reflected politicsand history more than anyglobal concern for fairness.The fine for a “seriousviolation” of the regulationsconcerning worker safety is

capped at $7,000, while a viBmaknseflected polation ofthe Wild Bird ConservationActcanresult inafineofupto $25,000. The fines aresensible in the context ofother penalties set by eachagency, but they appear oddwhencomparedtoeachother.As in the other examples inthis chapter, you can see theabsurdity only when the twocasesareviewedtogetherinabroad frame. The system of

administrative penalties iscoherent within agencies butincoherentglobally.

SpeakingofReversals

“The BTU units meantnothingtomeuntilIsawhow much air-conditioning units vary.Joint evaluation wasessential.”

“You say this was anoutstanding speechbecauseyoucompareditto her other speeches.Compared toothers, shewasstillinferior.”

“It is often the case thatwhen you broaden theframe, you reach morereasonabledecisions.”

“When you see cases in

isolation, you are likelyto be guided by anemotional reaction ofSystem1.”

P

FramesandReality

ItalyandFrancecompeted inthe 2006 final of the WorldCup.The next two sentencesboth describe the outcome:“Italy won.” “France lost.”Do thosestatementshave thesame meaning? The answerdependsentirelyonwhatyoumeanbymeaning.For the purpose of logical

reasoning, the twodescriptions of the outcomeof the match areinterchangeable because theydesignate the same state ofthe world. As philosopherssay, their truthconditionsareidentical: if one of thesesentences is true, then theother is true as well. This ishowEconsunderstandthings.Their beliefs and preferencesare reality-bound. Inparticular,theobjectsoftheir

choices are states of theworld,whicharenotaffectedby the words chosen todescribethem.There is another sense ofmeaning, in which “Italywon” and “France lost” donothavethesamemeaningatall.Inthissense,themeaningofasentenceiswhathappensinyourassociativemachinerywhile you understand it. Thetwo sentences evokemarkedly different

associations. “Italy won”evokesthoughtsoftheItalianteam andwhat it did towin.“Francelost”evokesthoughtsof theFrench teamandwhatit did that caused it to lose,including the memorableheadbuttofan Italianplayerby theFrenchstarZidane. Intermsoftheassociationstheybring to mind—how System1 reacts to them—the twosentences really “mean”different things.Thefact that

logically equivalentstatements evoke differentreactionsmakes it impossibleforHumans to be as reliablyrationalasEcons.

EmotionalFramingAmosand Iapplied the labelof framing effects to theunjustified influences offormulationonbeliefsanCond preferences. This is one oftheexamplesweused:

Would you accept agamblethatoffersa10%chancetowin$95anda90%chancetolose$5?

Would you pay $5 toparticipate in a lotterythatoffersa10%chanceto win $100 and a 90%chancetowinnothing?

First, take a moment toconvince yourself that thetwoproblemsareidentical.In

bothofthemyoumustdecidewhether to accept anuncertain prospect that willleaveyoueitherricherby$95or poorer by $5. Someonewhosepreferencesarereality-bound would give the sameanswer tobothquestions,butsuch individuals are rare. Infact, one version attractsmanymore positive answers:thesecond.Abadoutcomeismuchmoreacceptable if it isframedasthecostofalottery

ticketthatdidnotwinthanifit is simply described aslosing a gamble. We shouldnot be surprised: lossesevokes stronger negativefeelings than costs. Choicesarenot reality-boundbecauseSystem1isnotreality-bound.Theproblemweconstructed

was influenced by what wehad learned from RichardThaler,whotoldusthatwhenhewas agraduate studenthehad pinned on his board a

card that said costs are notlosses. In his early essay onconsumer behavior, Thalerdescribed the debate aboutwhethergasstationswouldbeallowed to charge differentpricesforpurchasespaidwithcashoroncredit.Thecredit-card lobby pushed hard tomake differential pricingillegal, but it had a fallbackposition: the difference, ifallowed, would be labeled acash discount, not a credit

surcharge. Their psychologywas sound: peoplewillmorereadily forgo a discount thanpayasurcharge.Thetwomaybe economically equivalent,but they are not emotionallyequivalent.In an elegant experiment, a

team of neuroscientists atUniversity College Londoncombined a study of framingeffects with recordings ofactivity in different areas ofthebrain. Inorder toprovide

reliablemeasuresofthebrainresponse, the experimentconsisted of many trials.Figure 14 illustrates the twostagesofoneofthesetrials.First,thesubjectisaskedto

imagine that she received anamount of money, in thisexample£50.Thesubjectisthenaskedto

choose between a sureoutcome and a gamble on awheelofchance.Ifthewheelstopsonwhiteshe“receives”

the entire amount; if it stopson black she gets nothing.The sure outcome is simplythe expected value of thegamble,inthiscaseagainof£20.

Figure14

As shown, the same sure

outcome can be framed in

twodifferentways: asKEEP£20 or as LOSE £30. Theobjective outcomes areprecisely identical in the twoframes, and a reality-boundEcon would respond to bothin the same way—selectingeither the sure thing or thegamble regardless of theframe—butwe already knowthat the Human mind is notbound to reality. Tendenciesto approach or avoid areevokedbythewords,andwe

expectSystem1 tobebiasedin favor of the sure optionwhen it is designated asKEEP and against that sameoption when it is designatedasLOSE.Theexperimentconsistedof

many trials, and eachparticipant encountere Bonp>Theactivityofthebrainwas

recordedasthesubjectsmadeeachdecision.Later,thetrialswere separated into two

categories:

1Trialsonwhichthesubject’s choiceconformedtotheframe

preferred the surething in the KEEPversionpreferred thegamble in theLOSSversion

2Trialsinwhichthe

choice did not conformtotheframe.

The remarkable resultsillustrate the potential of thenew discipline ofneuroeconomics—the studyofwhataperson’sbraindoeswhile he makes decisions.Neuroscientists have runthousands of suchexperiments, and they havelearned to expect particularregions of the brain to “light

up”—indicating increasedflow of oxygen, whichsuggests heightened neuralactivity—depending on thenature of the task. Differentregions are active when theindividual attends to a visualobject, imagines kicking aball, recognizes a face, orthinks of a house. Otherregions light up when theindividual is emotionallyaroused, is in conflict, orconcentrates on solving a

problem. Althoughneuroscientists carefullyavoid the language of “thispart of the brain does suchand such…,” they havelearnedagreatdealaboutthe“personalities” of differentbrain regions, and thecontribution of analyses ofbrain activity topsychological interpretationhas greatly improved. Theframing study yielded threemainfindings:

A region that iscommonly associatedwith emotional arousal(theamygdala)wasmostlikely to be activewhensubjects’ choicesconformed to the frame.This is justaswewouldexpectiftheemotionallyloadedwordsKEEPandLOSE produce animmediate tendency to

approach the sure thing(when it is framed as again)oravoidit(whenitisframedasaloss).Theamygdala is accessedvery rapidly byemotional stimuli—andit is a likely suspect forinvolvement in System1.Abrainregionknowntobe associated withconflict and self-control(the anterior cingulate)

was more active whensubjectsdidnotdowhatcomes naturally—whentheychosethesurethingin spite of its beinglabeledLOSE.ResistingtheinclinationofSystem1 apparently involvesconflict.The most “rational”subjects—those whowere the leastsusceptible to framingeffects—showed

enhanced activity in afrontal area of the brainthat is implicated incombining emotion andreasoning to guidedecisions. Remarkably,the “rational”individuals were notthose who showed thestrongest neuralevidence of conflict. Itappears that these eliteparticipants were (often,not always) reality-

bound with littleconflict.

By joining observations

of actual choices with amapping of neural activity,this study provides a goodillustration of how theemotion evoked by a wordcan “leak” into the finalchoice.An experiment that Amos

carriedoutwithcolleaguesat

Harvard Medical School isthe classic example ofemotional framing. Physicianparticipants were givenstatistics about the outcomesof two treatments for lungcancer:surgeryandradiation.The five-year survival ratesclearly favor surgery, but inthe short term surgery isriskierthanradiation.Halftheparticipants read statisticsabout survival rates, theothers received the same

information in terms ofmortality rates. The twodescriptionsof theshort-termoutcomesofsurgerywere:

The one-month survivalrateis90%.There is 10% mortalityinthefirstmonth.

Youalreadyknowtheresults:surgery was much morepopular in the former frame(84% of physicians chose it)

thaninthelatter(where50%favored radiation). Thelogicalequivalenceofthetwodescriptions is transparent,and a reality-bound decisionmaker wouldmake the samechoice regardless of whichversion she saw. But System1,aswehavegottentoknowit, is rarely indifferent toemotionalwords:mortality isbad, survival is good, and90% survival soundsencouraging whereas 10%

mortality is frightening. Animportantfindingofthestudyisthatphysicianswerejustassusceptible to the framingeffect as medicallyunsophisticated people(hospital patients andgraduate students in abusiness school). Medicaltraining is, evidently, nodefense against the power offraming.TheKEEP–LOSEstudyand

the survival–mortality

experiment differed in oneimportant respect. Theparticipants in the brain-imaging study had manytrials in which theyencountered the differentframes. They had anopportunity to recognize thedistracting effects of theframes and to simplify theirtask by adopting a commonframe, perhaps by translatingthe LOSE amount into itsKEEP equivalent. It would

take an intelligent person(and an alert System 2) tolearn to do this, and the fewparticipantswhomanagedthefeatwereprobablyamongthe“rational” agents that theexperimenters identified. Incontrast, the physicians whoread the statistics about thetwo therapies in the survivalframe had no reason tosuspect that theywould havemade a different choice ifthey had heard the same

statistics framed in terms ofmortality. Reframing iseffortful and System 2 isnormallylazy.Unlessthereisan obvious reason to dootherwise, most of uspassively accept decisionproblems as they are framedand therefore rarely have anopportunity to discover theextent to which ourpreferences are frame-boundratherthanreality-bound.

EmptyIntuitionsAmos and I introduced ourdiscussion of framing by anexample that has becomeknown as the “Asian diseaseproblem”:

Imagine that the UnitedStates is preparing forthe outbreak of anunusual Asian disease,whichisexpectedtokill600 people. Two

alternative programs tocombat the disease havebeen proposed. Assumethat the exact scientificestimates of theconsequences of theprogramsareasfollows:

IfprogramAisadopted,200 people will besaved.

If program B isadopted, there is a

one-thirdprobability that600people will besaved and a two-thirds probabilitythat no people willbesaved.

A substantial majority ofrespondents choose programA: they prefer the certainoptionoverthegamble.The outcomes of the

programs are framed

differently in a secondversion:

IfprogramA'isadopted,400peoplewilldie.IfprogramB'isadopted,there is a one-thirdprobability that nobodywilldieandatwo-thirdsprobability that 600peoplewilldie.

Lookcloselyandcomparethetwo versions: the

consequences of programs Aand A' are identical; so aretheconsequencesofprogramsB and B'. In the secondframe, however, a largemajorityofpeoplechoosethegamble.Thedifferent choices in the

two frames fit prospecttheory, in which choicesbetween gambles and surethings are resolveddifferently, depending onwhether the outcomes are

goodorbad.Decisionmakerstend to prefer the sure thingoverthegamble(theyareriskaverse) when the outcomesaregood.They tend to rejectthe sure thing and accept thegamble (they are riskseeking)whenbothoutcomesare negative. Theseconclusions were wellestablished for choices aboutgambles and sure things inthe domain of money. Thedisease problem shows that

the same rule applies whentheoutcomesaremeasuredinlives saved or lost. In thiscontext, as well, the framingexperiment reveals that risk-averse and risk-seekingpreferences are not reality-bound. Preferences betweenthe same objective outcomesreverse with differentformulations.An experience that Amos

shared with me adds a grimnote to the story. Amos was

invited to give a speech to agroup of public-healthprofessionals—the peoplewho make decisions aboutvaccines andotherprograms.He took the opportunity topresent them with the Asiandiseaseproblem:halfsawthe“lives-saved” version, theothers answered the “lives-lost” question. Like otherpeople, these professionalswere susceptible to theframing effects. It is

somewhat worrying that theofficialswhomake decisionsthat affect everyone’s healthcan be swayed by such asuperficialmanipulation—butwemust get used to the ideathat even importantdecisionsare influenced, if notgoverned,bySystem1.Evenmoretroublingiswhat

happens when people areconfronted with theirinconsistency: “You chose tosave200livesforsureinone

formulationandyouchosetogambleratherthanaccept400deaths in theother.Now thatyouknowthesechoiceswereinconsistent, how do youdecide?” The answer isusually embarrassed silence.TheintuitionsthatdeterminedtheoriginalchoicecamefromSystem 1 and had no moremoral basis than did thepreferenceforkeeping£20orthe aversion to losing £30.Saving liveswithcertainty is

good, deaths are bad. Mostpeople find that their System2 has no moral intuitions ofits own to answer thequestion.I am grateful to the great

economist Thomas Schellingformy favorite exampleof aframing effect, which hedescribed inhisbookChoiceandConsequence.Schelling’sbook was written before ourwork on framing waspublished, and framing was

not his main concern. Hereported on his experienceteaching a class at theKennedy School at Harvard,inwhichBon he linthe topicwas child exemptions in thetax code. Schelling told hisstudents that a standardexemptionisallowedforeachchild, and that the amountoftheexemption is independentof the taxpayer’s income.Heasked their opinion of thefollowingproposition:

Should the childexemption be larger forthe rich than for thepoor?

Your own intuitions are verylikely the same as those ofSchelling’s students: theyfoundtheideaoffavoringtherich by a larger exemptioncompletelyunacceptable.Schelling then pointed out

thatthetaxlawisarbitrary.Itassumesachildlessfamilyas

the default case and reducesthe tax by the amount of theexemptionforeachchild.Thetax law could of course berewrittenwithanotherdefaultcase: a family with twochildren. In this formulation,families with fewer than thedefault number of childrenwould pay a surcharge.Schelling now asked hisstudents to report their viewofanotherproposition:

Should the childlesspoor pay as large asurcharge as thechildlessrich?

Here again you probablyagree with the students’reaction to this idea, whichthey rejected with as muchvehemence as the first. ButSchelling showed his classthat they could not logicallyreject bothproposals.Set thetwoformulationsnexttoeach

other.Thedifferencebetweenthe tax due by a childlessfamily and by a family withtwochildrenisdescribedasareduction of tax in the firstversionandas an increase inthe second. If in the firstversionyouwant thepoor toreceive the same (or greater)benefit as the rich forhavingchildren, thenyoumustwantthe poor to pay at least thesame penalty as the rich forbeingchildless.

WecanrecognizeSystem1at work. It delivers animmediate response to anyquestionaboutrichandpoor:when in doubt, favor thepoor.ThesurprisingaspectofSchelling’s problem is thatthis apparently simple moralruledoesnotworkreliably.Itgenerates contradictoryanswerstothesameproblem,depending on how thatproblem is framed. And ofcourse you already know the

question that comes next.Now that you have seen thatyourreactionstotheproblemare influenced by the frame,what is your answer to thequestion:Howshouldthetaxcode treat the childrenof therichandthepoor?Here again, you will

probably find yourselfdumbfounded. You havemoral intuitions aboutdifferences between the richand the poor, but these

intuitions depend on anarbitrary reference point, andthey are not about the realproblem. This problem—thequestionaboutactualstatesofthe world—is howmuch taxindividual families shouldpay, how to fill the cells inthe matrix of the tax code.You have no compellingmoral intuitions toguideyouinsolvingthatproblem.Yourmoralfeelingsareattachedtoframes, to descriptions of

reality rather than to realityitself.Themessageabout thenature of framing is stark:framingshouldnotbeviewedasan intervention thatmasksor distorts an underlyingpreference. At least in thisinstance—and also in theproblemsoftheAsiandiseaseand of surgery versusradiation for lung cancer—there is no underlyingpreference that is masked ordistorted by the frame. Our

preferences are about framedproblems, and our moralintuitions are aboutdescriptions, not aboutsubstance.

GoodFramesNotall framesareequal, ands Bon nd t="4%" womeframesareclearlybetter thanalternative ways to describe(or to think about) the samething.Consider thefollowing

pairofproblems:

A woman has boughttwo $80 tickets to thetheater. When shearrivesatthetheater,sheopens her wallet anddiscoversthattheticketsare missing. Will shebuy twomore tickets toseetheplay?

A woman goes to thetheater, intending tobuy

twoticketsthatcost$80each. She arrives at thetheater,opensherwallet,and discovers to herdismay that the $160with which she wasgoing to make thepurchaseismissing.Shecouldusehercreditcard.Willshebuythetickets?

Respondents who see onlyone version of this problemreach different conclusions,

dependingontheframe.Mostbelievethatthewomaninthefirst story will go homewithout seeing the show ifshehaslost tickets,andmostbelieve that she will chargeticketsfortheshowifshehaslostmoney.The explanation should

already be familiar—thisproblem involves mentalaccounting and the sunk-costfallacy. The different framesevoke different mental

accounts,andthesignificanceof the loss depends on theaccounttowhichitisposted.When tickets to a particularshow are lost, it is natural topost them to the accountassociatedwiththatplay.Thecost appears to have doubledand may now be more thanthe experience is worth. Incontrast, a loss of cash ischarged to a “generalrevenue” account—thetheater patron is slightly

poorer than she had thoughtshewas,andthequestionsheis likely to ask herself iswhether the small reductionin her disposablewealthwillchange her decision aboutpaying for tickets. Mostrespondents thought it wouldnot.The version in which cash

was lost leads to morereasonable decisions. It is abetterframebecausetheloss,even if tickets were lost, is

“sunk,”andsunkcostsshouldbe ignored. History isirrelevant and the only issuethat matters is the set ofoptionsthetheaterpatronhasnow, and their likelyconsequences. Whatever shelost, the relevant fact is thatshe is less wealthy than shewas before she opened herwallet.Ifthepersonwholosttickets were to ask for myadvice, this is what I wouldsay:“Wouldyouhavebought

tickets if you had lost theequivalentamountofcash?Ifyes, go ahead and buy newones.” Broader frames andinclusive accounts generallylead to more rationaldecisions.In the next example, two

alternative frames evokedifferent mathematicalintuitions, and one is muchsuperior to the other. In anarticle titled “The MPGIllusion,” which appeared in

Science magazine in 2008,the psychologists RichardLarrick and Jack Sollidentified a case in whichpassive acceptance of amisleading frame hassubstantial costs and seriouspolicy consequences. Mostcarbuyerslistgasmileageasone of the factors thatdetermine their choice; theyknow that high-mileage carshave lower operating costs.But the frame that has

traditionallybeenused in theUnited States—miles pergallon—provides very poorguidance to the decisions ofboth individuals and policymakers. Consider two carowners who seek to reducetheircosts:

Adam switches from agas-guzzlerof12mpgtoa slightly less voraciousguzzler that runs at 14mpg.

The environmentallyvirtuous Beth switchesfrom a Bon ss es from30 mpg car to one thatrunsat40mpg.

Suppose both drivers travelequal distances over a year.Who will save more gas byswitching? You almostcertainly share thewidespread intuition thatBeth’s action is more

significant than Adam’s: shereduced mpg by 10 milesrather than 2, and by a third(from30 to40) rather thanasixth (from 12 to 14). Nowengage your System 2 andwork it out. If the two carowners both drive 10,000miles, Adam will reduce hisconsumption from ascandalous 833 gallons to astillshocking714gallons,fora saving of 119 gallons.Beth’s use of fuel will drop

from 333 gallons to 250,saving only 83 gallons. Thempg frame is wrong, and itshould be replaced by thegallons-per-mile frame (orliters-per–100 kilometers,which is used in most othercountries). As Larrick andSollpointout,themisleadingintuitionsfosteredbythempgframe are likely to misleadpolicy makers as well as carbuyers.Under President Obama,

Cass Sunstein served asadministratoroftheOfficeofInformation and RegulatoryAffairs.WithRichardThaler,Sunstein coauthored Nudge,whichisthebasicmanualforapplying behavioraleconomics to policy. It wasno accident that the “fueleconomy and environment”sticker thatwill be displayedon every new car starting in2013willforthefirsttimeintheUnited States include the

gallons-per-mile information.Unfortunately, the correctformulation will be in smallprint, along with the morefamiliar mpg information inlargeprint,butthemoveisinthe right direction. The five-year interval between thepublication of “The MPGIllusion” and theimplementation of a partialcorrectionisprobablyaspeedrecord for a significantapplication of psychological

sciencetopublicpolicy.A directive about organ

donationincaseofaccidentaldeath is noted on anindividual’s driver license inmany countries. Theformulation of that directiveis another case in which oneframe is clearly superior tothe other. Few people wouldargue that the decision ofwhether or not to donateone’s organs is unimportant,but there is strong evidence

that most people make theirchoice thoughtlessly. Theevidence comes from acomparison of the rate oforgan donation in Europeancountries, which revealsstartling differences betweenneighboring and culturallysimilar countries. An articlepublished in 2003 noted thatthe rate of organ donationwascloseto100%inAustriabut only 12% in Germany,86% inSweden but only 4%

inDenmark.Theseenormousdifferences

areaframingeffect,whichiscaused by the format of thecritical question. The high-donation countries have anopt out form, whereindividuals who wish not todonate must check anappropriate box. Unless theytake this simple action, theyare considered willingdonors. The low-contributioncountries have an opt-in

form: you must check a boxto become a donor. That isall. The best single predictorofwhetherornotpeoplewilldonate their organs is thedesignation of the defaultoption that will be adoptedwithout having to check abox.Unlikeotherframingeffects

that have been traced tofeatures of System 1, theorgan donation effect is bestexplained by the laziness of

System 2. People will checkthe box if they have alreadydecidedwhattheywishtodo.Iftheyareunpreparedforthequestion, they have to maketheeffortofthinkingwhethertheywanttocheckthebox.Iimagine an organ donationform in which people arerequired to solve amathematical problem in thebox that corresponds to theirdecision. One of the boxescontains theproblem2+2=

? The problem in the otherboxis13×37=?Therateofdonations would surely beswayed.When the role of

formulationisacknowledged,a policy question arises:Which formulation shouldbeadopted? In this case, theanswer is straightforward. Ifyou believe that a largesupply of donated organs isgoodforsociety,youwillnotbe neutral between a

formulationthatyieldsalmost100% donations and anotherformulation that elicitsdonationsfrom4%ofdrivers.Aswehave seen again and

again, an important choice iscontrolled by an utterlyinconsequentialfeatureofthesituation. This isembarrassing—it is not howwe would wish to makeimportant decisions.Furthermore,itisnothowweexperience the workings of

our mind, but the evidenceforthesecognitiveillusionsisundeniable.Countthatasapointagainst

the rational-agent theory. Atheory that is worthy of thename asserts that certainevents are impossible—theywill not happen if the theoryistrue.Whenan“impossible”event is observed, the theoryis falsified. Theories cansurvive for a long time afterconclusive evidence falsifies

them, and the rational-agentmodel certainly survived theevidence we have seen, andmuchotherevidenceaswell.The case of organ donation

shows that the debate abouthuman rationality can have alargeeffect in therealworld.A significant differencebetween believers in therational-agent model and theskeptics who question it isthat thebelieverssimplytakeit for granted that the

formulation of a choicecannot determine preferencesonsignificantproblems.Theywillnotevenbe interested ininvestigating the problem—andsoweareoften leftwithinferioroutcomes.Skeptics about rationality

are not surprised. They aretrained to be sensitive to thepower of inconsequentialfactors as determinants ofpreference—my hope is thatreaders of this book have

acquiredthissensitivity.

SpeakingofFramesandReality

“They will feel betterabout what happened ifthey manage to framethe outcome in terms ofhow much money theykept rather than howmuchtheylost.”

“Let’s reframe theproblembychangingthereference point. Imaginewe did not own it; howmuchwouldwe think itisworth?”

“Chargethelosstoyourmental account of‘general revenue’—youwillfeelbetter!”

“They ask you to check

the box to opt out oftheir mailing list. Theirlistwould shrink if theyaskedyoutocheckaboxtooptin!”

P

Part5

P

TwoSelvesP

TwoSelves

The termutility has had twodistinct meanings in its longhistory. Jeremy BenthamopenedhisIntroductiontothePrinciples of Morals andLegislation with the famoussentence “Nature has placedmankind under thegovernance of two sovereignmasters,painandpleasure. It

isforthemalonetopointoutwhatweought todo,aswellastodeterminewhatweshalldo.”Inanawkwardfootnote,Bentham apologized forapplying the word utility totheseexperiences,sayingthathe had been unable to find abetter word. To distinguishBentham’s interpretation ofthe term, I will call itexperiencedutility.For the last 100 years,

economists have used the

same word to meansomething else. Aseconomists and decisiontheorists apply the term, itmeans “wantability”—and Ihavecalleditdecisionutility.Expected utility theory, forexample,isentirelyabouttherulesofrationalitythatshouldgovern decision utilities; ithasnothingatalltosayabouthedonic experiences. Ofcourse, the two concepts ofutilitywill coincide ifpeople

want what they will enjoy,andenjoywhattheychoseforthemselves—and thisassumption of coincidence isimplicit in the general ideathat economic agents arerational. Rational agents areexpectedtoknowtheirtastes,both present and future, andthey are supposed to makegood decisions that willmaximizetheseinterests.

ExperiencedUtility

My fascination with thepossible discrepanciesbetween experienced utilityanddecisionutilitygoesbackalongway.WhileAmosandI were still working onprospect theory, I formulateda puzzle, which went likethis: imagine an individualwho receives one painfulinjection every day. There isnoadaptation; thepain is thesamedaytoday.Willpeople

attach the same value toreducing the number ofplannedinjectionsfrom20to18 as from 6 to 4? Is thereany justification for adistinction?I did not collect data,

because the outcome wasevident. You can verify foryourself that you would paymoretoreducethenumberofinjections by a third (from 6to4) thanbyonetenth(from20to18).Thedecisionutility

of avoiding two injections ishigherinthefirstcasethaninthesecond,andeveryonewillpay more for the firstreductionthanforthesecond.But this difference is absurd.If the pain does not changefrom day to day, what couldjustify assigning differentutilities to a reduction of thetotal amount of pain by twoinjections, depending on thenumber of previousinjections? In the terms we

would use today, the puzzleintroduced the idea thatexperienced utility could bemeasured by the number ofinjections. It also suggestedthat, at least in some cases,experienced utility is thecriterionbywhichadecisionshould be assessed. Adecision maker who paysdifferent amounts to achievethesamegainofexperiencedutility(orbesparedthesameloss) is making a mistake.

You may find thisobservation obvious, but indecisiontheorytheonlybasisfor judging that a decision iswrong is inconsistency withotherpreferences.AmosandIdiscussedtheproblembutwedidnotpursueit.Manyyearslater,Ireturnedtoit.

ExperienceandMemory

How can experienced utility

bemeasured?Howshouldweanswer questions such as“How much pain did Helensuffer during the medicalprocedure?” or “How muchenjoyment did she get fromher 20 minutes on thebeach?”TJonet8221;TJheBritish economist FrancisEdgeworth speculated aboutthis topic in the nineteenthcenturyandproposedtheideaof a “hedonimeter,” animaginary instrument

analogoustothedevicesusedinweather-recordingstations,which would measure thelevel of pleasure or pain thatan individual experiences atanymoment.Experienced utility would

vary, much as dailytemperature or barometricpressure do, and the resultswouldbeplottedasafunctionof time. The answer to thequestionofhowmuchpainorpleasure Helen experienced

duringhermedicalprocedureor vacation would be the“areaunder thecurve.”Timeplays a critical role inEdgeworth’s conception. IfHelen stays on the beach for40minutesinsteadof20,andher enjoyment remains asintense, then the totalexperienced utility of thatepisode doubles, just asdoubling the number ofinjections makes a course ofinjections twice as bad. This

wasEdgeworth’s theory, andwe now have a preciseunderstanding of theconditions under which histheoryholds.The graphs in figure 15

show profiles of theexperiences of two patientsundergoing a painfulcolonoscopy, drawn from astudy that Don Redelmeierand I designed together.Redelmeier, a physician andresearcher at the University

of Toronto, carried it out inthe early 1990s. Thisprocedure is now routinelyadministered with ananesthetic as well as anamnesicdrug,butthesedrugswerenotaswidespreadwhenour data were collected. Thepatientswerepromptedevery60 seconds to indicate thelevelofpaintheyexperiencedat the moment. The datashown are on a scale wherezerois“nopainatall”and10

is “intolerable pain.” As youcan see, the experience ofeach patient variedconsiderably during theprocedure, which lasted 8minutes forpatientAand24minutesforpatientB(thelastreading of zero pain wasrecorded after the end of theprocedure). A total of 154patients participated in theexperiment; the shortestprocedure lasted 4 minutes,thelongest69minutes.

Next, consider an easyquestion: Assuming that thetwopatientsusedthescaleofpain similarly, which patientsuffered more? No contest.There is general agreementthat patient B had the worsetime. PatientB spent at leastasmuch time as patientA atany level of pain, and the“area under the curve” isclearly larger for B than forA.Thekey factor, of course,is that B’s procedure lasted

much longer. I will call themeasuresbasedon reportsofmomentary pain hedonimetertotals.

Figure15

When the procedure was

over, all participants wereasked to rate “the totalamount of pain” they hadexperienced during theprocedure. The wording wasintended to encourage themto thinkof the integralof thepain they had reported,reproducing the hedonimetertotals. Surprisingly, the

patients did nothing of thekind. The statistical analysisrevealed two findings,whichillustrate a pattern we haveobserved in otherexperiments:

Peak-end rule: Theglobal retrospectiveratingwaswellpredictedby the average of thelevel of pain reported attheworstmomentofthe

experience and at itsend.Duration neglect: Theduration of theprocedure had no effectwhatsoever on theratingsoftotalpain.

You can now apply theserules to the profiles ofpatients A and B. The worstratiJonersoeveronng(8onthe 10-point scale) was the

same for both patients, butthe last rating before the endof the procedure was 7 forpatient A and only 1 forpatient B. The peak-endaveragewas therefore7.5 forpatient A and only 4.5 forpatient B. As expected,patient A retained a muchworsememoryoftheepisodethanpatientB.Itwasthebadluck of patient A that theprocedure ended at a badmoment, leavinghimwithan

unpleasantmemory.We now have an

embarrassmentof riches: twomeasures of experiencedutility—thehedonimeter totaland the retrospectiveassessment—that aresystematically different. Thehedonimeter totals arecomputed by an observerfromanindividual’sreportofthe experience of moments.We call these judgmentsduration-weighted, because

the computation of the “areaunder the curve” assignsequalweightstoallmoments:twominutesofpainatlevel9istwiceasbadasoneminuteat the same level of pain.However, thefindingsof thisexperiment and others showthat the retrospectiveassessmentsareinsensitivetoduration and weight twosingular moments, the peakandtheend,muchmorethanothers. So which should

matter? What should thephysiciando?Thechoicehasimplications for medicalpractice.Wenotedthat:

If the objective is toreducepatients’memoryof pain, lowering thepeak intensity of paincouldbemoreimportantthan minimizing theduration of theprocedure. By the same

reasoning, gradual reliefmay be preferable toabrupt relief if patientsretain a better memorywhenthepainattheendof the procedure isrelativelymild.If the objective is toreduce the amount ofpain actuallyexperienced, conductingthe procedure swiftlymaybeappropriateevenifdoingso increases the

peak pain intensity andleaves patients with anawfulmemory.

Which of the two objectivesdid you find mostcompelling? I have notconducted a proper survey,but my impression is that astrong majority will comedowninfavorofreducingthememory of pain. I find ithelpful to think of this

dilemma as a conflict ofinterests between two selves(which do not correspond tothe two familiar systems).The experiencing self is theonethatanswersthequestion:“Does it hurt now?” Theremembering self is the onethat answers the question:“Howwas it,on thewhole?”Memories are all we get tokeep from our experience ofliving, and the onlyperspectivethatwecanadopt

aswethinkaboutourlivesistherefore that of therememberingself.A comment I heard from a

memberoftheaudienceaftera lecture illustrates thedifficulty of distinguishingmemories from experiences.Hetoldoflisteningraptlytoalongsymphonyonadiscthatwas scratched near the end,producing a shocking sound,and he reported that the badending “ruined the whole

experience.” But theexperience was not actuallyruined,onlythememoryofit.Theexperiencingselfhadhadanexperiencethatwasalmostentirely good, and the badend could not undo it,because it had alreadyhappened.Myquestionerhadassigned the entire episode afailing grade because it hadended very badly, but thatgrade effectively ignored 40minutes of musical bliss.

Does the actual experiencecountfornothing?Confusing experience with

the memory of it is acompelling cognitive illusion—and it is the substitutionthat makes us believe a pastexperiencecanberuined.Theexperiencing self does nothave a voice. Theremembering self issometimeswrong,butitistheone that keeps score andgoverns what we learn from

living, and it is the one thatmakes decisions Jonthaperienci. What we learnfrom the past is tomaximizethe qualities of our futurememories, not necessarily ofourfutureexperience.Thisisthe tyranny of therememberingself.

WhichSelfShouldCount?

To demonstrate the decision-

making power of theremembering self, mycolleagues and I designed anexperiment, using a mildformoftorturethatIwillcallthe cold-hand situation (itsugly technical name is cold-pressor). Participants areaskedtoholdtheirhanduptothe wrist in painfully coldwateruntiltheyareinvitedtoremove it and are offered awarm towel. The subjects inourexperimentusedtheirfree

hand to control arrows on akeyboard to provide acontinuousrecordofthepainthey were enduring, a directcommunication from theirexperiencingself.Wechoseatemperature that causedmoderate but tolerable pain:the volunteer participantswereofcoursefreetoremovetheir hand at any time, butnonechosetodoso.Each participant endured

twocold-handepisodes:

The short episodeconsisted of 60 secondsofimmersioninwaterat14° Celsius, which isexperienced as painfullycold,butnot intolerable.At the end of the 60seconds, theexperimenter instructedtheparticipanttoremovehis hand from thewaterand offered a warmtowel.

The long episode lasted90 seconds. Its first 60secondswereidenticaltothe short episode. Theexperimenter saidnothing at all at the endof the 60 seconds.Instead he opened avalve that allowedslightlywarmerwater toflowintothetub.Duringthe additional 30seconds,thetemperatureof the water rose by

roughly 1°, just enoughfor most subjects todetect a slight decreaseintheintensityofpain.

Our participants were toldthat they would have threecold-hand trials, but in factthey experienced only theshort and the long episodes,each with a different hand.The trials were separated bysevenminutes.Sevenminutesafter the second trial, the

participants were given achoice about the third trial.They were told that one oftheir experiences would berepeated exactly, and werefree to choose whether torepeat the experience theyhad had with their left handor with their right hand. Ofcourse, half the participantshad the short trial with theleft hand,halfwith the right;half had the short trial first,halfbeganwiththelong,etc.

This was a carefullycontrolledexperiment.The experiment was

designed to create a conflictbetween the interests of theexperiencing and therememberingselves,andalsobetween experienced utilityanddecisionutility.Fromtheperspective of theexperiencing self, the longtrial was obviously worse.We expected theremembering self to have

another opinion. The peak-end rule predicts a worsememoryfortheshortthanforthe long trial, and durationneglect predicts that thedifference between 90seconds and 60 seconds ofpain will be ignored. Wetherefore predicted that theparticipants would have amore favorable (or lessunfavorable) memory of thelongtrialandchoosetorepeatit.Theydid.Fully80%ofthe

participantswhoreportedthattheir pain diminished duringthe final phase of the longerepisode opted to repeat it,thereby declaring themselveswilling to suffer 30 secondsof needless pain in theanticipatedthirdtrial.The subjects who preferred

the long episode were notmasochists and did notdeliberatelychoose toexposethemselves to the worseexperience; they simply Jon

theheigmadeamistake.Ifwehadasked them,“Wouldyouprefera90-secondimmersionor only the first part of it?”they would certainly haveselected the shortoption.Wedid not use these words,however,andthesubjectsdidwhat came naturally: theychosetorepeattheepisodeofwhich they had the lessaversive memory. Thesubjects knew quite wellwhich of the two exposures

was longer—we asked them—but they did not use thatknowledge. Their decisionwas governed by a simplerule of intuitive choice: pickthe option you like themost,or dislike the least. Rules ofmemory determined howmuch they disliked the twooptions, which in turndetermined their choice. Thecold-hand experiment, likemy old injections puzzle,revealed a discrepancy

between decision utility andexperiencedutility.The preferences we

observed in this experimentare another example of theless-is-more effect that wehaveencounteredonpreviousoccasions. One wasChristopher Hsee’s study inwhich adding dishes to a setof24dishesloweredthetotalvalue because some of theadded dishes were broken.Another was Linda, the

activistwomanwhoisjudgedmore likely to be a feministbanktellerthanabankteller.The similarity is notaccidental. The sameoperatingfeatureofSystem1accounts for all threesituations: System 1represents sets by averages,norms,andprototypes,notbysums. Each cold-handepisode is a set ofmoments,which the remembering selfstores as a prototypical

moment. This leads to aconflict. For an objectiveobserver evaluating theepisode from the reports ofthe experiencing self, whatcounts is the “area under thecurve” that integrates painovertime;ithasthenatureofa sum. Thememory that theremembering self keeps, incontrast, is a representativemoment, strongly influencedbythepeakandtheend.Of course, evolution could

have designed animals’memory to store integrals, asit surely does in some cases.It is important for a squirrelto“know”thetotalamountoffood it has stored, and arepresentation of the averagesizeof thenutswouldnotbea good substitute. However,the integral of pain orpleasure over time may beless biologically significant.We know, for example, thatratsshowdurationneglectfor

bothpleasureandpain.Inoneexperiment, rats wereconsistently exposed to asequence in which the onsetof a light signals that anelectric shock will soon bedelivered. The rats quicklylearned to fear the light, andthe intensity of their fearcouldbemeasuredbyseveralphysiological responses. Themain finding was that theduration of the shock haslittleornoeffectonfear—all

that matters is the painfulintensityofthestimulus.Other classic studies

showed that electricalstimulation of specific areasin the rat brain (and ofcorresponding areas in thehuman brain) produce asensationof intensepleasure,so intense in somecases thatrats who can stimulate theirbrainbypressingaleverwilldie of starvation withouttaking a break to feed

themselves. Pleasurableelectric stimulation can bedelivered in bursts that varyin intensity and duration.Here again, only intensitymatters. Up to a point,increasing the duration of aburst of stimulation does notappear to increase theeagerness of the animal toobtain it. The rules thatgovern the remembering selfof humans have a longevolutionaryhistory.

Biologyvs.Rationality

The most useful idea in theinjections puzzle thatpreoccupied me years agowas that the experiencedutility of a series of equallypainful injections can bemeasured,bysimplycountingtheinjections.Ifallinjectionsare equally aversive, then 20of them are twice as bad as10,andJoneoeeareduction

from20to18andareductionfrom 6 to 4 are equallyvaluable. If the decisionutilitydoesnotcorrespondtothe experienced utility, thensomething is wrong with thedecision. The same logicplayed out in the cold-handexperiment: an episode ofpain that lasts 90 seconds isworse than the first 60seconds of that episode. Ifpeople willingly choose toendure the longer episode,

somethingiswrongwiththeirdecision. Inmy early puzzle,the discrepancy between thedecision and the experienceoriginated from diminishingsensitivity: the differencebetween 18 and 20 is lessimpressive,andappearstobeworthless,thanthedifferencebetween6and4injections.Inthecold-handexperiment,theerror reflects two principlesof memory: duration neglectand the peak-end rule. The

mechanisms are different butthe outcome is the same: adecision that is not correctlyattunedtotheexperience.Decisions that do not

produce the best possibleexperience and erroneousforecasts of future feelings—both are bad news forbelievers in the rationality ofchoice. The cold-hand studyshowed that we cannot fullytrustourpreferencestoreflectourinterests,evenif theyare

basedonpersonalexperience,and even if the memory ofthat experience was laiddown within the last quarterof an hour! Tastes anddecisions are shaped bymemories, and the memoriescan be wrong. The evidencepresentsaprofoundchallengeto the idea that humans haveconsistent preferences andknowhowtomaximizethem,a cornerstone of the rational-agent model. An

inconsistencyisbuilt intothedesignofourminds.Wehavestrong preferences about thedurationofourexperiencesofpain and pleasure. We wantpain to be brief and pleasureto last. But our memory, afunction of System 1, hasevolvedtorepresentthemostintensemomentofanepisodeofpainorpleasure(thepeak)and the feelings when theepisode was at its end. Amemory that neglects

duration will not serve ourpreference for long pleasureandshortpains.

SpeakingofTwoSelves

“You are thinking ofyour failed marriageentirely from theperspective of theremembering self. Adivorce is like asymphony with a

screeching sound at theend—the fact that itended badly does notmeanitwasallbad.”

“This is a bad case ofduration neglect. Youare giving the good andthe bad part of yourexperienceequalweight,although the good partlasted ten times as longastheother.”

P

LifeasaStory

Earlyinthedaysofmyworkon the measurement ofexperience, I saw Verdi’soperaLaTraviata.Knownforitsgorgeousmusic,itisalsoamoving story of the lovebetween a young aristocratandVioletta,awomanof thedemimonde. The youngman’s father approaches

Violettaandconvinceshertogive up her lover, to protectthe honor of the family andthemarriageprospectsof theyoungman’s sister. In an actof supreme self-sacrifice,Violettapretendstorejecttheman she adores. She soonrelapses into consumption(the nineteenth-century termfor tuberculosis). In the finalact, Violetta lies dying,surrounded by a few friends.Herbelovedhasbeenalerted

and is rushing toParis to seeher. H Kto earing the news,she is transformedwith hopeand joy, but she is alsodeterioratingquickly.Nomatter howmany times

youhaveseentheopera,youaregrippedbythetensionandfear of themoment:Will theyoung lover arrive in time?There is a sense that it isimmensely important forhimtojoinhisbelovedbeforeshedies. He does, of course,

some marvelous love duetsaresung,andafter10minutesof glorious music Violettadies.Onmyway home from the

opera, I wondered: Why dowecare somuchabout thoselast 10 minutes? I quicklyrealized that Ididnotcareatall about the length ofVioletta’s life. If I had beentold that she died at age 27,not age 28 as I believed, thenews that she had missed a

year of happy lifewould nothavemovedmeatall,butthepossibilityofmissingthelast10 minutes mattered a greatdeal. Furthermore, theemotion I felt about thelovers’ reunion would nothavechangedifIhadlearnedthat theyactuallyhadaweektogether, rather than 10minutes. If the lover hadcome too late, however, LaTraviatawouldhavebeenanaltogether different story. A

story is about significantevents and memorablemoments, not about timepassing. Duration neglect isnormal in a story, and theending often defines itscharacter. The same corefeaturesappearintherulesofnarratives and in thememories of colonoscopies,vacations, and films. This ishow the remembering selfworks: it composes storiesand keeps them for future

reference.It is not only at the opera

thatwethinkoflifeasastoryandwishittoendwell.Whenwehear about the death of awoman who had beenestranged from her daughterfor many years, we want toknow whether they werereconciled as deathapproached. We do not careonly about the daughter’sfeelings—itisthenarrativeofthemother’slifethatwewish

toimprove.Caringforpeopleoften takes the form ofconcern for the quality oftheir stories, not for theirfeelings. Indeed, we can bedeeplymovedevenbyeventsthat change the stories ofpeoplewhoarealreadydead.We feel pity for a man whodied believing in his wife’slove for him, when we hearthatshehadaloverformanyyears and stayed with herhusband only for hismoney.

Wepitythehusbandalthoughhehadlivedahappylife.Wefeel the humiliation of ascientist who made animportant discovery that wasproved false after she died,although she did notexperience the humiliation.Mostimportant,ofcourse,weall care intensely for thenarrativeofourown life andvery much want it to be agood story, with a decenthero.

ThepsychologistEdDienerand his students wonderedwhether duration neglect andthe peak-end rule wouldgovern evaluations of entirelives. They used a shortdescription of the life of afictitiouscharactercalledJen,a never-married woman withno children, who diedinstantlyandpainlessly inanautomobile accident. In oneversion of Jen’s story, shewas extremely happy

throughout her life (whichlasted either 30 or 60 years),enjoying her work, takingvacations,spendingtimewithher friends and on herhobbies. Another versionadded 5 extra years to Jen’slife, who now died eitherwhen shewas 35 or 65. Theextrayearsweredescribedaspleasant but less so thanbefore. After reading aschematic biography of Jen,each participant answered

two questions: “Taking herlifeasawhole,howdesirabledoyouthinkJen’s lifewas?”and “How much totalhappiness or unhappinesswould you say that Jenexperiencedinherlife?”The results provided clear

evidence of both durationneglectandapeak-endeffect.In a between-subjectsexperiment (differentparticipants saw differentforms),doubling theduration

of Jen’s life had JtoAad Jtono effect whatsoever on thedesirability of her life, or onjudgments of the totalhappiness that Jenexperienced. Clearly, her lifewas represented by aprototypicalsliceoftime,notas a sequence of time slices.As a consequence, her “totalhappiness”wasthehappinessof a typical period in herlifetime, not the sum (orintegral) of happiness over

thedurationofherlife.As expected from this idea,

Diener and his students alsofounda less-is-moreeffect, astrong indication that anaverage (prototype) has beensubstitutedforasum.Adding5 “slightly happy” years to avery happy life caused asubstantial drop inevaluations of the totalhappinessofthatlife.At my urging, they also

collecteddataontheeffectof

theextra5years inawithin-subject experiment; eachparticipant made bothjudgments in immediatesuccession. In spite of mylong experience withjudgment errors, I did notbelievethatreasonablepeoplecould say that adding 5slightly happy years to a lifewould make it substantiallyworse. I was wrong. Theintuition that thedisappointing extra 5 years

made the whole life worsewasoverwhelming.The pattern of judgments

seemedsoabsurdthatDienerand his students initiallythoughtthatitrepresentedthefollyoftheyoungpeoplewhoparticipated in theirexperiments. However, thepattern did not change whenthe parents and older friendsof students answered thesame questions. In intuitiveevaluation of entire lives as

well as brief episodes, peaksand endsmatter but durationdoesnot.The pains of labor and the

benefits of vacations alwayscome up as objections to theidea of duration neglect: weallsharetheintuitionthatitismuch worse for labor to last24 than 6 hours, and that 6daysatagoodresortisbetterthan 3. Duration appears tomatterinthesesituations,butthis is only because the

quality of the end changeswith the length of theepisode. Themother is moredepletedandhelplessafter24hours than after 6, and thevacationer is more refreshedand rested after 6 days thanafter 3. What truly matterswhen we intuitively assesssuch episodes is theprogressive deterioration orimprovement of the ongoingexperience, and how thepersonfeelsattheend.

AmnesicVacationsConsider the choice of avacation. Do you prefer toenjoy a relaxing week at thefamiliar beach to which youwent last year? Or do youhope to enrich your store ofmemories?Distinctindustrieshave developed to cater tothese alternatives: resortsoffer restorative relaxation;tourism is about helpingpeople construct stories and

collect memories. Thefrenetic picture taking ofmany tourists suggests thatstoring memories is often animportantgoal,which shapesboth the plans for thevacation and the experienceof it. The photographer doesnot view the scene as amoment tobe savoredbut asa future memory to bedesigned. Pictures may beuseful to the rememberingself—though we rarely look

at them for very long, or asoftenasweexpected,orevenat all—but picture taking isnot necessarily the best wayfor the tourist’s experiencingselftoenjoyaview.In many cases we evaluate

touristic vacations by thestory and the memories thatweexpect tostore.Thewordmemorable is often used todescribe vacation highlights,explicitly revealing the goalof the experience. In other

situations—love comes tomind—the declaration thatthe present moment willnever be forgotten, thoughnot always accurate, changesthe character of themoment.A self-consciouslymemorable experience gainsa weight and a significanceJtoAceJto that it would nototherwisehave.Ed Diener and his team

provided evidence that it isthe remembering self that

chooses vacations. Theyasked students to maintaindaily diaries and record adaily evaluation of theirexperiences during springbreak. The students alsoprovided a global rating ofthe vacation when it hadended.Finally,theyindicatedwhether or not they intendedto repeat or not to repeat thevacation they had just had.Statistical analysisestablishedthattheintentions

for future vacations wereentirely determined by thefinal evaluation—even whenthat score did not accuratelyrepresent the quality of theexperiencethatwasdescribedinthediaries.Asinthecold-hand experiment, right orwrong, people choose bymemory when they decidewhether or not to repeat anexperience.Athoughtexperimentabout

yournextvacationwillallow

you to observe your attitudetoyourexperiencingself.

At the end of thevacation,allpicturesandvideoswillbedestroyed.Furthermore, you willswallow a potion thatwill wipe out all yourmemories of thevacation.

Howwouldthisprospectaffect your vacation

plans?Howmuchwouldyoubewillingtopayforit, relative toanormallymemorablevacation?

While I have not formallystudied the reactions to thisscenario,myimpressionfromdiscussing it with people isthat the elimination ofmemoriesgreatly reduces thevalue of the experience. Insome cases, people treatthemselves as they would

treat another amnesic,choosingtomaximizeoverallpleasure by returning to aplace where they have beenhappy in the past. However,some people say that theywouldnotbothertogoatall,revealing that they care onlyabouttheirrememberingself,and care less about theiramnesic experiencing selfthan about an amnesicstranger.Manypointout thatthey would not send either

themselves or anotheramnesic to climb mountainsor trek through the jungle—becausetheseexperiencesaremostly painful in real timeand gain value from theexpectationthatboththepainand the joy of reaching thegoalwillbememorable.For another thought

experiment,imagineyoufacea painful operation duringwhich you will remainconscious. You are told you

will scream in pain and begthesurgeontostop.However,youarepromisedanamnesia-inducing drug that willcompletely wipe out anymemoryof theepisode.Howdo you feel about such aprospect? Here again, myinformal observation is thatmost people are remarkablyindifferent to the pains oftheir experiencing self.Somesay they don’t care at all.Others share my feeling,

which is that I feel pity formy suffering self but notmore than Iwould feel for astranger in pain. Odd as itmay seem, I am myremembering self, and theexperiencing self, who doesmyliving,islikeastrangertome.

SpeakingofLifeasaStory

“Heisdesperatelytrying

to protect the narrativeof a life of integrity,which is endangered bythelatestepisode.”

“The length towhichhewas willing to go for aone-night encounter is asign of total durationneglect.”

“You seem to bedevoting your entire

vacation to theconstruction ofmemories. Perhaps youshould put away thecamera and enjoy themoment,evenifitisnotverymemorable?”

“She is an Alzheimer’spatient. She no longermaintains a narrative ofher life, but herexperiencing self is still

sensitive to beauty andgentleness.”

P

ExperiencedWell-Being

When I became interested inthestudyofwell-beingaboutfifteen years ago, I quicklyfound out that almosteverything that was knownaboutthesubjectdrewontheanswersofmillionsofpeopleto minor variations of asurvey question, which was

generally accepted as ameasure of happiness. Thequestion is clearly addressedto your remembering self,which is invited to thinkaboutyourlife:

All things considered,how satisfied are youwithyourlifeasawholethesedays?

Havingcometothetopicof

well-being from the study of

the mistaken memories ofcolonoscopies and painfullycold hands, I was naturallysuspicious of globalsatisfaction with life as avalid measure of well-being.As the remembering self hadnot proved to be a goodwitness inmy experiments, Ifocused on the well-being ofthe experiencing self. Iproposedthatitmadesensetosaythat“HelenwashappyinthemonthofMarch”if

she spent most of hertime engaged inactivities that shewouldrather continue thanstop, little time insituations she wished toescape, and—veryimportantbecause life isshort—not too muchtimeinaneutralstate inwhich she would notcareeitherway.

There are many different

experiences we would rathercontinue than stop, includingboth mental and physicalpleasures. One of theexamples Ihad inmindforasituation that Helen wouldwish to continue is totalabsorption in a task, whichMihaly Csikszentmihalyicalls flow—a state that someartists experience in theircreative moments and thatmany other people achievewhen enthralled by a film, a

book, or a crossword puzzle:interruptionsarenotwelcomein any of these situations. Ialsohadmemoriesofahappyearly childhood in which Ialwayscriedwhenmymothercame to tear me away frommy toys to take me to thepark, and cried again whenshe took me away from theswings and the slide. Theresistance to interruptionwasa sign I had been having agoodtime,bothwithmytoys

andwiththeswings.I proposed to measure

Helen’s objective happinessprecisely as we assessed theexperience of the twocolonoscopy patients, byevaluating a profile of thewell-being she experiencedover successive moments ofher life. In this I wasfollowing Edgeworth’shedonimeter method of acentury earlier. In my initialenthusiasmfor thisapproach,

I was inclined to dismissHelen’s remembering self asan error-pronewitness to theactual well-being of herexperiencingself.Isuspectedthispositionwastooextreme,whichit turnedouttobe,butitwasagoodstart.

n="4">ExperiencedWell-Being

I assembled “a dream team”that included three otherpsychologists of different

specialties and oneeconomist, and we set outtogethertodevelopameasureof the well-being of theexperiencing self. Acontinuous record ofexperiencewas unfortunatelyimpossible—a person cannotlive normally whileconstantly reporting herexperiences. The closestalternative was experiencesampling, a method thatCsikszentmihalyi had

invented. Technology hasadvanced since its first uses.Experience sampling is nowimplemented byprogramming an individual’scell phone to beep or vibrateatrandomintervalsduringtheday.Thephone thenpresentsa brief menu of questionsabout what the respondentwasdoingandwhowaswithherwhenshewasinterrupted.Theparticipantisalsoshownrating scales to report the

intensity of various feelings:happiness, tension, anger,worry, engagement, physicalpain,andothers.Experience sampling is

expensive and burdensome(althoughlessdisturbingthanmost people initially expect;answeringthequestionstakesvery little time). A morepractical alternative wasneeded, so we developed amethod that we called theDay Reconstruction Method

(DRM). We hoped it wouldapproximate the results ofexperience sampling andprovide additionalinformation about the waypeople spend their time.Participants (all women, intheearlystudies)wereinvitedto a two-hour session. Wefirst asked them to relive theprevious day in detail,breaking it up into episodeslike scenes in a film. Later,they answered menus of

questionsabouteachepisode,based on the experience-sampling method. Theyselected activities in whichtheywereengagedfromalistand indicated the one towhich they paid mostattention.Theyalsolistedtheindividuals they had beenwith, and rated the intensityof several feelings onseparate 0–6 scales (0 = theabsence of the feeling; 6 =most intense feeling). Our

methoddrewonevidencethatpeople who are able toretrieve a past situation indetail are also able to relivethefeelingsthataccompaniedit, even experiencing theirearlier physiologicalindicationsofemotion.We assumed that our

participants would fairlyaccuratelyrecoverthefeelingof a prototypical moment ofthe episode. Severalcomparisons with experience

sampling confirmed thevalidityoftheDRM.Becausethe participants also reportedthe times at which episodesbegan and ended, we wereable to compute a duration-weighted measure of theirfeeling during the entirewakingday.Longer episodescounted more than shortepisodes in our summarymeasure of daily affect. Ourquestionnaire also includedmeasures of life satisfaction,

which we interpreted as thesatisfaction of theremembering self. We usedthe DRM to study thedeterminants of bothemotionalwell-beingand lifesatisfaction in severalthousand women in theUnited States, France, andDenmark.Theexperienceofamoment

or an episode is not easilyrepresented by a singlehappiness value. There are

many variants of positivefeelings, including love, joy,engagement, hope,amusement,andmanyothers.Negativeemotionsalsocomein many varieties, includinganger,shame,depression,andloneliness. Although positiveand negative emotions existatthesametime,itispossibleto classify most moments oflife as ultimately positive ornegative. We could identifyunpleasant episodes by

comparing the ratings ofpositive and negativeadjectives. We called anepisode unpleasant if anegativefeelingwasassigneda higher rating than all thepositive feelings. We foundthat American women spentabout 19% of the time in anunpleasant state, somewhathigher than French women(16%) or Danish women(14%).We called the percentage

Jr">nQgeJr">oftimethatanindividual spends in anunpleasant state theU-index.For example, an individualwho spent 4 hours of a 16-hour waking day in anunpleasantstatewouldhaveaU-index of 25%. The appealof the U-index is that it isbased not on a rating scalebut on an objectivemeasurement of time. If theU-index for a populationdropsfrom20%to18%,you

can infer that the total timethat the population spent inemotional discomfort or painhasdiminishedbyatenth.A striking observation was

theextentofinequalityinthedistribution of emotionalpain. About half ourparticipants reported goingthroughanentiredaywithoutexperiencing an unpleasantepisode.Ontheotherhand,asignificant minority of thepopulation experienced

considerable emotionaldistress formuch of the day.It appears that a smallfraction of the populationdoesmost of the suffering—whether because of physicalormental illness,anunhappytemperament, or themisfortunes and personaltragediesintheirlife.A U-index can also be

computed for activities. Forexample,wecanmeasuretheproportionoftimethatpeople

spendinanegativeemotionalstate while commuting,working, or interacting withtheir parents, spouses, orchildren.For1,000Americanwomen inaMidwesterncity,theU-indexwas29%for themorning commute, 27% forwork, 24% for child care,18%forhousework,12%forsocializing, 12% for TVwatching, and 5% for sex.The U-index was higher byabout6%onweekdaysthanit

was on weekends, mostlybecause on weekends peoplespend less time in activitiestheydislikeanddonotsufferthe tension and stressassociated with work. Thebiggest surprise was theemotional experience of thetime spent with one’schildren,whichforAmericanwomen was slightly lessenjoyable than doinghousework. Here we foundone of the few contrasts

between French andAmerican women:Frenchwomenspendlesstimewith their children but enjoyitmore,perhapsbecausetheyhave more access to childcare and spend less of theafternoon driving children tovariousactivities.Anindividual’smoodatany

moment depends on hertemperament and overallhappiness, but emotionalwell-being also fluctuates

considerablyoverthedayandthe week. The mood of themomentdependsprimarilyonthecurrentsituation.Moodatwork, for example, is largelyunaffectedby thefactors thatinfluence general jobsatisfaction, includingbenefits and status. Moreimportant are situationalfactorssuchasanopportunityto socialize with coworkers,exposure to loud noise, timepressure (a significant source

of negative affect), and theimmediatepresenceofaboss(in our first study, the onlything that was worse thanbeing alone). Attention iskey. Our emotional state islargely determined by whatwe attend to, and we arenormally focused on ourcurrent activity andimmediate environment.There are exceptions, wherethe quality of subjectiveexperience is dominated by

recurrentthoughtsratherthanbytheeventsof themoment.When happily in love, wemay feel joy even whencaught in traffic, and ifgrieving, we may remaindepressed when watching afunny movie. In normalcircumstances, however, wedraw pleasure and pain fromwhat is happening at themoment,ifweattendtoit.Toget pleasure from eating, forexample,youmustnoticethat

you are doing it. We foundthat French and Americanwomen spent about the sameamountoftimeeating,butforFrenchwomen, eating wastwice as likely to be focal asit was for American women.TheAmericanswerefarmorepronetocombineeatingwithother activities, and theirpleasure from eating wascorrespondinglydiluted.These observations have

implications for both

individuals and society. Theuseoftimeisoneoftheareasof life over which peoplehave some control. Fewindividuals can willthemselves to ha Jr">nQ haJr">ve a sunnier disposition,but some may be able toarrange their lives to spendless of their day commuting,and more time doing thingsthey enjoy with people theylike. The feelings associatedwith different activities

suggest that another way toimprove experience is toswitch time from passiveleisure,suchasTVwatching,to more active forms ofleisure, including socializingandexercise.Fromthesocialperspective, improvedtransportation for the laborforce, availability of childcareforworkingwomen,andimproved socializingopportunities for the elderlymay be relatively efficient

waystoreducetheU-indexofsociety—even a reduction by1% would be a significantachievement, amounting tomillions of hours of avoidedsuffering. Combined nationalsurveys of time use and ofexperienced well-being caninform social policy inmultipleways.Theeconomiston our team, Alan Krueger,took the lead in an effort tointroduce elements of thismethod into national

statistics.

Measures of experiencedwell-being are now routinelyused in large-scale nationalsurveys in the United States,Canada, and Europe, and theGallup World Poll hasextended thesemeasurementstomillions of respondents intheUnitedStatesandinmorethan150countries.Thepollselicit reports of the emotions

experienced during theprevious day, though in lessdetail than the DRM. Thegigantic samples allowextremely fine analyses,which have confirmed theimportance of situationalfactors, physical health, andsocial contact in experiencedwell-being. Not surprisingly,a headache will make aperson miserable, and thesecond best predictor of thefeelingsofadayiswhethera

person did or did not havecontacts with friends orrelatives. It is only a slightexaggeration to say thathappinessistheexperienceofspending time with peopleyouloveandwholoveyou.The Gallup data permit a

comparisonoftwoaspectsofwell-being:

the well-being thatpeople experience as

theylivetheirlivesthe judgment theymakewhen theyevaluate theirlife

Gallup’s life evaluation ismeasured by a questionknown as the Cantril Self-AnchoringStrivingScale:

Please imagine a ladderwith steps numberedfrom zero at the bottom

to10atthetop.Thetopof the ladder representsthebestpossible life foryou and the bottom ofthe ladder represents theworst possible life foryou. On which step oftheladderwouldyousayyou personally feel youstandatthistime?

Some aspects of life havemoreeffectontheevaluationof one’s life than on the

experience of living.Educational attainment is anexample. More education isassociated with higherevaluation of one’s life, butnot with greater experiencedwell-being.Indeed,atleastinthe United States, the moreeducatedtendtoreporthigherstress. On the other hand, illhealth has a much strongeradverseeffectonexperiencedwell-being than on lifeevaluation. Living with

children also imposes asignificant cost in thecurrency of daily feelings—reportsofstressandangerarecommon among parents, butthe adverse effects on lifeevaluation are smaller.Religious participation alsohas relatively greaterfavorable impact on bothpositive affect and stressreduction than on lifeevaluation. Surprisingly,however,religionprovidesno

reduction of feelings ofdepressionorworry.An analysis of more than

450,000 responses to theGallup-Healthways Well-BeiJr">n QBei Jr">ng Index, adaily survey of 1,000Americans, provides asurprisingly definite answerto the most frequently askedquestion in well-beingresearch: Can money buyhappiness?The conclusion isthat being poor makes one

miserable,andthatbeingrichmay enhance one’s lifesatisfaction, but does not (onaverage) improveexperiencedwell-being.Severepovertyamplifiesthe

experienced effects of othermisfortunes of life. Inparticular, illness is muchworse for the very poor thanfor those who are morecomfortable. A headacheincreases the proportionreporting sadness and worry

from 19% to 38% forindividuals in the top two-thirds of the incomedistribution. Thecorresponding numbers forthepooresttenthare38%and70%—ahigherbaseline leveland a much larger increase.Significant differencesbetween the very poor andothers are also found for theeffects of divorce andloneliness. Furthermore, thebeneficial effects of the

weekend on experiencedwell-being are significantlysmallerfortheverypoorthanformosteveryoneelse.The satiation level beyond

whichexperiencedwell-beingno longer increases was ahousehold income of about$75,000 inhigh-costareas (itcould be less in areas wherethe cost of living is lower).The average increase ofexperienced well-beingassociated with incomes

beyond that level wasprecisely zero. This issurprising because higherincome undoubtedly permitsthe purchase of manypleasures,includingvacationsin interesting places andopera tickets, as well as animprovedlivingenvironment.Why do these addedpleasures not show up inreports of emotionalexperience? A plausibleinterpretation is that higher

income is associated with areduced ability to enjoy thesmallpleasuresof life.Thereis suggestive evidence infavor of this idea: primingstudents with the idea ofwealth reduces the pleasuretheir face expresses as theyeatabarofchocolate!There is a clear contrast

betweentheeffectsofincomeon experienced well-beingand on life satisfaction.Higher income bringswith it

higher satisfaction, wellbeyond the point at which itceases to have any positiveeffect on experience. Thegeneralconclusionisasclearfor well-being as it was forcolonoscopies: people’sevaluations of their lives andtheir actual experience maybe related, but they are alsodifferent. Life satisfaction isnotaflawedmeasureoftheirexperienced well-being, as Ithought someyears ago. It is

somethingelseentirely.

SpeakingofExperiencedWell-

Being

“Theobjectiveofpolicyshould be to reducehuman suffering. Weaimfora lowerU-indexin society. Dealing withdepression and extremepoverty should be a

priority.”

“The easiest way toincrease happiness is tocontrolyouruseoftime.Can you findmore timeto do the things youenjoydoing?”

“Beyond the satiationlevelofincome,youcanbuy more pleasurableexperiences,butyouwill

losesomeofyourabilityto enjoy the lessexpensiveones.”

P

ThinkingAboutLife

Figure 16 is taken from ananalysisbyAndrewClark,EdDiener, and YannisGeorgellis of the GermanSocio-Economic Panel, inwhich the same respondentswere asked every year abouttheir satisfaction with theirlife. Respondents alsoreported major changes that

had occurred in theircircumstances during thepreceding year. The graphshowsthelevelofsatisfactionreportedbypeoplearoundthetimetheygotmarried.

Figure16

The graph reliably evokes

nervous laughter fromaudiences, and thenervousness is easy tounderstand: after all, peoplewhodecidetogetmarrieddosoeitherbecause theyexpectitwillmake them happier orbecause they hope thatmaking a tie permanent willmaintain the present state ofbliss. In the useful termintroduced by Daniel Gilbert

and Timothy Wilson, thedecision to get marriedreflects, for many people, amassive error of affectiveforecasting.Ontheirweddingday, thebride and thegroomknow that the rateofdivorceishighandthattheincidenceof marital disappointment iseven higher, but they do notbelieve that these statisticsapplytothem.Thestartlingnewsoffigure

16 is thesteepdeclineof life

satisfaction. The graph iscommonly interpreted astracing a process ofadaptation,inwhichtheearlyjoys of marriage quicklydisappear as the experiencesbecome routine. However,another approach is possible,which focuses on heuristicsof judgment. Here we askwhat happens in people’smindswhentheyareaskedtoevaluate their life. Thequestions “How satisfied are

you with your life as awhole?”and“Howhappyareyou these days?” are not assimple as “What is yourtelephone number?” How dosurveyparticipantsmanagetoanswer such questions in afewseconds,asalldo?Itwillhelp to think of this asanother judgment. As is alsothe case for other questions,some people may have aready-made answer, whichtheyhadproducedonanother

occasion in which theyevaluated their life. Others,probably themajority,donotquicklyfindaresponsetotheexact question they wereasked, and automaticallymake their task easier bysubstituting the answer toanotherquestion.System1isat work. When we look atfigure16inthislight,ittakesonadifferentmeaning.Theanswerstomanysimple

questions can be substituted

foraglobalevaluationoflife.You remember the study inwhich students who had justbeen asked how many datesthey had in the previousmonth reported their“happiness these days” as ifdating was the onlysignificantfactintheirlife.Inanother well-knownexperiment in the same vein,Norbert Schwarz and hiscolleagues invited subjects tothe lab to complete a

questionnaire on lifesatisfaction. Before theybegan that task, however, heasked them to photocopy asheet of paper for him. Halftherespondentsfoundadimeon the copying machine,planted there by theexperimenter. The minorlucky incident caused amarked improvement insubjects’reportedsatisfactionwith their life as awhole!Amoodheuristic isoneway to

answer life-satisfactionquestions.The dating survey and the

coin-on-the-machineexperiment demonstrated, asintended,thattheresponsestoglobal well-being questionsshould be takenwith a grainof salt. But of course yourcurrentmood is not the onlything that comes to mindwhen you are asked toevaluate your life. You arelikely to be reminded of

significant events in yourrecent past or near future; ofrecurrent concerns, such asthehealthJghtA5althJghtofaspouseor thebadcompanythat your teenager keeps; ofimportant achievements andpainful failures. A few ideasthat are relevant to thequestion will occur to you;many others will not. Evenwhen it is not influenced bycompletely irrelevantaccidentssuchas thecoinon

the machine, the score thatyou quickly assign to yourlife is determined by a smallsample of highly availableideas, not by a carefulweighting of the domains ofyourlife.People who recently

married, or are expecting tomarry in the near future, arelikely to retrieve that factwhen asked a generalquestion about their life.Because marriage is almost

always voluntary in theUnited States, almosteveryonewho is remindedofhis or her recent orforthcomingmarriagewill behappy with the idea.Attention is the key to thepuzzle.Figure16canbereadas a graph of the likelihoodthatpeoplewillthinkoftheirrecent or forthcomingmarriage when asked abouttheirlife.Thesalienceofthisthought is bound to diminish

with the passage of time, asitsnoveltywanes.The figure shows an

unusually high level of lifesatisfaction that lasts two orthree years around the eventofmarriage.However, if thisapparent surge reflects thetimecourseofaheuristic foranswering thequestion, thereis little we can learn from itabout either happiness orabout the process ofadaptation to marriage. We

cannotinferfromitthatatideof raised happiness lasts forseveral years and graduallyrecedes.Evenpeoplewhoarehappytoberemindedoftheirmarriage when asked aquestion about their life arenot necessarily happier therest of the time. Unless theythink happy thoughts abouttheirmarriageduringmuchoftheir day, it will not directlyinfluence their happiness.Even newlyweds who are

luckyenoughtoenjoyastateof happy preoccupation withtheir love will eventuallyreturn to earth, and theirexperienced well-being willagain depend, as it does forthe rest of us, on theenvironment and activities ofthepresentmoment.In the DRM studies, there

was no overall difference inexperienced well-beingbetween women who livedwithamateandwomenwho

did not. The details of howthe two groups used theirtime explained the finding.Women who have a matespend less time alone, butalso much less time withfriends. They spend moretime making love, which iswonderful,butalsomoretimedoing housework, preparingfood,andcaringforchildren,all relatively unpopularactivities.And of course, thelargeamountoftimemarried

women spend with theirhusband is much morepleasant for some than forothers. Experienced well-being is on averageunaffected by marriage, notbecause marriage makes nodifference to happiness butbecause it changes someaspects of life for the betterandothersfortheworse.

One reason for the low

correlations betweenindividuals’ circumstancesandtheirsatisfactionwithlifeis that both experiencedhappinessandlifesatisfactionarelargelydeterminedbythegenetics of temperament. Adisposition for well-being isas heritable as height orintelligence, as demonstratedby studies of twins separatedat birth. People who appearequallyfortunatevarygreatlyin how happy they are. In

someinstances,asinthecaseof marriage, the correlationswith well-being are lowbecause of balancing effects.The same situation may begoodforsomepeopleandbadfor others, and newcircumstances have bothbenefits and costs. In othercases, such as high income,theeffectsonlifesatisfactionaregenerallypositive,butthepicture is complicated by thefact that some people care

muchmoreaboutmoneythanothersdo.A large-scale study of the

impact of higher education,which was conducted forJghtA5 aor Jghtanotherpurpose, revealed strikingevidence of the lifelongeffectsofthegoalsthatyoungpeople set for themselves.Therelevantdataweredrawnfromquestionnaires collectedin 1995–1997 fromapproximately 12,000 people

who had started their highereducation in elite schools in1976.When theywere 17 or18, theparticipantshadfilledout a questionnaire in whichthey rated the goal of “beingvery well-off financially” ona 4-point scale ranging from“not important” to“essential.”Thequestionnairethey completed twenty yearslater included measures oftheir incomein1995,aswellas a global measure of life

satisfaction.Goals make a large

difference. Nineteen yearsafter they stated theirfinancialaspirations,manyofthepeoplewhowantedahighincome had achieved it.Among the 597 physiciansand other medicalprofessionals in the sample,for example, each additionalpoint on the money-importance scale wasassociated with an increment

of over $14,000 of jobincome in 1995 dollars!Nonworking married womenwere also likely to havesatisfied their financialambitions. Each point on thescale translated into morethan $12,000 of addedhousehold income for thesewomen,evidentlythroughtheearningsoftheirspouse.The importance that people

attached to income at age 18also anticipated their

satisfactionwiththeirincomeas adults. We compared lifesatisfaction in a high-incomegroup (more than $200,000household income) to a low-to moderate-income group(less than $50,000). Theeffect of income on lifesatisfaction was larger forthose who had listed beingwell-off financially as anessential goal: .57 point on a5-point scale. Thecorresponding difference for

those who had indicated thatmoneywasnotimportantwasonly .12. The people whowantedmoneyandgotitweresignificantly more satisfiedthan average; those whowantedmoneyanddidn’tgetit were significantly moredissatisfied. The sameprinciple applies to othergoals—one recipe for adissatisfied adulthood issetting goals that areespecially difficult to attain.

Measured by life satisfaction20 years later, the leastpromising goal that a youngperson could have was“becomingaccomplishedinaperforming art.” Teenagers’goals influencewhathappensto them, where they end up,andhowsatisfiedtheyare.In part because of these

findings I have changed mymind about the definition ofwell-being. The goals thatpeople set for themselvesare

so important towhat theydoand how they feel about itthat an exclusive focus onexperiencedwell-being isnottenable. We cannot hold aconcept of well-being thatignoreswhatpeoplewant.Ontheotherhand, it is also truethat a concept of well-beingthat ignores how people feelas they liveand focusesonlyon how they feel when theythink about their life is alsountenable. We must accept

the complexities of a hybridview,inwhichthewell-beingofbothselvesisconsidered.

TheFocusingIllusionWe can infer from the speedwithwhichpeoplerespondtoquestionsabouttheirlife,andfrom the effects of currentmoodontheirresponses, thatthey do not engage in acareful examination whenthey evaluate their life. They

must be using heuristics,which are examples of bothsubstitution and WYSIATI.Although their view of theirlife was influenced by aquestionaboutdatingorbyacoinonthecopyingmachine,the participants in thesestudies did not forget thatthere is more to life thandating or feeling lucky. Theconcept of happiness is notsuddenly changed by findinga dime, but System1 readily

substitutes a small part of itfor the whole of it. Anyaspect of life to whichattentionisdirectedwillloomJghtA5 aoom Jght large in aglobal evaluation.This is theessence of the focusingillusion, which can bedescribed in a singlesentence:

Nothing in life is asimportantasyouthinkitiswhenyouarethinking

aboutit.Theoriginof this ideawasafamily debate about movingfromCalifornia to Princeton,in which my wife claimedthat people are happier inCalifornia than on the EastCoast.Iarguedthatclimateisdemonstrably not animportant determinant ofwell-being—theScandinavian countries areprobably the happiest in the

world. I observed thatpermanent life circumstanceshave little effect on well-being and tried in vain toconvince my wife that herintuitionsaboutthehappinessofCalifornianswereanerrorofaffectiveforecasting.Ashort time later,with this

debate still on my mind, Iparticipated in a workshopabout the social science ofglobalwarming.A colleaguemade an argument that was

basedonhisviewofthewell-being of the population ofplanet Earth in the nextcentury. I argued that it waspreposterous to forecastwhatit would be like to live on awarmer planet when we didnotevenknowwhat it is liketo live in California. Soonafter that exchange, mycolleagueDavidSchkadeandIweregrantedresearchfundsto study two questions: ArepeoplewholiveinCalifornia

happier than others? andWhat are the popular beliefsabout the relative happinessofCalifornians?We recruited large samples

of students at major stateuniversities in California,Ohio, and Michigan. Fromsome of themwe obtained adetailed report of theirsatisfaction with variousaspects of their lives. Fromothers we obtained aprediction of how someone

“with your interests andvalues” who lived elsewherewould complete the samequestionnaire.Aswe analyzed the data, it

became obvious that I hadwon thefamilyargument.Asexpected, the students in thetwo regions differed greatlyin their attitude to theirclimate: the Californiansenjoyed their climateand theMidwesterners despisedtheirs.Butclimatewasnotan

important determinant ofwell-being. Indeed, therewasno difference whatsoeverbetween the life satisfactionof students in California andin the Midwest. We alsofound that my wife was notalone in her belief thatCalifornians enjoy greaterwell-being than others. Thestudents in both regionsshared the same mistakenview, and we were able totrace their error to an

exaggerated belief in theimportance of climate. Wedescribed the error as afocusingillusion.Theessenceofthefocusing

illusion is WYSIATI, givingtoo much weight to theclimate, too little to all theother determinants of well-being. To appreciate howstrong this illusion is, take afew seconds to consider thequestion:

How much pleasure doyougetfromyourcar?

Ananswercametoyourmindimmediately; you know howmuchyoulikeandenjoyyourcar.Nowexamineadifferentquestion: “When do you getpleasurefromyourcar?”Theanswer to this question maysurprise you, but it isstraightforward: you getpleasure(ordispleasure)fromyour car when you think

about your car, which isprobably not very often.Under normal circumstances,you do not spendmuch timethinkingaboutyourcarwhenyou are driving it.You thinkof other things as you drive,andyourmoodisdeterminedbywhateveryou thinkabout.Hereagain,whenyoutriedtorate how much you enjoyedyour car, you actuallyanswered JghtA5 aed Jghtamuch narrower question:

“Howmuch pleasure do youget from your car when youthink about it?” Thesubstitution caused you toignorethefactthatyourarelythink about your car, a formof duration neglect. Theupshot is a focusing illusion.If you like your car, you arelikely to exaggerate thepleasure you derive from it,whichwillmisleadyouwhenyou think of the virtues ofyour current vehicle as well

as when you contemplatebuyinganewone.A similar bias distorts

judgmentsofthehappinessofCalifornians. When askedabout the happiness ofCalifornians, you probablyconjureanimageofsomeoneattending to a distinctiveaspect of the Californiaexperience, such as hiking inthe summer or admiring themild winter weather. Thefocusing illusion arises

becauseCalifornians actuallyspend little time attending tothese aspects of their life.Moreover, long-termCalifornians are unlikely tobe reminded of the climatewhen asked for a globalevaluationoftheirlife.Ifyouhave been there all your lifeanddonottravelmuch,livingin California is like havingten toes: nice, but notsomething one thinks muchabout.Thoughtsofanyaspect

of life are more likely to besalient if a contrastingalternativeishighlyavailable.Peoplewho recentlymoved

to California will responddifferently. Consider anenterprising soulwhomovedfromOhio to seek happinessinabetterclimate.Forafewyears following the move, aquestionabouthissatisfactionwithlifewillprobablyremindhim of the move and alsoevoke thoughts of the

contrasting climates in thetwo states. The comparisonwill surely favor California,and the attention to thataspect of life may distort itstrue weight in experience.However, the focusingillusion can also bringcomfort. Whether or not theindividual is actually happierafterthemove,hewillreporthimself happier, becausethoughts of the climate willmake him believe that he is.

The focusing illusion cancause people to be wrongabout their present state ofwell-being as well as aboutthe happiness of others, andabout their own happiness inthefuture.

What proportion of thedaydoparaplegicsspendinabadmood?

Thisquestionalmostcertainlymade you think of a

paraplegic who is currentlythinkingaboutsomeaspectofhis condition. Your guessabout a paraplegic’smood isthereforelikelytobeaccuratein the early days after acrippling accident; for sometime after the event, accidentvictims think of little else.But over time, with fewexceptions, attention iswithdrawn from a newsituation as it becomes morefamiliar.Themainexceptions

are chronic pain, constantexposure to loud noise, andsevere depression. Pain andnoise are biologically set tobe signals that attractattention, and depressioninvolves a self-reinforcingcycle of miserable thoughts.There is therefore noadaptation to theseconditions. Paraplegia,however, is not one of theexceptions: detailedobservations show that

paraplegics are in a fairlygoodmoodmorethanhalfofthe time as early as onemonth following theiraccident—though their moodiscertainlysomberwhentheythink about their situation.Most of the time, however,paraplegicswork, read,enjoyjokes and friends, and getangry when they read aboutpolitics in the newspaper.When they are involved inany of these activities, they

are not much different fromanyone else, and we canexpect the experienced well-being of paraplegics to benear normal much of thetime. Adaptation to a newsituation, whether good orbad, consists in large part ofthinkinglessandlessaboutit.In that sense,most long-termcircumstances of life,including paraplegia andmarriage, are part-time statesthat one inhabits only when

one at JghtA5 a at Jghttendstothem.One of the privileges of

teaching at Princeton is theopportunity to guide brightundergraduates through aresearch thesis. And one ofmy favorite experiences inthis vein was a project inwhichBeruriaCohncollectedand analyzed data from asurvey firm that askedrespondents to estimate theproportion of time that

paraplegics spend in a badmood. She split herrespondents into two groups:some were told that thecrippling accident hadoccurred a month earlier,some a year earlier. Inaddition, each respondentindicated whether he or sheknewaparaplegicpersonally.The two groups agreedclosely in their judgmentabout the recent paraplegics:thosewhoknewaparaplegic

estimated 75% bad mood;those who had to imagine aparaplegic said 70%. Incontrast, the two groupsdiffered sharply in theirestimates of the mood ofparaplegics a year after theaccidents: those who knew aparaplegic offered 41% astheir estimate of the time inthatbadmood.Theestimatesof those who were notpersonally acquainted with aparaplegic averaged 68%.

Evidently, thosewhoknewaparaplegic had observed thegradual withdrawal ofattention from the condition,but others did not forecastthat this adaptation wouldoccur. Judgments about themood of lottery winners onemonth andoneyear after theevent showed exactly thesamepattern.We can expect the life

satisfaction of paraplegicsand those afflicted by other

chronic and burdensomeconditions to be low relativeto their experienced well-being, because the request toevaluate their lives willinevitablyremindthemofthelife of others and of the lifethey used to lead. Consistentwith this idea, recent studiesof colostomy patients haveproduced dramaticinconsistencies between thepatients’ experienced well-beingandtheirevaluationsof

their lives. Experiencesamplingshowsnodifferencein experienced happinessbetween these patients and ahealthy population. Yetcolostomy patients would bewillingtotradeawayyearsoftheir life for a shorter lifewithout the colostomy.Furthermore, patients whosecolostomy has been reversedremember their time in thiscondition as awful, and theywould give up evenmore of

their remaining life not tohave to return to it. Here itappearsthattherememberingself is subject to a massivefocusing illusion about thelife that theexperiencingselfenduresquitecomfortably.DanielGilbert andTimothy

Wilson introduced the wordmiswanting to describe badchoices thatarise fromerrorsof affective forecasting. Thisword deserves to be ineveryday language. The

focusing illusion (whichGilbert and Wilson callfocalism) is a rich source ofmiswanting. In particular, itmakesuspronetoexaggeratethe effect of significantpurchases or changedcircumstances on our futurewell-being.Compare two commitments

thatwillchangesomeaspectsof your life: buying acomfortable new car andjoining a group that meets

weekly, perhaps a poker orbook club. Both experienceswill be novel and exciting atthe start. The crucialdifference is that you willeventually pay little attentiontothecarasyoudrive it,butyouwill alwaysattend to thesocial interaction to whichyou committed yourself. ByWYSIATI, you are likely toexaggerate the long-termbenefits of the car, but youare not likely to make the

same mistake for a socialgathering or for inherentlyattention-demandingactivities such as playingtennisor learning toplay thecello. The focusing illusioncreates a bias in favor ofgoods and experiences thatare initially exciting, even iftheywilleventuallylosetheirappeal. Time is neglected,causing experiences that willretain their attention value inthe long term to be

appreciated less than theydeservetobe.

TimeandTimeAgain

The role of time has been arefrain in this part of thebook.It is logical todescribethe life of the experiencingself as a series of moments,eachwith a value.The valueof an episode—I have calledit a hedonimeter total—is

simply the sumof thevaluesofitsmoments.Butthisisnothow the mind representsepisodes. The rememberingself, as I have described it,also tells stories and makeschoices, and neither thestories nor the choicesproperly represent time. Instorytellingmode,anepisodeis represented by a fewcritical moments, especiallythe beginning, the peak, andthe end. Duration is

neglected.Wesaw this focuson singularmoments both inthecold-handsituationandinVioletta’sstory.We sawadifferent formof

duration neglect in prospecttheory, in which a state isrepresented by the transitiontoit.Winningalotteryyieldsanewstateofwealththatwillendure for some time, butdecisionutilitycorrespondstothe anticipated intensity ofthe reaction to the news that

onehaswon.Thewithdrawalof attention and otheradaptations to the new stateare neglected, as only thatthin slice of time isconsidered. The same focuson the transition to the newstateand the sameneglectoftimeandadaptationarefoundinforecastsof thereactiontochronic diseases, and ofcourse in the focusingillusion. The mistake thatpeople make in the focusing

illusion involves attention toselectedmomentsandneglectof what happens at othertimes.Themindisgoodwithstories,butitdoesnotappearto be well designed for theprocessingoftime.Duringthelasttenyearswe

have learnedmanynew factsabouthappiness.Butwehavealso learned that the wordhappiness does not have asimple meaning and shouldnot be used as if it does.

Sometimesscientificprogressleaves us more puzzled thanwewerebefore.

SpeakingofThinkingAboutLife

“Shethoughtthatbuyinga fancy carwouldmakeherhappier,butitturnedout to be an error ofaffectiveforecasting.”

“His car brokedownonthe way to work thismorning and he’s in afoulmood.This isnotagood day to ask himabout his jobsatisfaction!”

“She looks quitecheerful most of thetime, but when she isasked she says she isvery unhappy. The

question must make herthink of her recentdivorce.”

“Buying a larger housemaynotmakeushappierin the long term. Wecould be suffering fromafocusingillusion.”

“He has chosen to splithis time between twocities.Probablyaserious

caseofmiswanting.”

P

Conclusions

I began this book byintroducing two fictitiouscharacters, spent some timediscussing two species, andended with two selves. Thetwo characters were theintuitive System 1, whichdoes JghtA5 `؇J5 the fastthinking,andtheeffortfuland

slowerSystem2,whichdoesthe slow thinking, monitorsSystem 1, and maintainscontrol as best it can withinitslimitedresources.Thetwospecies were the fictitiousEcons,wholiveinthelandoftheory,andtheHumans,whoactintherealworld.Thetwoselves are the experiencingself, which does the living,and the remembering self,whichkeepsscoreandmakesthe choices. In this final

chapter I consider someapplications of the threedistinctions, taking them inreverseorder.

TwoSelvesThe possibility of conflictsbetweentherememberingselfand the interests of theexperiencing self turned outtobeaharderproblemthanIinitially thought. In an earlyexperiment, the cold-hand

study, the combination ofduration neglect and thepeak-end rule led to choicesthat were manifestly absurd.Why would people willinglyexpose themselves tounnecessary pain? Oursubjects left the choice totheir remembering self,preferring to repeat the trialthat left the better memory,although it involved morepain.Choosingbythequalityof the memory may be

justifiedinextremecases,forexamplewhenpost-traumaticstress is apossibility, but thecold-handexperiencewasnottraumatic. An objectiveobserver making the choicefor someone else wouldundoubtedlychoose the shortexposure, favoring thesufferer’s experiencing self.Thechoicesthatpeoplemadeontheirownbehalfarefairlydescribed as mistakes.Duration neglect and the

peak-end rule in theevaluation of stories, both attheoperaandinjudgmentsofJen’s life, are equallyindefensible.Itdoesnotmakesensetoevaluateanentirelifebyitslastmoments,ortogiveno weight to duration indeciding which life is moredesirable.The remembering self is a

construction of System 2.However, the distinctivefeatures of the way it

evaluates episodes and livesare characteristics of ourmemory. Duration neglectand the peak-end ruleoriginate inSystem1 anddonot necessarily correspond tothe values of System 2. Webelieve that duration isimportant, but our memorytellsusitisnot.Therulesthatgovern the evaluation of thepast are poor guides fordecision making, becausetimedoesmatter.Thecentral

fact of our existence is thattime is the ultimate finiteresource, but therememberingselfignoresthatreality. The neglect ofduration combined with thepeak-end rule causes a biasthat favors a short period ofintensejoyoveralongperiodof moderate happiness. Themirrorimageofthesamebiasmakes us fear a short periodof intense but tolerablesufferingmorethanwefeara

much longer period ofmoderate pain. Durationneglect also makes us proneto accept a long period ofmild unpleasantness becausethe endwill be better, and itfavors giving up anopportunity for a long happyperiodifitislikelytohaveapoor ending. To drive thesame idea to the point ofdiscomfort, consider thecommon admonition, “Don’tdo it,youwill regret it.”The

advice sounds wise becauseanticipated regret is theverdict of the rememberingself and we are inclined toaccept such judgments asfinal and conclusive. Weshould not forget, however,that the perspective of theremembering self is notalways correct. An objectiveobserver of the hedonimeterprofile, with the interests oftheexperiencingselfinmind,might well offer different

advice. The rememberingself’s neglect of duration, itsexaggerated emphasis onpeaks and ends, and itssusceptibility to hindsightcombine to yield distortedreflections of our actualexperience.In contrast, the duration-

weighted conception ofwell-being treats all moments oflife alike, memorable or not.Some moments end upweighted more than others,

either because they arememorable Sareeva orbecause they are important.The time that people spenddwelling on a memorablemoment should be includedin its duration, adding to itsweight. A moment can alsogain importance by alteringthe experience of subsequentmoments. For example, anhour spent practicing theviolin may enhance theexperience ofmany hours of

playing or listening tomusicyears later. Similarly, a briefawfuleventthatcausesPTSDshould be weighted by thetotal duration of the long-termmisery it causes. In theduration-weightedperspective, we candetermine only after the factthat a moment is memorableor meaningful. Thestatements “I will alwaysremember…” or “this is ameaningful moment” should

be taken as promises orpredictions, which can befalse—and often are—evenwhen uttered with completesincerity.Itisagoodbetthatmanyofthethingswesaywewillalwaysrememberwillbelongforgottentenyearslater.The logic of duration

weighting is compelling, butit cannot be considered acompletetheoryofwell-beingbecause individuals identifywith their remembering self

and care about their story.Atheory of well-being thatignores what people wantcannot be sustained. On theother hand, a theory thatignoreswhatactuallyhappensin people’s lives and focusesexclusively on what theythink about their life is nottenable either. Theremembering self and theexperiencing self must bothbe considered, because theirinterests do not always

coincide. Philosophers couldstruggle with these questionsforalongtime.The issue of which of the

two selves matters more isnot a question only forphilosophers; it hasimplications for policies inseveral domains, notablymedicine and welfare.Consider the investment thatshould be made in thetreatment of various medicalconditions, including

blindness,deafness,orkidneyfailure. Should theinvestmentsbedeterminedbyhow much people fear theseconditions? Shouldinvestmentsbeguidedby thesuffering that patientsactually experience? Orshould they follow theintensity of the patients’desire to be relieved fromtheir condition and by thesacrifices that they would bewilling to make to achieve

that relief? The ranking ofblindness anddeafness, or ofcolostomyanddialysis,mightwell be different dependingon which measure of theseverity of suffering is used.No easy solution is in sight,but the issue is too importanttobeignored.The possibility of using

measures of well-being asindicators to guidegovernment policies hasattracted considerable recent

interest, both amongacademics and in severalgovernments in Europe. It isnow conceivable, as it wasnotevenafewyearsago,thatan index of the amount ofsuffering in society willsomeday be included innational statistics, alongwithmeasures of unemployment,physical disability, andincome. This project hascomealongway.

EconsandHumansIn everyday speech, we callpeople reasonable if it ispossible to reasonwith them,iftheirbeliefsaregenerallyintunewith reality, and if theirpreferences are in line withtheir interests and theirvalues. The word rationalconveys an image of greaterdeliberation, morecalculation, and lesswarmth,but in common language a

rational person is certainlyreasonable. For economistsand decision theorists, theadjective has an altogetherdifferent meaning. The onlytest of rationality is notwhether a person’s beliefsand preferences arereasonable, but whether theyare internally consistent. Arationalpersoncanbelieveinghostssolongasallherotherbeliefsareconsistentwiththeexistence of ghosts. A

rational person can preferbeinghatedoverbeingloved,so long as hi Sso as allspreferences are consistent.Rationality is logicalcoherence—reasonable ornot.Econsarerationalbythisdefinition, but there isoverwhelming evidence thatHumans cannot be.An Econwould not be susceptible topriming, WYSIATI, narrowframing, the inside view, orpreference reversals, which

Humans cannot consistentlyavoid.Thedefinitionof rationality

as coherence is impossiblyrestrictive; it demandsadherence to rules of logicthat a finitemind is not ableto implement. Reasonablepeople cannot be rational bythat definition, but theyshould not be branded asirrational for that reason.Irrational is a strong word,which connotes impulsivity,

emotionality, and a stubbornresistance to reasonableargument.Ioftencringewhenmy work with Amos iscredited with demonstratingthat human choices areirrational, when in fact ourresearch only showed thatHumans are not welldescribed by the rational-agentmodel.Although Humans are not

irrational, they often needhelp to make more accurate

judgments and betterdecisions, and in some casespolicies and institutions canprovide that help. Theseclaims may seem innocuous,but they are in fact quitecontroversial. As interpretedby the important Chicagoschool of economics, faith inhuman rationality is closelylinked to an ideology inwhich it is unnecessary andeven immoral to protectpeople against their choices.

Rational people should befree, and they should beresponsiblefortakingcareofthemselves.MiltonFriedman,the leading figure in thatschool,expressedthisviewinthetitleofoneofhispopularbooks:FreetoChoose.The assumption that agents

are rational provides theintellectualfoundationforthelibertarianapproach topublicpolicy: do not interfere withthe individual’s right to

choose, unless the choicesharm others. Libertarianpolicies are further bolsteredby admiration for theefficiency of markets inallocatinggoodstothepeoplewho are willing to pay themost for them. A famousexample of the ChicagoapproachistitledATheoryofRational Addiction; itexplainshowarationalagentwith a strong preference forintense and immediate

gratification may make therational decision to acceptfuture addiction as aconsequence. I once heardGary Becker, one of theauthorsofthatarticle,whoisalso a Nobel laureate of theChicago school, argue in alighter vein, but not entirelyas a joke, that we shouldconsider the possibility ofexplaining the so-calledobesity epidemic by people’sbelief thatacurefordiabetes

will soon become available.He was making a valuablepoint: when we observepeople acting in ways thatseem odd, we should firstexamine the possibility thattheyhaveagoodreasontodowhat they do. Psychologicalinterpretationsshouldonlybeinvoked when the reasonsbecome implausible—whichBecker’s explanation ofobesityprobablyis.In a nation of Econs,

government should keep outof the way, allowing theEcons to act as they choose,so long as they do not harmothers. If a motorcycle riderchooses to ride without ahelmet, a libertarian willsupport his right to do so.Citizens know what they aredoing,evenwhentheychoosenot to save for their old age,or when they exposethemselves to addictivesubstances. There is

sometimesahardedgetothisposition: elderly people whodid not save enough forretirement get little moresympathy than someonewhocomplainsaboutthebillafterconsuming a large meal at arestaurant. Much is thereforeatstakeinthedebatebetweenthe Chicago school and thebehavioral economists, whorejecttheextremeformoftherational-agent model.Freedom is not a contested

value; all the participants inthe debate are in favor of it.But life ismore complex forbehavioral economists thanfor tru S th17;e believers inhuman rationality. Nobehavioraleconomistfavorsastate that will force itscitizenstoeatabalanceddietand to watch only televisionprograms that are good forthe soul. For behavioraleconomists, however,freedom has a cost, which is

borne by individuals whomake bad choices, and by asocietythatfeelsobligatedtohelp them. The decision ofwhether or not to protectindividuals against theirmistakes therefore presents adilemma for behavioraleconomists. The economistsof theChicagoschooldonotface that problem, becauserational agents do not makemistakes. For adherents ofthisschool,freedomisfreeof

charge.In 2008 the economist

Richard Thaler and the juristCass Sunstein teamed up towrite a book, Nudge, whichquickly became aninternational bestseller andthe bible of behavioraleconomics. Their bookintroducedseveralnewwordsinto the language, includingEcons and Humans. It alsopresentedasetofsolutionstothe dilemma of how to help

people make good decisionswithout curtailing theirfreedom.ThalerandSunsteinadvocate a position oflibertarian paternalism, inwhich the state and otherinstitutions are allowed tonudge people to makedecisionsthatservetheirownlong-term interests. Thedesignation of joining apension plan as the defaultoption is an example of anudge. It is difficult to argue

that anyone’s freedom isdiminished by beingautomatically enrolled in theplan,when theymerely havetocheckaboxtooptout.Aswesawearlier,theframingofthe individual’s decision—Thaler and Sunstein call itchoice architecture—has ahuge effect on the outcome.Thenudge isbasedonsoundpsychology, which Idescribedearlier.Thedefaultoption is naturally perceived

as the normal choice.Deviating from the normalchoice is an act ofcommission, which requiresmore effortful deliberation,takes on more responsibility,and is more likely to evokeregret than doing nothing.These are powerful forcesthatmayguidethedecisionofsomeone who is otherwiseunsureofwhattodo.Humans, more than Econs,

also need protection from

others who deliberatelyexploit their weaknesses—and especially the quirks ofSystem 1 and the laziness ofSystem2.Rationalagentsareassumed to make importantdecisionscarefully,andtouseall the information that isprovided to them. An Econwill read and understand thefineprintofacontractbeforesigning it, but Humansusually do not. Anunscrupulous firm that

designs contracts thatcustomerswill routinely signwithout reading hasconsiderable legal leeway inhiding important informationin plain sight. A perniciousimplication of the rational-agent model in its extremeform is that customers areassumed to need noprotection beyond ensuringthat the relevant informationis disclosed. The size of theprint and the complexity of

thelanguageinthedisclosurearenotconsideredrelevant—an Econ knows how to dealwith small print when itmatters. In contrast, therecommendations of Nudgerequire firms to offercontracts that are sufficientlysimple to be read andunderstood by Humancustomers. It is a good signthat some of theserecommendations haveencountered significant

opposition from firms whoseprofits might suffer if theircustomers were betterinformed. A world in whichfirms compete by offeringbetter products is preferabletooneinwhichthewinneristhe firm that is best atobfuscation.A remarkable feature of

libertarian paternalism is itsappeal across a broadpolitical spectrum. Theflagship example of

behavioralpolicy,calledSaveMore Tomorrow, wassponsored in Congress by anunusual coalition thatincluded extremeconservatives as well asliberals. Save MoreTomorrow isa financialplanthat firms can offer theiremployees. Those who signon allow the employer toincrea Syers liberalse theircontribution to their savingplan by a fixed proportion

whenevertheyreceivearaise.The increased saving rate isimplemented automaticallyuntil the employee givesnotice that she wants to optout of it. This brilliantinnovation, proposed byRichard Thaler and ShlomoBenartzi in 2003, has nowimprovedthesavingsrateandbrightened the futureprospects of millions ofworkers. It is soundly basedin the psychological

principles that readersof thisbookwillrecognize.Itavoidsthe resistance to animmediate loss by requiringno immediate change; bytying increasedsaving topayraises, it turns losses intoforegone gains, which aremuch easier to bear; and thefeatureof automaticity alignsthelazinessofSystem2withthe long-term interests of theworkers. All this, of course,withoutcompellinganyoneto

doanythinghedoesnotwishto do and without anymisdirectionorartifice.The appeal of libertarian

paternalism has beenrecognizedinmanycountries,including the UK and SouthKorea, and by politicians ofmany stripes, includingTories and the Democraticadministration of PresidentObama. Indeed, Britain’sgovernment has created anewsmallunitwhosemission

is to apply the principles ofbehavioralsciencetohelpthegovernment betteraccomplish its goals. Theofficialnameforthisgroupisthe Behavioural InsightTeam,butitisknownbothinandoutofgovernmentsimplyas the NudgeUnit. Thaler isanadvisertothisteam.Inastorybooksequeltothe

writing of Nudge, Sunsteinwas invited by PresidentObama to serve as

administratoroftheOfficeofInformation and RegulatoryAffairs, a position that gavehim considerable opportunityto encourage the applicationof the lessons of psychologyand behavioral economics ingovernment agencies. Themission is described in the2010Report of theOffice ofManagement and Budget.Readers of this book willappreciate the logic behindspecific recommendations,

includingencouraging“clear,simple, salient, andmeaningful disclosures.”They will also recognizebackground statements suchas “presentation greatlymatters; if, for example, apotential outcome is framedas a loss, it may have moreimpact than if it is presentedasagain.”Theexampleofaregulation

about the framing ofdisclosures concerning fuel

consumption was mentionedearlier. Additionalapplications that have beenimplemented includeautomatic enrollment inhealth insurance, a newversion of the dietaryguidelines that replaces theincomprehensible FoodPyramid with the powerfulimageofaFoodPlateloadedwith a balanced diet, and aruleformulatedbytheUSDAthat permits the inclusion of

messages such as “90% fat-free” on the label of meatproducts, provided that thestatement “10% fat” is alsodisplayed “contiguous to, inlettering of the same color,size, and type as, and on thesame color background as,the statement of leanpercentage.” Humans, unlikeEcons, need help to makegooddecisions, and thereareinformed and unintrusivewaystoprovidethathelp.

TwoSystemsThis book has described theworkings of the mind as anuneasy interaction betweentwo fictitious characters: theautomatic System 1 and theeffortful System 2. You arenow quite familiar with thepersonalities of the twosystemsandabletoanticipatehow they might respond indifferent situations. And ofcourse you also remember

that the two systems do notreally exist in the brain oranywhere else. “System 1doesX” is a shortcut for “Xoccurs automatically.” And“System2 ismobilized todoY” is a shortcut for “arousalincreases, pupils dilate,attention is foStenations,cused,andactivityY is performed.” I hope youfind the language of systemsas helpful as I do, and thatyou have acquired an

intuitive sense of how theywork without gettingconfused by the question ofwhether they exist. Havingdelivered this necessarywarning, I will continue tousethelanguagetotheend.The attentive System 2 is

whowethinkweare.System2 articulates judgments andmakes choices, but it oftenendorsesor rationalizes ideasand feelings that weregenerated by System 1. You

may not know that you areoptimistic about a projectbecause something about itsleader reminds you of yourbeloved sister, or that youdislike a person who looksvaguely like your dentist. Ifasked for an explanation,however, you will searchyourmemory for presentablereasons and will certainlyfind some. Moreover, youwill believe the story youmakeup.ButSystem2isnot

merely an apologist forSystem 1; it also preventsmany foolish thoughts andinappropriate impulses fromovert expression. Theinvestment of attentionimproves performance innumerousactivities—thinkoftherisksofdrivingthroughanarrow space while yourmind is wandering—and isessential to some tasks,includingcomparison,choice,and ordered reasoning.

However, System 2 is not aparagon of rationality. Itsabilities are limited and so istheknowledgetowhichithasaccess. We do not alwaysthink straight when wereason,andtheerrorsarenotalways due to intrusive andincorrect intuitions.Oftenwemake mistakes because we(our System 2) do not knowanybetter.I have spent more time

describing System 1, and

have devoted many pages toerrors of intuitive judgmentand choice that I attribute toit. However, the relativenumber of pages is a poorindicator of the balancebetween the marvels and theflaws of intuitive thinking.System1is indeedtheoriginof much that we do wrong,but it is also the origin ofmost of what we do right—whichismostofwhatwedo.Our thoughts and actions are

routinelyguidedbySystem1and generally are on themark. One of the marvels istherichanddetailedmodelofour world that is maintainedin associative memory: itdistinguishes surprising fromnormaleventsinafractionofa second, immediatelygeneratesanideaofwhatwasexpectedinsteadofasurprise,and automatically searchesforsomecausalinterpretationof surprises and of events as

theytakeplace.Memoryalsoholds thevast

repertory of skills we haveacquired in a lifetime ofpractice,which automaticallyproduceadequatesolutionstochallengesastheyarise,fromwalking around a large stoneon the path to averting theincipient outburst of acustomer. The acquisition ofskills requires a regularenvironment, an adequateopportunity to practice, and

rapid and unequivocalfeedback about thecorrectness of thoughts andactions. When theseconditions are fulfilled, skilleventually develops, and theintuitive judgments andchoices that quickly come tomindwillmostlybeaccurate.AllthisistheworkofSystem1, which means it occursautomatically and fast. Amarker of skilledperformance is the ability to

deal with vast amounts ofinformation swiftly andefficiently.When a challenge is

encountered to which askilled response is available,thatresponseisevoked.Whathappens in the absence ofskill? Sometimes, as in theproblem 17 × 24 = ?, whichcalls for a specific answer, itis immediately apparent thatSystem 2 must be called in.But it is rare forSystem1 to

bedumbfounded.System1isnot constrained by capacitylimits and is profligate in itscomputations.Whenengagedinsearchingforananswer toone question, itsimultaneously generates theanswers to related questions,and it may substitute aresponse that more easilycomes to mind for the onethat was requested. In thisconception of heuSepttedristics, the heuristic

answer is not necessarilysimpler or more frugal thanthe original question—it isonly more accessible,computed more quickly andeasily. The heuristic answersare not random, and they areoften approximately correct.Andsometimestheyarequitewrong.System 1 registers the

cognitive ease with which itprocesses information, but itdoes not generate a warning

signal when it becomesunreliable. Intuitive answerscome to mind quickly andconfidently, whether theyoriginate from skills or fromheuristics.Thereisnosimpleway for System 2 todistinguish between a skilledand a heuristic response. Itsonlyrecourseistoslowdownand attempt to construct anansweronitsown,whichitisreluctant to do because it isindolent. Many suggestions

of System 1 are casuallyendorsed with minimalchecking, as in the bat-and-ball problem. This is howSystem 1 acquires its badreputation as the source oferrors and biases. Itsoperative features, whichinclude WYSIATI, intensitymatching, and associativecoherence, among others,giverisetopredictablebiasesand to cognitive illusionssuch as anchoring,

nonregressive predictions,overconfidence, andnumerousothers.What can be done about

biases?Howcanweimprovejudgments and decisions,bothourownandthoseoftheinstitutions thatweserveandthat serve us? The shortanswer is that little can beachieved without aconsiderable investment ofeffort. As I know fromexperience, System 1 is not

readily educable. Except forsome effects that I attributemostly to age, my intuitivethinking is just as prone tooverconfidence, extremepredictions, and the planningfallacy as it was before Imadea studyof these issues.I have improved only in myability to recognizesituationsin which errors are likely:“This number will be ananchor…,” “The decisioncould change if the problem

is reframed…” And I havemademuchmoreprogress inrecognizing the errors ofothersthanmyown.Thewaytoblockerrorsthat

originate in System 1 issimpleinprinciple:recognizethe signs that you are in acognitive minefield, slowdown, and ask forreinforcementfromSystem2.This ishowyouwillproceedwhen you next encounter theMüller-Lyer illusion. When

you see lines with finspointing in differentdirections,youwillrecognizethe situation as one inwhichyou should not trust yourimpressions of length.Unfortunately, this sensibleprocedureisleastlikelytobeapplied when it is neededmost. We would all like tohaveawarningbellthatringsloudlywheneverweareaboutto make a serious error, butnosuchbell isavailable, and

cognitive illusions aregenerally more difficult torecognize than perceptualillusions.Thevoiceofreasonmaybemuchfainterthantheloud and clear voice of anerroneous intuition, andquestioning your intuitions isunpleasantwhenyoufacethestressofabigdecision.Moredoubt is the last thing youwantwhenyouareintrouble.Theupshot is that it ismucheasier to identify aminefield

when you observe otherswandering into it than whenyou are about to do so.Observersarelesscognitivelybusy and more open toinformation than actors.Thatwas my reason for writing abookthatisorientedtocriticsand gossipers rather than todecisionmakers.Organizations are better

than individuals when itcomes to avoiding errors,because they naturally think

more slowly and have thepower to impose orderlyprocedures. Organizationscan institute and enforce theapplication of usefulchecklists, as well as moreelaborate exercises, such asreference-class forecastingand the premortem. At leastin part by providing adistinctive vocabulary,organizations can alsoencourage a culture inwhichpeople watch out for one

another as they approachminefields. Whatever else itproduces, a St pof othersnorganization is a factory thatmanufactures judgments anddecisions.Everyfactorymusthave ways to ensure thequality of its products in theinitial design, in fabrication,and in final inspections. Thecorresponding stages in theproduction of decisions arethe framing of the problemthat is to be solved, the

collection of relevantinformation leading to adecision, and reflection andreview. An organization thatseeks to improve its decisionproductshouldroutinelylookfor efficiency improvementsat each of these stages. Theoperative concept is routine.Constantqualitycontrolisanalternative to the wholesalereviews of processes thatorganizations commonlyundertake in the wake of

disasters.Thereismuchtobedone to improve decisionmaking. One example out ofmany is the remarkableabsenceofsystematictrainingfor the essential skill ofconducting efficientmeetings.Ultimately, a richer

language is essential to theskillofconstructivecriticism.Much like medicine, theidentification of judgmenterrors is a diagnostic task,

which requires a precisevocabulary. The name of adiseaseisahooktowhichallthat is known about thedisease is attached, includingvulnerabilities,environmentalfactors,symptoms,prognosis,and care. Similarly, labelssuch as “anchoring effects,”“narrow framing,” or“excessive coherence” bringtogether in memoryeverything we know about abias, its causes, its effects,

and what can be done aboutit.There is a direct link from

more precise gossip at thewatercooler to betterdecisions. Decision makersare sometimes better able toimaginethevoicesofpresentgossipers and future criticsthantohearthehesitantvoiceof their own doubts. Theywill make better choiceswhentheytrusttheircriticstobesophisticatedandfair,and

when they expect theirdecisiontobejudgedbyhowitwasmade,notonlybyhowitturnedout.

P

AppendixA:JudgmentUnderUncertainty:Heuristicsand

Biases*

AmosTverskyandDanielKahneman

Many decisions are based onbeliefs concerning thelikelihoodofuncertaineventssuch as the outcome of anelection, the guilt of adefendant,orthefuturevalueof the dollar. These beliefsare usually expressed instatements such as “I thinkthat…,” “chances are…,” “itis unlikely that…,” and soforth. Occasionally, beliefsconcerning uncertain eventsare expressed in numerical

form as odds or subjectiveprobabilities. Whatdeterminessuchbeliefs?Howdo people assess theprobability of an uncertainevent or the value of anuncertain quantity? Thisarticleshowsthatpeoplerelyon a limited number ofheuristic principles whichreduce the complex tasks ofassessing probabilities andpredicting values to simplerjudgmental operations. In

general, these heuristics arequite useful, but sometimesthey lead to severe andsystematicerrors.The subjective assessment

of probability resembles thesubjective assessment ofphysical quantities such asdistance or size. Thesejudgments are all based ondataoflimitedvalidity,whichare processed according toheuristic rules. For example,the apparent distance of an

object is determined in partby its clarity. The moresharplytheobjectisseen,thecloser it appears to be. Thisrule has some validity,because in any given scenethe more distant objects areseen less sharply than Vtpofreak/>stimated whenvisibility isgoodbecause theobjects are seen sharply.Thus, the reliance on clarityas an indication of distanceleadstocommonbiases.Such

biases are also found in theintuitive judgment ofprobability. This articledescribes threeheuristics thatare employed to assessprobabilities and to predictvalues.Biases towhich theseheuristics lead areenumerated, and the appliedand theoretical implicationsof these observations arediscussed.

Representativeness

Many of the probabilisticquestions with which peopleare concerned belong to oneof the following types:Whatis the probability that objectAbelongstoclassB?Whatisthe probability that event Aoriginates from process B?What is the probability thatprocessBwillgenerateeventA? In answering suchquestions, people typicallyrelyontherepresentativeness

heuristic, in whichprobabilitiesareevaluatedbythe degree to which A isrepresentativeofB,thatis,bythe degree to which Aresembles B. For example,when A is highlyrepresentative of B, theprobability that A originatesfromB is judged to be high.Ontheotherhand,ifAisnotsimilar to B, the probabilitythat A originates from B isjudgedtobelow.

For an illustration ofjudgment byrepresentativeness, consideran individual who has beendescribed by a formerneighborasfollows:“Steveisvery shy and withdrawn,invariably helpful, but withlittle interest in people, or intheworld of reality.Ameekand tidy soul, he has a needfororderandstructure,andapassion for detail.” How dopeople assess the probability

that Steve is engaged in aparticular occupation from alist of possibilities (forexample, farmer, salesman,airline pilot, librarian, orphysician)? How do peopleorder these occupations frommost to least likely? In therepresentativeness heuristic,theprobabilitythatSteveisalibrarian, for example, isassessed by the degree towhichheisrepresentativeof,orsimilarto,thestereotypeof

a librarian. Indeed, researchwithproblemsofthistypehasshown that people order theoccupations by probabilityand by similarity in exactlythesameway.1Thisapproachtothejudgmentofprobabilityleads to serious errors,because similarity, orrepresentativeness, is notinfluenced by several factorsthat should affect judgmentsofprobability.

Insensitivity to priorprobability of outcomes. Oneof the factors that have noeffect on representativenessbut should have a majoreffect on probability is thepriorprobability,orbaseratefrequency, of the outcomes.In the case of Steve, forexample, the fact that thereare many more farmers thanlibrarians in the populationshould enter into anyreasonable estimate of the

probability that Steve is alibrarianratherthanafarmer.Considerations of base-ratefrequency, however, do notaffect the similarity of Steveto the stereotypes oflibrarians and farmers. Ifpeople evaluate probabilityby representativeness,therefore, prior probabilitieswill be neglected. Thishypothesis was tested in anexperiment where priorprobabilities were

manipulated.2 Subjects wereshown brief personalitydescriptions of severalindividuals, allegedlysampled at random from agroup of 100 professionals—engineers and lawyers. Thesubjectswereaskedtoassess,for each description, theprobabilitythatitbelongedtoan engineer rather than to alawy [hanerser. In oneexperimental condition,

subjects were told that thegroup from which thedescriptions had been drawnconsistedof70engineersand30 lawyers. In anothercondition, subjects were toldthatthegroupconsistedof30engineers and 70 lawyers.The odds that any particulardescription belongs to anengineer rather than to alawyer should be higher inthe first condition, wherethere is a majority of

engineers, than in the secondcondition, where there is amajority of lawyers.Specifically, it can be shownby applying Bayes’ rule thattheratiooftheseoddsshouldbe (.7/.3)2, or 5.44, for eachdescription. In a sharpviolation of Bayes’ rule, thesubjectsinthetwoconditionsproducedessentiallythesameprobability judgments.Apparently, subjects

evaluatedthelikelihoodthataparticular descriptionbelonged to an engineerratherthantoalawyerbythedegree to which thisdescription wasrepresentative of the twostereotypes, with little or noregard for the priorprobabilities of thecategories.The subjects used prior

probabilities correctly whenthey had no other

information.Intheabsenceofa personality sketch, theyjudgedtheprobabilitythatanunknown individual is anengineer to be .7 and .3,respectively, in the twobase-rate conditions. However,prior probabilities wereeffectively ignored when adescription was introduced,even when this descriptionwas totally uninformative.The responses to thefollowing description

illustratethisphenomenon:

Dick is a 30-year-oldman.He ismarriedwithno children. A man ofhigh ability and highmotivation, he promisestobequite successful inhisfield.Heiswelllikedbyhiscolleagues.

Thisdescriptionwasintendedto convey no informationrelevant to the question of

whether Dick is an engineeror a lawyer. Consequently,theprobabilitythatDickisanengineer should equal theproportionofengineersinthegroup, as if no descriptionhadbeengiven.Thesubjects,however, judged theprobability of Dick being anengineertobe.5regardlessofwhether the statedproportionofengineersinthegroupwas.7 or .3. Evidently, peoplerespond differently when

given no evidence and whengiven worthless evidence.Whennospecificevidenceisgiven, prior probabilities areproperly utilized; whenworthless evidence is given,prior probabilities areignored.3Insensitivity to sample size.

Toevaluatetheprobabilityofobtainingaparticularresultina sample drawn from aspecified population, people

typically apply therepresentativeness heuristic.That is, they assess thelikelihoodofa sample result,forexample, that theaverageheight in a random sampleoften men will be 6 feet, bythesimilarityof this result tothe corresponding parameter(that is, to theaverageheightin the population of men).The similarity of a samplestatistic to a populationparameterdoesnotdependon

the size of the sample.Consequently, ifprobabilitiesare assessed byrepresentativeness, then thejudged probability of asample statistic will beessentially independent ofsample size. Indeed, whensubjects assessed thedistributions of averageheight forsamplesofvarioussizes,theyproducedidenticaldistributions. For example,the probability of obtaining

anaverageheightgreaterthan6 feetwasassigned the samevalue for samples of 1,000,100,and10men.4Moreover,subjects failed to appreciatethe role of sample size evenwhen it was emphasized inthe formulation of theproblem. Consider thefollowingquestion:

A certain town is s[ainquote wierved bytwo hospitals. In the

larger hospital about 45babies are born eachday, and in the smallerhospital about 15 babiesare born each day. Asyouknow,about50%ofall babies are boys.However, the exactpercentage varies fromdaytoday.Sometimes it may behigher than 50%,sometimeslower.For a period of 1 year,

each hospital recordedthedaysonwhichmorethan 60% of the babiesborn were boys. Whichhospital do you thinkrecorded more suchdays?Thelargerhospital(21)The smaller hospital(21)Aboutthesame(thatis,within5%ofeachother)(53)

Thevaluesinparenthesesarethe number of undergraduatestudents who chose eachanswer.Most subjects judged the

probabilityofobtainingmorethan60%boystobethesamein the small and in the largehospital, presumably becausetheseeventsaredescribedbythe same statistic and aretherefore equallyrepresentative of the generalpopulation. In contrast,

sampling theory entails thatthe expected number of dayson which more than 60% ofthe babies are boys is muchgreater in the small hospitalthaninthelargeone,becausealargesampleislesslikelytostray from 50%. Thisfundamental notion ofstatistics isevidentlynotpartof people’s repertoire ofintuitions.A similar insensitivity to

samplesizehasbeenreported

in judgments of posteriorprobability, that is, of theprobability that a sample hasbeen drawn from onepopulation rather than fromanother. Consider thefollowingexample:

Imagine an urn filledwith balls, ofwhich 2/3areofonecolorand1/3of another. Oneindividual has drawn 5balls from the urn, and

found that 4 were redand 1 was white.Another individual hasdrawn 20 balls andfound that 12 were redand8werewhite.Whichof the two individualsshould feel moreconfident that the urncontains 2/3 red ballsand 1/3 white balls,ratherthantheopposite?What odds should eachindividualgive?

In this problem, the correctposterior odds are 8 to 1 forthe4:1sampleand16to1forthe 12:8 sample, assumingequal prior probabilities.However, most people feelthat the first sampleprovidesmuch stronger evidence forthehypothesis that theurn ispredominantly red, becausethe proportion of red balls islarger in the first than in thesecond sample. Here again,

intuitive judgments aredominated by the sampleproportionandareessentiallyunaffected by the size of thesample,whichplaysacrucialrole in the determination ofthe actual posterior odds.5 Inaddition, intuitive estimatesof posterior odds are far lessextreme than the correctvalues. The underestimationoftheimpactofevidencehasbeen observed repeatedly in

problemsof this type.6 It hasbeenlabeled“conservatism.”Misconceptions of chance.

Peopleexpectthatasequenceof events generated by arandomprocesswillrepresenttheessentialcharacteristicsofthat process even when thesequence is short. Inconsidering tosses of a coinfor heads or tails, forexample, people regard thesequenceH-T-H-T-T-Htobe

morelikelythanthesequenceH-H-H-T- [enc. IT-T, whichdoes not appear random, andalso more likely than thesequence H-H-H-H-T-H,which does not represent thefairness of the coin.7 Thus,people expect that theessentialcharacteristicsoftheprocess will be represented,notonlygloballyintheentiresequence, but also locally ineach of its parts. A locally

representative sequence,however, deviatessystematically from chanceexpectation: it contains toomany alternations and toofew runs. Anotherconsequence of the belief inlocalrepresentativenessisthewell-known gambler’sfallacy. After observing alongrunofredontheroulettewheel, for example, mostpeople erroneously believethat black is now due,

presumably because theoccurrence of black willresult in a morerepresentative sequence thanthe occurrence of anadditional red. Chance iscommonly viewed as a self-correctingprocessinwhichadeviation in one directioninduces a deviation in theopposite direction to restorethe equilibrium. In fact,deviationsarenot“corrected”as a chance process unfolds,

theyaremerelydiluted.Misconceptions of chance

are not limited to naivesubjects. A study of thestatistical intuitions ofexperienced researchpsychologists8 revealed alingering belief in what maybe called the “law of smallnumbers,”accordingtowhichevensmallsamplesarehighlyrepresentative of thepopulations from which they

are drawn. The responses ofthese investigators reflectedthe expectation that a validhypothesisaboutapopulationwill be represented by astatistically significant resultin a samplewith little regardforitssize.Asaconsequence,the researchers put toomuchfaith in the results of smallsamples and grosslyoverestimated thereplicability of such results.In the actual conduct of

research,thisbiasleadstotheselection of samples ofinadequate size and tooverinterpretationoffindings.Insensitivity topredictability. People aresometimes called upon tomake such numericalpredictionsasthefuturevalueof a stock, the demand for acommodity, or the outcomeof a football game. Suchpredictionsareoftenmadebyrepresentativeness. For

example, suppose one isgiven a description of acompany and is asked topredictitsfutureprofit.Ifthedescriptionofthecompanyisvery favorable, a very highprofit will appear mostrepresentative of thatdescription; if thedescriptionis mediocre, a mediocreperformancewillappearmostrepresentative. The degree towhich the description isfavorableisunaffectedbythe

reliability of that descriptionor by the degree to which itpermits accurate prediction.Hence, if people predictsolely in terms of thefavorableness of thedescription, their predictionswill be insensitive to thereliabilityoftheevidenceandto the expected accuracy oftheprediction.This mode of judgment

violates the normativestatisticaltheoryinwhichthe

extremenessand therangeofpredictions are controlled byconsiderations ofpredictability. Whenpredictability is nil, the sameprediction shouldbemade inall cases.For example, if thedescriptions of companiesprovide no informationrelevant to profit, then thesame value (such as averageprofit)shouldbepredictedforall companies. Ifpredictability is perfect, of

course, the values predictedwill match the actual valuesand the range of predictionswill equal the range ofoutcomes. In general, thehigher the predictability, thewider the range of predictedvalues.Severalstudiesofnumerical

predictionhavedemonstratedthat intuitive predictionsviolate this rule, and thatsubjects show little or noregard for considerations of

predictability.9Inoneo[pandtfthesestudies,subjectswerepresented with severalparagraphs, each describingthe performance of a studentteacher during a particularpractice lesson. Somesubjects were asked toevaluate the quality of thelesson described in theparagraph in percentilescores, relative to a specifiedpopulation. Other subjects

wereaskedtopredict,alsoinpercentilescores,thestandingof each student teacher 5yearsafterthepracticelesson.The judgments made underthe two conditions wereidentical. That is, theprediction of a remotecriterion(successofateacherafter5years)wasidenticaltothe evaluation of theinformation on which theprediction was based (thequalityofthepracticelesson).

Thestudentswhomadethesepredictionswereundoubtedlyaware of the limitedpredictability of teachingcompetenceon thebasisofasingle trial lesson 5 yearsearlier; nevertheless, theirpredictions were as extremeastheirevaluations.The illusion of validity. As

we have seen, people oftenpredict by selecting theoutcome (for example, anoccupation) that is most

representative of the input(for example, the descriptionof a person). The confidencethey have in their predictiondepends primarily on thedegree of representativeness(that is, on the quality of thematch between the selectedoutcome and the input) withlittle or no regard for thefactors that limit predictiveaccuracy. Thus, peopleexpress great confidence inthepredictionthatapersonis

a librarian when given adescription of his personalitywhichmatches thestereotypeof librarians, even if thedescription is scanty,unreliable, or outdated. Theunwarranted confidencewhich isproducedbyagoodfit between the predictedoutcome and the inputinformationmaybecalledtheillusion of validity. Thisillusion persists even whenthe judge is aware of the

factorsthatlimittheaccuracyof his predictions. It is acommon observation thatpsychologists who conductselection interviews oftenexperience considerableconfidence in theirpredictions, even when theyknow of the vast literaturethat shows selectioninterviews to be highlyfallible. The continuedreliance on the clinicalinterview for selection,

despite repeateddemonstrations of itsinadequacy, amply attests tothestrengthofthiseffect.The internal consistency of

apatternof inputs isamajordeterminant of one’sconfidence in predictionsbased on these inputs. Forexample, people expressmoreconfidenceinpredictingthe final grade point averageof a student whose first-yearrecordconsistsentirelyofB’s

than in predicting the gradepoint average of a studentwhose first-year recordincludes many A’s and C’s.Highlyconsistentpatternsaremostoftenobservedwhentheinput variables are highlyredundant or correlated.Hence, people tend to havegreat confidence inpredictions based onredundant input variables.However, an elementaryresult in the statistics of

correlationasserts that, giveninput variables of statedvalidity,apredictionbasedonseveral such inputs canachievehigheraccuracywhenthey are independent of eachother than when they areredundantorcorrelated.Thus,redundancy among inputsdecreasesaccuracyevenas itincreases confidence, andpeople are often confident inpredictions that are quite

likelytobeoffthemark.10Misconceptions ofregression. Suppose a largegroup of children has beenexamined on two equivalentversionsofanaptitudetest.Ifone selects ten children fromamongthosewhodidbestonone of the two versions, hewill usually find theirperformance on the secondversion to be somewhatdisappointing. Conversely, if

one selects ten children fromamong those who did worston one version, they will befound, on the average, to dosomewhatbetteron theotherversion. Mo [r vs tregenerally, consider twovariablesXandYwhichhavethe same distribution. If oneselects individuals whoseaverageXscoredeviatesfromthemeanofXbykunits,thenthe average of theirY scoreswill usually deviate from the

mean of Y by less than kunits. These observationsillustrate a generalphenomenon known asregression toward the mean,which was first documentedby Galton more than 100yearsago.Inthenormalcourseoflife,

one encounters manyinstances of regressiontoward the mean, in thecomparison of the height offathers and sons, of the

intelligence of husbands andwives, or of the performanceof individualsonconsecutiveexaminations. Nevertheless,peopledonotdevelopcorrectintuitions about thisphenomenon. First, they donotexpectregressioninmanycontextswhere it isbound tooccur. Second, when theyrecognize the occurrence ofregression, they often inventspurious causal explanations

for it.11 We suggest that thephenomenon of regressionremains elusive because it isincompatible with the beliefthat the predicted outcomeshould be maximallyrepresentative of the input,and, hence, that the value ofthe outcome variable shouldbeasextremeas thevalueoftheinputvariable.The failure to recognize the

importofregressioncanhave

pernicious consequences, asillustrated by the followingobservation.12Inadiscussionofflighttraining,experiencedinstructors noted that praisefor an exceptionally smoothlanding is typically followedby a poorer landing on thenexttry,whileharshcriticismafter a rough landing isusually followed by animprovementon thenext try.The instructors concluded

that verbal rewards aredetrimentaltolearning,whileverbal punishments arebeneficial, contrary toaccepted psychologicaldoctrine. This conclusion isunwarranted because of thepresenceofregressiontowardthemean.Asinothercasesofrepeated examination, animprovement will usuallyfollow a poor performanceand a deterioration willusuallyfollowanoutstanding

performance, even if theinstructordoesnotrespondtothe trainee’s achievement onthe first attempt.Because theinstructors had praised theirtrainees after good landingsand admonished them afterpoor ones, they reached theerroneous and potentiallyharmful conclusion thatpunishment ismore effectivethanreward.Thus, the failure to

understand the effect of

regression leads one tooverestimatetheeffectivenessof punishment and tounderestimate theeffectiveness of reward. Insocial interaction, as well asin training, rewards aretypically administered whenperformance is good, andpunishments are typicallyadministered whenperformance is poor. Byregression alone, therefore,behavior is most likely to

improveafterpunishmentandmostlikelytodeteriorateafterreward. Consequently, thehumanconditionissuchthat,by chance alone,one ismostoften rewarded for punishingothers and most oftenpunishedforrewardingthem.People are generally notawareof thiscontingency. Infact, the elusive role ofregression in determining theapparent consequences ofrewardandpunishmentseems

tohaveescaped thenoticeofstudentsofthisarea.

AvailabilityThereare situations inwhichpeople assess the frequencyofaclassortheprobabilityofan event by the ease withwhich instances oroccurrencescanbebroughttomind. For example, onemayassesstheriskofheartattackamong middle-aged people

byrecallingsuchoccurrencesa [occpunishmentmong one’sacquaintances. Similarly, onemay evaluate the probabilitythat a given business venturewillfailbyimaginingvariousdifficultiesitcouldencounter.This judgmental heuristic iscalled availability.Availability is a useful cluefor assessing frequency orprobability,becauseinstancesof large classes are usuallyrecalledbetterandfasterthan

instances of less frequentclasses.However,availabilityis affected by factors otherthan frequency andprobability. Consequently,the reliance on availabilityleads to predictable biases,someofwhichare illustratedbelow.Biases due to theretrievability of instances.When the size of a class isjudged by the availability ofits instances, a class whose

instances are easily retrievedwill appear more numerousthan a class of equalfrequency whose instancesare less retrievable. In anelementary demonstration ofthis effect, subjects heard alist of well-knownpersonalities of both sexesandwere subsequently askedto judge whether the listcontainedmorenamesofmenthanofwomen.Differentlistswere presented to different

groupsofsubjects.Insomeofthe lists the men wererelatively more famous thanthewomen,and inothers thewomen were relatively morefamousthanthemen.Ineachof the lists, the subjectserroneously judged that theclass (sex) that had themorefamous personalities was themorenumerous.13In addition to familiarity,

there are other factors, such

as salience, which affect theretrievabilityofinstances.Forexample,theimpactofseeinga house burning on thesubjectiveprobabilityofsuchaccidents is probably greaterthan the impact of readingaboutafireinthelocalpaper.Furthermore, recentoccurrences are likely to berelativelymoreavailablethanearlier occurrences. It is acommon experience that thesubjective probability of

traffic accidents risestemporarily when one sees acaroverturnedby the sideoftheroad.Biases due to theeffectiveness of a search set.Supposeone samples aword(of three letters or more) atrandomfromanEnglishtext.Isitmorelikelythatthewordstarts with r or that r is thethird letter? People approachthis problem by recallingwords that begin with r

(road) andwords that have rinthethirdposition(car)andassess the relative frequencybytheeasewithwhichwordsof the two types come tomind. Because it is mucheasier to search forwordsbytheir first letter than by theirthirdletter,mostpeoplejudgewordsthatbeginwithagivenconsonant to be morenumerous than words inwhich the same consonantappears in the third position.

They do so even forconsonants, such as r or k,that aremore frequent in thethird position than in thefirst.14Different tasks elicit

different search sets. Forexample, suppose you areasked to rate the frequencywith which abstract words(thought, love) and concretewords(door,water)appearinwritten English. A natural

waytoanswerthisquestionisto search for contexts inwhichthewordcouldappear.It seems easier to think ofcontexts inwhichanabstractconceptismentioned(loveinlove stories) than to think ofcontexts in which a concreteword (such as door) ismentioned. If the frequencyof words is judged by theavailabilityof thecontexts inwhich they appear, abstractwords will be judged as

relatively more numerousthan concrete words. Thisbias has been observed in arecent study15 which showedthat the judged frequency ofoccurrence of abstract wordswasmuchhigherthanthatofconcrete words, equated inobjective frequency.Abstractwords were also judged toappear in a much greatervariety of contexts thanconcretewords.

Biases of imaginability.Sometimes one has to assessthe frequency of a classwhose instances are notstored inmemory but can begenerated according to agivenrule.Insuchsituations,one typically generatesseveral instances andevaluates frequency orprobability by the ease withwhich the relevant instancescanbeconstructed.However,the ease of constructing

instances does not alwaysreflect theiractual frequency,andthismodeofevaluationisprone tobiases.To illustrate,consideragroupof10peoplewho form committees of kmembers, 2 = k= 8. Howmanydifferentcommitteesofk members can be formed?The correct answer to thisproblem is given by thebinomial coefficient (10/k)whichreachesamaximumof252 for k= 5. Clearly, the

number of committees of kmembers equals the numberof committees of (10 – k)members, because anycommittee of k membersdefinesauniquegroupof(10–k)nonmembers.One way to answer this

questionwithoutcomputationis to mentally constructcommitteesofkmembersandto evaluate their number bythe ease with which theycometomind.Committeesof

fewmembers,say2,aremoreavailable than committees ofmany members, say 8. Thesimplest scheme for theconstructionofcommittees isa partition of the group intodisjointsets.Onereadilyseesthatitiseasytoconstructfivedisjoint committees of 2members, while it isimpossible to generate eventwo disjoint committees of 8members. Consequently, iffrequency is assessed by

imaginability, or byavailability for construction,the small committees willappear more numerous thanlargercommittees,incontrastto the correct bell-shapedfunction. Indeed,when naivesubjects were asked toestimate the number ofdistinctcommitteesofvarioussizes, their estimates were adecreasing monotonicfunction of committee size.16

For example, the medianestimate of the number ofcommittees of 2 memberswas70,whiletheestimateforcommittees of 8 memberswas20(thecorrectansweris45inbothcases).Imaginability plays an

important role in theevaluation of probabilities inreal-life situations. The riskinvolved in an adventurousexpedition, for example, isevaluated by imagining

contingencieswithwhich theexpedition isnotequipped tocope. If many suchdifficulties are vividlyportrayed, the expedition canbe made to appearexceedingly dangerous,although theeasewithwhichdisasters are imagined neednot reflect their actuallikelihood. Conversely, therisk involved in anundertaking may be grosslyunderestimated if some

possible dangers are eitherdifficult to conceive of, orsimplydonotcometomind.Illusory correlation.

Chapman and Chapman17have described an interestingbias in the judgment of thefrequency with which twoevents co-occur. Theypresented naive judges withinformation concerningseveral hypothetical mentalpatients. The data for each

patientconsistedofaclinicaldiagnosis andadrawingof aperson made by the patient.Laterthejudgesestimatedthefrequency with which eachdiagnosis(suchasparanoiaorsuspiciousness) had beenaccompanied by variousfeaturesof thedrawing(suchas peculiar eyes). Thesubjects markedlyoverestimated the frequencyof [ frpici co-occurrence ofnatural associates, such as

suspiciousness and peculiareyes. This effectwas labeledillusory correlation. In theirerroneous judgments of thedata to which they had beenexposed, naive subjects“rediscovered” much of thecommon, but unfounded,clinical lore concerning theinterpretation of the draw-a-person test. The illusorycorrelation effect wasextremely resistant tocontradictory data. It

persisted even when thecorrelationbetweensymptomand diagnosis was actuallynegative,anditpreventedthejudges from detectingrelationshipsthatwereinfactpresent.Availability provides a

natural account for theillusory-correlation effect.The judgment of howfrequently two events co-occur could be based on thestrength of the associative

bond between them. Whenthe association is strong, oneis likely to conclude that theevents have been frequentlypaired. Consequently, strongassociates will be judged tohave occurred togetherfrequently. According to thisview, the illusory correlationbetween suspiciousness andpeculiar drawingof the eyes,forexample,isduetothefactthat suspiciousness is morereadily associated with the

eyes thanwithanyotherpartofthebody.Lifelong experience has

taught us that, in general,instances of large classes arerecalledbetterandfasterthaninstances of less frequentclasses; that likelyoccurrences are easier toimagine than unlikely ones;and that the associativeconnections between eventsare strengthened when theevents frequently co-occur.

As a result, man has at hisdisposal a procedure (theavailability heuristic) forestimating the numerosity ofa class, the likelihood of anevent,orthefrequencyofco-occurrences,bytheeasewithwhich the relevant mentaloperations of retrieval,construction, or associationcan be performed. However,as the preceding exampleshave demonstrated, thisvaluableestimationprocedure

resultsinsystematicerrors.

AdjustmentandAnchoring

In many situations, peoplemake estimates by startingfrom an initial value that isadjusted to yield the finalanswer. The initial value, orstarting point, may besuggested by the formulationof the problem, or itmay bethe result of a partial

computation. In either case,adjustments are typicallyinsufficient.18 That is,different starting points yielddifferentestimates,whicharebiased toward the initialvalues. We call thisphenomenonanchoring.Insufficientadjustment. Ina

demonstration of theanchoring effect, subjectswere asked to estimatevarious quantities, stated in

percentages(forexample,thepercentage of Africancountries in the UnitedNations).Foreachquantity,anumber between 0 and 100wasdeterminedbyspinningawheel of fortune in thesubjects’ presence. Thesubjects were instructed toindicate first whether thatnumber was higher or lowerthanthevalueofthequantity,andthentoestimatethevalueof the quantity by moving

upward or downward fromthe given number. Differentgroups were given differentnumbers for each quantity,and these arbitrary numbershad a marked effect onestimates. For example, themedian estimates of thepercentage of Africancountries in the UnitedNations were 25 and 45 forgroups that received 10 and65, respectively, as startingpoints. Payoffs for accuracy

did not reduce the anchoringeffect.Anchoring occurs not only

when the starting point isgiven to the subject, but alsowhen the subject bases hisestimateontheresultofsomeincomplete computation. Astudy of intuitive numericalestimation illustrates thiseffect. Two groups of highschool student [choult osestimated,within5seconds,anumericalexpressionthatwas

written on the blackboard.One group estimated theproduct

8×7×6×5×4×3×2×1while another groupestimatedtheproduct

1×2×3×4×5×6×7×8To rapidly answer suchquestions, people mayperform a few steps ofcomputationandestimate the

product by extrapolation oradjustment. Becauseadjustments are typicallyinsufficient, this procedureshould lead tounderestimation.Furthermore, because theresultofthefirstfewstepsofmultiplication (performedfromlefttoright)ishigherinthedescendingsequencethanin the ascending sequence,the former expression shouldbe judged larger than the

latter. Both predictions wereconfirmed. The medianestimate for the ascendingsequence was 512, while themedian estimate for thedescending sequence was2,250. The correct answer is40,320.Biases in the evaluation ofconjunctive and disjunctiveevents. In a recent study byBar-Hillel19 subjects weregiven the opportunity to bet

on one of two events. Threetypesofeventswereused:(i)simple events, such asdrawing a redmarble from abag containing 50% redmarbles and 50% whitemarbles; (ii) conjunctiveevents,suchasdrawingaredmarble seven times insuccession,withreplacement,from a bag containing 90%red marbles and 10% whitemarbles; and (iii) disjunctiveevents,suchasdrawingared

marbleat leastonce in sevensuccessive tries, withreplacement, from a bagcontaining 10% red marblesand9%whitemarbles.Inthisproblem, a significantmajorityofsubjectspreferredto bet on the conjunctiveevent (the probability ofwhich is .48) rather than onthe simple event (theprobability of which is .50).Subjectsalsopreferred tobeton the simple event rather

thanonthedisjunctiveevent,which has a probability of.52. Thus, most subjects beton the less likely event inboth comparisons. Thispatternofchoicesillustratesageneral finding. Studies ofchoiceamonggamblesandofjudgments of probabilityindicate that people tend tooverestimate the probabilityofconjunctiveevents20andtounderestimate the probability

of disjunctive events. Thesebiases are readily explainedas effects of anchoring. Thestated probability of theelementary event (success atany one stage) provides anatural starting point for theestimationoftheprobabilitiesof both conjunctive anddisjunctive events. Sinceadjustment from the startingpoint is typically insufficient,thefinalestimatesremaintooclose to the probabilities of

theelementaryeventsinbothcases. Note that the overallprobability of a conjunctiveevent is lower than theprobability of eachelementary event, whereasthe overall probability of adisjunctive event is higherthan the probability of eachelementary event. As aconsequence of anchoring,theoverallprobabilitywillbeoverestimated in conjunctiveproblems and underestimated

indisjunctiveproblems.Biases in the evaluation of

compound events areparticularly significant in thecontext of planning. Thesuccessful completion of anundertaking, such as thedevelopment of a newproduct, typically has aconjunctivecharacter: for theundertaking to succeed, eachof a series of events mustoccur. Even when each oftheseeventsisverylikely,the

overallprobabilityof successcan be quite low if thenumber of events is large.The general tendency tooverestimate the pr [timrallobability of conjunctiveevents leads to unwarrantedoptimismintheevaluationofthelikelihoodthataplanwillsucceedor thataprojectwillbe completed on time.Conversely, disjunctivestructures are typicallyencountered in theevaluation

of risks. A complex system,suchasanuclearreactororahuman body, willmalfunction if any of itsessential components fails.Even when the likelihood offailure in each component isslight, the probability of anoverall failure can be high ifmany components areinvolved. Because ofanchoring,peoplewilltendtounderestimate theprobabilities of failure in

complex systems. Thus, thedirection of the anchoringbias can sometimes beinferredfromthestructureofthe event. The chain-likestructure of conjunctionsleads to overestimation, thefunnel-like structure ofdisjunctions leads tounderestimation.Anchoringintheassessmentof subjective probabilitydistributions. In decisionanalysis, experts are often

required to express theirbeliefsaboutaquantity,suchasthevalueoftheDowJonesaverage on a particular day,in the form of a probabilitydistribution. Such adistribution is usuallyconstructed by asking theperson toselectvaluesof thequantity that correspond tospecified percentiles of hissubjective probabilitydistribution.Forexample,thejudgemaybe asked to select

a number,X90, such that hissubjective probability thatthis number will be higherthan the value of the DowJones average is .90.That is,heshouldselectthevalueX90so that he is just willing toaccept 9 to 1 odds that theDow Jones average will notexceed it. A subjectiveprobability distribution forthe value of the Dow Jonesaverage can be constructed

from several such judgmentscorresponding to differentpercentiles.By collecting subjective

probability distributions formanydifferentquantities,itispossible to test the judge forpropercalibration.Ajudgeisproperly (or externally)calibratedinasetofproblemsif exactly % of the truevalues of the assessedquantities falls below hisstated values of X . For

example, the true valuesshouldfallbelowX01 for 1%of the quantities and aboveX99 for 1% of the quantities.Thus, the true values shouldfallintheconfidenceintervalbetweenX01andX99on98%oftheproblems.Several investigators21 have

obtained probabilitydistributions for manyquantities from a largenumber of judges. These

distributions indicated largeand systematic departuresfrom proper calibration. Inmost studies, the actualvalues of the assessedquantities are either smallerthan X0l or greater than X99for about 30% of theproblems. That is, thesubjects state overly narrowconfidence intervals whichreflectmore certainty than isjustified by their knowledge

about theassessedquantities.Thisbiasiscommontonaiveand to sophisticated subjects,and it is not eliminated byintroducing proper scoringrules, which provideincentives for externalcalibration. This effect isattributable,inpartatleast,toanchoring.To selectX90 for the value

oftheDowJonesaverage,forexample,itisnaturaltobegin

by thinking about one’s bestestimate of the Dow Jonesand to adjust this valueupward. If this adjustment—like most others—isinsufficient,thenX90willnotbe sufficiently extreme. Asimilar anchoring [laricientlyeffect will occur in theselection of X10, which ispresumably obtained byadjusting one’s best estimatedownward.Consequently,the

confidence interval betweenX10 and X90 will be toonarrow, and the assessedprobability distribution willbetootight.Insupportofthisinterpretationitcanbeshownthat subjective probabilitiesaresystematicallyalteredbyaprocedureinwhichone’sbestestimatedoesnotserveasananchor.Subjective probability

distributions for a given

quantity (the Dow Jonesaverage) can be obtained intwo different ways: (i) byasking the subject to selectvaluesof theDowJones thatcorrespond to specifiedpercentiles of his probabilitydistributionand(ii)byaskingthe subject to assess theprobabilities that the truevalue of the Dow Jones willexceedsomespecifiedvalues.The two procedures areformally equivalent and

should yield identicaldistributions. However, theysuggest different modes ofadjustment from differentanchors. Inprocedure (i), thenaturalstartingpoint isone’sbest estimate of the quantity.Inprocedure(ii),ontheotherhand, the subject may beanchored on the value statedinthequestion.Alternatively,hemaybe anchored on evenodds, or a 50–50 chance,which is a natural starting

point in the estimation oflikelihood. In either case,procedure (ii) should yieldless extreme odds thanprocedure(i).To contrast the two

procedures, a set of 24quantities (such as the airdistance from New Delhi toPeking) was presented to agroup of subjects whoassessedeitherX10orX90 foreachproblem.Anothergroup

of subjects received themedian judgment of the firstgroup for each of the 24quantities. They were askedtoassesstheoddsthateachofthegivenvaluesexceededthetrue value of the relevantquantity. In the absence ofany bias, the second groupshould retrieve the oddsspecified to the first group,that is,9:1.However, ifevenoddsorthestatedvalueserveas anchors, the odds of the

second group should be lessextreme,thatis,closerto1:1.Indeed, the median oddsstated by this group, acrossallproblems,were3:1.Whenthe judgments of the twogroups were tested forexternal calibration, it wasfoundthatsubjectsinthefirstgroup were too extreme, inaccord with earlier studies.The events that they definedashavingaprobabilityof.10actually obtained in 24% of

thecases.Incontrast,subjectsin thesecondgroupwere tooconservative.Eventstowhichthey assigned an averageprobability of .34 actuallyobtainedin26%ofthecases.These results illustrate themanner in which the degreeofcalibrationdependson theprocedureofelicitation.

DiscussionThis article has been

concerned with cognitivebiases that stem from thereliance on judgmentalheuristics. These biases arenot attributable tomotivational effects such aswishful thinking or thedistortion of judgments bypayoffsandpenalties.Indeed,severalofthesevereerrorsofjudgment reported earlieroccurred despite the fact thatsubjects were encouraged tobe accurate and were

rewarded for the correctanswers.22The reliance on heuristics

and the prevalence of biasesare not restricted to laymen.Experienced researchers arealsopronetothesamebiases—whentheythinkintuitively.Forexample, the tendency topredict the outcome that bestrepresents the data, withinsufficient regard for priorprobability, has been

observed in the intuitivejudgmentsofindividualswhohavehadextensivetraininginstatistics. [ticor pri23Although the statisticallysophisticated avoidelementaryerrors,suchasthegambler’s fallacy, theirintuitive judgments are liableto similar fallacies in moreintricate and less transparentproblems.It is not surprising that

useful heuristics such asrepresentativeness andavailabilityareretained,eventhoughtheyoccasionallyleadto errors in prediction orestimation. What is perhapssurprising is the failure ofpeople to infer from lifelongexperience such fundamentalstatistical rules as regressiontowardthemean,ortheeffectof sample size on samplingvariability. Althougheveryone is exposed, in the

normal course of life, tonumerous examples fromwhich these rules could havebeen induced, very fewpeoplediscovertheprinciplesofsamplingandregressionontheir own. Statisticalprinciples are not learnedfrom everyday experiencebecausetherelevantinstancesare not coded appropriately.For example, people do notdiscover that successive linesin a text differ more in

average word length than dosuccessive pages, becausethey simply do not attend tothe average word length ofindividual lines or pages.Thus,peopledonotlearntherelation between sample sizeand sampling variability,although the data for suchlearningareabundant.The lack of an appropriate

code also explains whypeople usually do not detectthe biases in their judgments

ofprobability.Apersoncouldconceivablylearnwhetherhisjudgments are externallycalibrated by keeping a tallyof the proportion of eventsthat actually occur amongthosetowhichheassignsthesameprobability.However,itisnotnatural togroupeventsbytheirjudgedprobability.Intheabsenceofsuchgroupingit is impossible for anindividual to discover, forexample,thatonly50%ofthe

predictions to which he hasassignedaprobabilityof.9orhigheractuallycametrue.The empirical analysis of

cognitive biases hasimplications for thetheoreticalandappliedroleofjudged probabilities. Moderndecision theory24 regardssubjective probability as thequantified opinion of anidealizedperson.Specifically,thesubjectiveprobabilityofa

givenevent isdefinedby theset of bets about this eventthat such a person is willingto accept. An internallyconsistent, or coherent,subjective probabilitymeasurecanbederivedforanindividual if his choicesamong bets satisfy certainprinciples, that is, theaxiomsof the theory. The derivedprobability is subjective inthe sense that differentindividuals are allowed to

have different probabilitiesforthesameevent.Themajorcontribution of this approachis that it provides a rigoroussubjective interpretation ofprobability that is applicableto unique events and isembeddedinageneraltheoryofrationaldecision.It should perhaps be noted

that, while subjectiveprobabilities can sometimesbe inferred from preferencesamong bets, they are

normally not formed in thisfashion. A person bets onteamAratherthanonteamBbecausehebelievesthatteamA is more likely to win; hedoesnotinferthisbelieffromhisbettingpreferences.Thus,in reality, subjectiveprobabilities determinepreferences among bets andarenotderivedfromthem,asin the axiomatic theory ofrationaldecision.25

The inherently subjectivenature of probability has ledmany students to the beliefthat coherence, or internalconsistency, is theonlyvalidcriterion by which judgedprobabilities should beevaluated. From thestandpoint of the formaltheory of subjectiveprobability, any set ofinternally consistentprobability judgments is asgood as any other. This

criterion is not entirelysatisfactory[safsub,becauseaninternallyconsistentsetofsubjective probabilities canbe incompatible with otherbeliefsheldbytheindividual.Consider a person whosesubjectiveprobabilitiesforallpossible outcomes of a coin-tossing game reflect thegambler’sfallacy.Thatis,hisestimateof theprobabilityoftails on a particular tossincreaseswith the number of

consecutive heads thatpreceded that toss. Thejudgments of such a personcouldbe internally consistentand therefore acceptable asadequate subjectiveprobabilities according to thecriterionoftheformaltheory.Theseprobabilities, however,are incompatible with thegenerally held belief that acoin has no memory and istherefore incapable ofgenerating sequential

dependencies. For judgedprobabilities tobeconsideredadequate,or rational, internalconsistency is not enough.The judgments must becompatible with the entireweb of beliefs held by theindividual. Unfortunately,therecanbenosimpleformalprocedure for assessing thecompatibility of a set ofprobability judgments withthe judge’s total system ofbeliefs. The rational judge

will nevertheless strive forcompatibility, even thoughinternal consistency is moreeasily achievedandassessed.In particular, he will attemptto make his probabilityjudgments compatible withhis knowledge about thesubject matter, the laws ofprobability, and his ownjudgmental heuristics andbiases.

Summary

This article described threeheuristics that are employedin making judgments underuncertainty: (i)representativeness, which isusually employed whenpeopleareaskedtojudgetheprobability that an object orevent A belongs to class orprocess B; (ii) availability ofinstances or scenarios,whichis often employed whenpeopleareaskedtoassessthe

frequency of a class or theplausibility of a particulardevelopment; and (iii)adjustment from an anchor,whichisusuallyemployed innumerical prediction when arelevant value is available.These heuristics are highlyeconomical and usuallyeffective, but they lead tosystematic and predictableerrors.Abetterunderstandingof theseheuristics andof thebiases to which they lead

couldimprovejudgmentsanddecisions in situations ofuncertainty.

P

Notes

1.D. Kahneman and A.Tversky,“OnthePsychologyof Prediction,”PsychologicalReview80(1973):237–51.

2.Ibid.

3.

Ibid.

4.D. Kahneman and A.Tversky, “SubjectiveProbability: A Judgment ofRepresentativeness,”Cognitive Psychology 3(1972):430–54.

5.Ibid.

6.

W. Edwards, “Conservatismin Human InformationProcessing,” in FormalRepresentation of HumanJudgment, ed.B.Kleinmuntz(New York: Wiley, 1968),17–52.

[t="orm7.

Kahneman and Tversky,“SubjectiveProbability.”

8.

A. Tversky and D.Kahneman, “Belief in theLaw of Small Numbers,”Psychological Bulletin 76(1971):105–10.

9.KahnemanandTversky, “Onthe Psychology ofPrediction.”

10.Ibid.

11.Ibid.

12.Ibid.

13.A. Tversky and D.Kahneman, “Availability: AHeuristic for JudgingFrequency and Probability,”Cognitive Psychology 5(1973):207–32.

14.Ibid.

15.R. C. Galbraith and B. J.Underwood, “PerceivedFrequency of Concrete andAbstractWords,”Memory&Cognition1(1973):56–60.

16.Tversky and Kahneman,“Availability.”

17.L. J. Chapman and J. P.Chapman, “Genesis ofPopular but ErroneousPsychodiagnosticObservations,” Journal ofAbnormal Psychology 73(1967): 193–204; L. J.ChapmanandJ.P.Chapman,“Illusory Correlation as anObstacle to theUse ofValidPsychodiagnostic Signs,”Journal of AbnormalPsychology 74 (1969): 271–

80.

18.P.SlovicandS.Lichtenstein,“ComparisonofBayesianandRegressionApproachestotheStudy of InformationProcessing in Judgment,”Organizational Behavior &Human Performance 6(1971):649–744.

19.M. Bar-Hillel, “On the

Subjective Probability ofCompound Events,”Organizational Behavior &Human Performance 9(1973):396–406.

20.J.Cohen,E.I.Chesnick,andD.Haran,“AConfirmationofthe Inertial-? Effect inSequential Choice andDecision,”British Journal ofPsychology63(1972):41–46.

21.M.Alpe[spa

Acta Psychologica 35(1971): 478–94; R. L.Winkler,“TheAssessmentofPrior Distributions inBayesian Analysis,” Journalof the American StatisticalAssociation 62 (1967): 776–800.

22.Kahneman and Tversky,“Subjective Probability”;

Tversky and Kahneman,“Availability.”

23.KahnemanandTversky, “Onthe Psychology ofPrediction”; Tversky andKahneman, “Belief in theLawofSmallNumbers.”

24.L. J. Savage, TheFoundations of Statistics(NewYork:Wiley,1954).

25.Ibid.; B. de Finetti,“Probability: Interpretations,”inInternationalEncyclopediaoftheSocialSciences,ed.D.E. Sills, vol. 12 (NewYork:Macmillan,1968),496–505.

P

AppendixB:Choices,

Values,AndFrames*

DanielKahnemanandAmosTversky

ABSTRACT: We discuss thecognitive and thepsychophysical determinantsofchoiceinriskyandriskless

contexts. The psychophysicsof value induce risk aversionin the domain of gains andrisk seeking in thedomainoflosses. The psychophysics ofchance induce overweightingof sure things and ofimprobableevents,relativetoevents of moderateprobability. Decisionproblemscanbedescribedorframed in multiple ways thatgive rise to differentpreferences, contrary to the

invariance criterion ofrational choice. The processof mental accounting, inwhich people organize theoutcomes of transactions,explains some anomalies ofconsumer behavior. Inparticular, the acceptabilityof an option can depend onwhether a negative outcomeisevaluatedasacostorasanuncompensated loss. Therelation between decisionvalues and experience values

isdiscussed.

Making decisions is likespeaking prose—people do itall the time, knowingly orunknowingly. It is hardlysurprising,then,thatthetopicof decisionmaking is sharedby many disciplines, frommathematics and statistics,through economics andpoliticalscience,tosociologyandpsychology.Thestudyof

decisions addresses bothnormative and descriptivequestions. The normativeanalysisisconcernedwiththenature of rationality and thelogicofdecisionmaking.Thedescriptive analysis, incontrast, is concerned withpeople’s beliefs andpreferencesastheyare,notasthey should be. The tensionbetween normative anddescriptive considerationscharacterizes much of the

studyofjudgmentandchoice.Analyses of decision

making commonlydistinguish risky and risklesschoices. The paradigmaticexampleofdecisionun^v>

RiskyChoiceRisky choices, such aswhether or not to take anumbrella and whether or notto go to war, are madewithout advance knowledge

of their consequences.Because the consequences ofsuch actions depend onuncertain events such as theweather or the opponent’sresolve, the choice of an actmay be construed as theacceptance of a gamble thatcan yield various outcomeswithdifferentprobabilities.Itis therefore natural that thestudy of decision makingunder risk has focused onchoices between simple

gambles with monetaryoutcomes and specifiedprobabilities, in thehope thatthese simple problems willreveal basic attitudes towardriskandvalue.Weshallsketchanapproach

to risky choice that derivesmanyofitshypothesesfromapsychophysical analysis ofresponses to money and toprobability. Thepsychophysical approach todecision making can be

traced to a remarkable essaythat Daniel Bernoullipublished in 1738 (Bernoulli1954) in which he attemptedto explain why people aregenerally averse to risk andwhy risk aversion decreaseswith increasing wealth. Toillustrate risk aversion andBernoulli’sanalysis,considerthechoicebetweenaprospectthat offers an 85%chance towin $1,000 (with a 15%chance to win nothing) and

the alternative of receiving$800 for sure. A largemajority of people prefer thesure thing over the gamble,although the gamble hashigher (mathematical)expectation. The expectationof a monetary gamble is aweightedaverage,whereeachpossibleoutcomeisweightedby its probability ofoccurrence. The expectationofthegambleinthisexampleis .85×$1,000+ .15×$0=

$850, which exceeds theexpectation of $800associatedwiththesurething.The preference for the suregain is an instance of riskaversion. In general, apreferenceforasureoutcomeoveragamblethathashigherorequalexpectation iscalledrisk averse, and the rejectionof a sure thing in favor of agamble of lower or equalexpectation is called riskseeking.

Bernoulli suggested thatpeople do not evaluateprospects by the expectationof their monetary outcomes,but rather by the expectationof the subjective value ofthese outcomes. Thesubjective value of a gambleis again a weighted average,but now it is the subjectivevalueofeachoutcomethatisweighted by its probability.To explain risk aversionwithin this framework,

Bernoulli proposed thatsubjectivevalue,orutility, isaconcavefunctionofmoney.In such a function, thedifference between theutilitiesof$200and$100,forexample, is greater than theutility difference between$1,200and$1,100.Itfollowsfrom concavity that thesubjectivevalueattachedtoagain of $800 is more than80%ofthevalueofagainof$1,000. Consequently, the

concavity of the utilityfunction entails a risk aversepreference for a sure gain of$800 over an 80% chance towin$1,000,althoughthetwoprospects have the samemonetaryexpectation.It is customary in decision

analysis to describe theoutcomes of decisions interms of total wealth. Forexample, an offer to bet $20on the toss of a fair coin isrepresented as a choice

between an individual’scurrentwealthWandanevenchancetomovetoW+$20ortoWn indispan> – $20. Thisrepresentation appearspsychologically unrealistic:Peopledonotnormally thinkof relatively small outcomesin terms of states of wealthbut rather in terms of gains,losses, and neutral outcomes(such as the maintenance ofthe status quo). If theeffective carriers of

subjective value are changesofwealthratherthanultimatestates of wealth, as wepropose, the psychophysicalanalysis of outcomes shouldbeappliedtogainsandlossesrather than to total assets.This assumption plays acentral role in a treatment ofrisky choice that we calledprospect theory (Kahnemanand Tversky 1979).Introspection as well aspsychophysical

measurements suggest thatsubjectivevalue is aconcavefunctionofthesizeofagain.The same generalizationapplies tolossesaswell.Thedifference insubjectivevaluebetweena lossof$200andaloss of $100 appears greaterthan the difference insubjective value between aloss of $1,200 and a loss of$1,100. When the valuefunctions for gains and forlossesarepiecedtogether,we

obtain an S-shaped functionof the type displayed inFigure1.

Figure1.AHypotheticalValueFunction

Thevaluefunctionshownin

Figure 1 is (a) defined ongains and losses rather thanon total wealth, (b) concavein the domain of gains andconvex in the domain oflosses, and (c) considerablysteeper for losses than for

gains. The last property,whichwelabellossaversion,expresses the intuition that aloss of $X is more aversivethanagainof$Xisattractive.Loss aversion explainspeople’s reluctance to bet ona fair coin for equal stakes:The attractiveness of thepossible gain is not nearlysufficient to compensate forthe aversiveness of thepossible loss. For example,mostrespondentsinasample

of undergraduates refused tostake$10onthetossofacoinif theystood towin less than$30.The assumption of risk

aversion has played a centralrole in economic theory.However, just as theconcavity of the value ofgainsentailsriskaversion,theconvexity of the value oflosses entails risk seeking.Indeed, riskseeking in lossesisarobusteffect,particularly

whentheprobabilitiesoflossare substantial. Consider, forexample,asituationinwhichan individual is forced tochoose between an 85%chancetolose$1,000(witha15% chance to lose nothing)and a sure loss of $800. Alarge majority of peopleexpress a preference for thegamble over the sure loss.This is a risk seeking choicebecausetheexpectationofthegamble (–$850) is inferior to

the expectation of the sureloss (–$800).Risk seeking inthedomainoflosseshasbeenconfirmed by severalinvestigators (Fishburn andKochenberger 1979;Hersheyand Schoemaker 1980;Payne,Laughhunn,andCrum1980; Slovic, Fischhoff, andLichtenstein1982).Ithasalsobeen observed withnonmonetary outcomes, suchas hours of pain (Eraker andSox1981)andlossofhuman

lives (Fischhoff 1983;Tversky 1977; Tversky andKahneman1981).Is itwrongto be risk averse in thedomain of gains and riskseeking in the domain oflosses? These preferencesconform to compellingintuitionsaboutthesubjectivevalueofgainsandlosses,andthepresumptionisthatpeopleshould be entitled to theirown values. However, weshall see that an S-shaped

value function hasimplications that arenormativelyunacceptable.To address the normative

issue we turn frompsychology to decisiontheory. Modern decisiontheory can be said to beginwith the pioneering work ofvon Neumann andMorgenstern(1947),wholaiddown several qualitativeprinciples, or axioms, thatshould g ctha211;$850)overn

the preferences of a rationaldecisionmaker.Theiraxiomsincluded transitivity (if A ispreferred to B and B ispreferred to C, then A ispreferred to C), andsubstitution(ifA ispreferredtoB, then an even chance togetAorC is preferred to aneven chance to get B or C),alongwithotherconditionsofamore technical nature. Thenormativeandthedescriptivestatus of the axioms of

rationalchoicehavebeen thesubject of extensivediscussions. In particular,there is convincing evidencethat people do not alwaysobey the substitution axiom,and considerabledisagreementexistsabout thenormativemeritofthisaxiom(e.g.,AllaisandHagen1979).However, all analyses ofrational choice incorporatetwo principles: dominanceand invariance. Dominance

demandsthatifprospectAisatleastasgoodasprospectBin every respect and betterthanBinatleastonerespect,thenAshouldbepreferredtoB.Invariancerequiresthatthepreference order betweenprospects should not dependon themanner inwhich theyare described. In particular,two versions of a choiceproblem that are recognizedtobeequivalentwhenshowntogether should elicit the

same preference even whenshown separately. We nowshow that the requirement ofinvariance, howeverelementary and innocuous itmay seem, cannot generallybesatisfied.

FramingofOutcomes

Risky prospects arecharacterized by theirpossibleoutcomesandbythe

probabilities of theseoutcomes. The same option,however, can be framed ordescribed in different ways(Tversky and Kahneman1981). For example, thepossible outcomes of agamble can be framed eitherasgainsandlossesrelativetothe status quo or as assetpositions that incorporateinitial wealth. Invariancerequires that such changes inthe description of outcomes

should not alter thepreference order. Thefollowing pair of problemsillustrates a violation of thisrequirement. The totalnumber of respondents ineach problem is denoted byN, and the percentage whochoseeachoptionisindicatedinparentheses.

Problem 1 (N = 152):Imagine that the U.S. ispreparing for the

outbreak of an unusualAsian disease, which isexpected to kill 600people. Two alternativeprograms to combat thedisease have beenproposed. Assume thatthe exact scientificestimates of theconsequences of theprogramsareasfollows:If Program A isadopted,200peoplewillbesaved.(72%)

If Program B isadopted, there is a one-thirdprobabilitythat600peoplewillbesavedanda two-thirds probabilitythat no people will besaved.(28%)Which of the twoprograms would youfavor?

TheformulationofProblem

1 implicitly adopts as areference point a state of

affairsinwhichthediseaseisallowedtotakeitstollof600lives. The outcomes of theprograms include thereference state and twopossible gains, measured bythenumberoflivessaved.Asexpected,preferencesareriskaverse: A clear majority ofrespondentsprefersaving200lives for sure over a gamblethatoffersaone-thirdchanceof saving 600 lives. Nowconsider another problem in

whichthesamecoverstoryisfollowed by a differentdescription of the prospectsassociated with the twoprograms:

Problem2(N=155):IfProgramCisadopted,400 people will die.(22%)IfProgramDisadopted,there is a one-thirdprobability that nobodywilldieandatwo-thirds

probability that 600peoplewilldie.(78%)

It is easy to verify that

optionsCandDinProblem2are undistinguishable in realterms from options A and Bin Problem 1, respectively.Thesecondversion,however,assumes a reference state inwhich no one dies of thedisease. The best outcome isthe maintenance of this stateandthealternativesarelosses

measured by the number ofpeople that will die of thedisease. Peoplewho evaluateoptions in these terms areexpected to show a riskseeking preference for thegamble (option D) over thesurelossof400lives.Indeed,there ismore risk seeking inthe second version of theproblem than there is riskaversioninthefirst.The failure of invariance is

both pervasive and robust. It

is as common amongsophisticated respondents asamong naive ones, and it isnoteliminatedevenwhenthesame respondents answerboth questions within a fewminutes. Respondentsconfronted with theirconflicting answers aretypically puzzled. Even afterrereading the problems, theystillwish tobe riskaverse inthe “lives saved” version;they wish to be risk seeking

in the “lives lost” version;and they also wish to obeyinvarianceandgiveconsistentanswers in the two versions.In their stubborn appeal,framing effects resembleperceptualillusionsmorethancomputationalerrors.The following pair of

problems elicits preferencesthat violate the dominancerequirement of rationalchoice.

Problem 3 (N = 86):Choosebetween:

E. 25% chance to win

$240and75%chancetolose$760(0%)

F. 25% chance to win$250and75%chancetolose$750(100%)It is easy to see that Fdominates E. Indeed, allrespondents choseaccordingly.

Problem 4 (N = 150):Imagine that you facethe following pair ofconcurrentdecisions.First examine bothdecisions, then indicatetheoptionsyouprefer.

Decision (i) Choosebetween:

A. a sure gain of $240

(84%)

B. 25% chance to gain$1,000 and 75% chance togainnothing(16%)

Decision (ii) Choosebetween:

C. a sure loss of $750

(13%)D. 75% chance to lose

$1,000 and 25% chance tolosenothing(87%)

As expected from theprevious analysis, a largemajority of subjects made ariskaversechoiceforthesuregainoverthepositivegamblein the first decision, and aneven larger majority ofsubjects made a risk seekingchoice for the gamble overthe sure loss in the seconddecision. In fact, 73% of therespondents chose A and Dandonly3%choseBandC.ThesamecdCcefpatternof

results was observed in amodified version of theproblem,withreducedstakes,in which undergraduatesselected gambles that theywouldactuallyplay.Because the subjects

considered the two decisionsinProblem4 simultaneously,they expressed in effect apreference for A andD overB and C. The preferredconjunction, however, isactually dominated by the

rejectedone.Addingthesuregain of $240 (option A) tooptionDyieldsa25%chanceto win $240 and a 75%chance to lose $760. This ispreciselyoptionEinProblem3. Similarly, adding the sureloss of $750 (option C) tooptionByieldsa25%chanceto win $250 and a 75%chance to lose $750. This ispreciselyoptionFinProblem3. Thus, the susceptibility toframing and the S-shaped

value function produce aviolation of dominance in asetofconcurrentdecisions.Themoraloftheseresultsis

disturbing: Invariance isnormatively essential,intuitively compelling, andpsychologically unfeasible.Indeed,weconceiveonlytwoways of guaranteeinginvariance. The first is toadopt a procedure that willtransformequivalentversionsofanyproblemintothesame

canonicalrepresentation.Thisis the rationale for thestandard admonition tostudentsofbusiness,thattheyshouldconsidereachdecisionproblem in terms of totalassets rather than in termsofgains or losses (Schlaifer1959). Such a representationwouldavoidtheviolationsofinvariance illustrated in theprevious problems, but theadvice is easier to give thanto follow. Except in the

context of possible ruin, it ismore natural to considerfinancial outcomes as gainsand losses rather than asstatesofwealth.Furthermore,a canonical representation ofrisky prospects requires acompoundingofalloutcomesof concurrent decisions (e.g.,Problem 4) that exceeds thecapabilities of intuitivecomputation even in simpleproblems. Achieving acanonical representation is

even more difficult in othercontexts such as safety,health, or quality of life.Should we advise people toevaluatetheconsequenceofapublic health policy (e.g.,Problems1and2)intermsofoverall mortality, mortalitydue to diseases, or thenumber of deaths associatedwith the particular diseaseunderstudy?Anotherapproachthatcould

guarantee invariance is the

evaluationofoptionsintermsof their actuarial rather thantheir psychologicalconsequences. The actuarialcriterion has some appeal inthe context of human lives,butitisclearlyinadequateforfinancialchoices,ashasbeengenerally recognized at leastsince Bernoulli, and it isentirely inapplicable tooutcomes that lack anobjective metric. Weconclude that frame

invariancecannotbeexpectedto hold and that a sense ofconfidence in a particularchoice does not ensure thatthe same choice would bemade in another frame. It isthereforegoodpracticetotestthe robustness of preferencesby deliberate attempts toframe a decision problem inmore than one way(Fischhoff, Slovic, andLichtenstein1980).

ThePsychophysicsofChances

Our discussion so far hasassumed a Bernoullianexpectation rule according towhichthevalue,orutility,ofan uncertain prospect isobtained by adding theutilities of the possibleoutcomes, each weighted byits probability. To examinethis assumption, let us againconsult psychophysical

intuitions.Settingthevalueofthe status quo at zero,imagine a cash gift, say of$300,andassignitavalueofone. Now imagine that youare only given a ticket to alottery thathasa singleprizeof$300.Howdoes thevalueof the ticket vary as afunctionof theprobabilityofwinning the prize? Barringutilityforgambling,thevalueof suchaprospectmustvarybetween zero (when the

chance of winning is nilcinntric.We) and one (whenwinning$300isacertainty).Intuition suggests that the

value of the ticket is not alinear function of theprobability of winning, asentailed by the expectationrule.Inparticular,anincreasefrom 0% to 5% appears tohave a larger effect than anincrease from 30% to 35%,which also appears smallerthananincreasefrom95%to

100%. These considerationssuggest a category-boundaryeffect: A change fromimpossibility topossibilityorfrom possibility to certaintyhas a bigger impact than acomparable change in themiddle of the scale. Thishypothesis is incorporatedinto the curve displayed inFigure 2, which plots theweightattachedtoaneventasa function of its statednumerical probability. The

mostsalientfeatureofFigure2 is thatdecisionweightsareregressive with respect tostated probabilities. Exceptnear the endpoints, anincrease of .05 in theprobability of winningincreases the value of theprospect by less than 5% ofthe value of the prize. Wenext investigate theimplications of thesepsychophysical hypothesesfor preferences among risky

options.

Figure2.AHypotheticalWeightingFunction

In Figure 2, decision

weights are lower than thecorresponding probabilitiesover most of the range.Underweighting of moderateandhighprobabilitiesrelativeto sure things contributes to

risk aversion in gains byreducing theattractivenessofpositive gambles. The sameeffect alsocontributes to riskseeking in losses byattenuating the aversivenessof negative gambles. Lowprobabilities, however, areoverweighted, and very lowprobabilities are eitheroverweightedquitegrosslyorneglected altogether, makingthe decision weights highlyunstable in that region. The

overweighting of lowprobabilities reverses thepattern described above: Itenhances the value of longshots and amplifies theaversiveness of a smallchance of a severe loss.Consequently, people areoften risk seeking in dealingwith improbable gains andrisk averse in dealing withunlikely losses. Thus, thecharacteristics of decisionweights contribute to the

attractiveness of both lotteryticketsandinsurancepolicies.Thenonlinearityofdecision

weights inevitably leads toviolations of invariance, asillustrated in the followingpairofproblems:

Problem 5 (N = 85):Consider the followingtwo-stage game. In thefirst stage, there is a75% chance to end thegame without winning

anything and a 25%chance tomove into thesecond stage. If youreach the second stageyou have a choicebetween:

A. a sure win of $30

(74%)B.80%chancetowin$45

(26%)

Your choice must be

made before the gamestarts, i.e., before theoutcome of the firststage is known. Pleaseindicate the option youprefer.

Problem 6 (N = 81):Which of the followingoptionsdoyouprefer?

C.25%chancetowin$30

(42%)

D. 20% chance to win$45(58%)Becausethereisonechance

itoceinfourtomoveintothesecond stage in Problem 5,prospect A offers a .25probability of winning $30,and prospect B offers .25 ×.80 = .20 probability ofwinning$45.Problems5and6 are therefore identical interms of probabilities andoutcomes. However, the

preferences are not the samein the two versions: A clearmajority favors the higherchance to win the smalleramount in Problem 5,whereasthemajoritygoestheotherwayinProblem6.Thisviolation of invariance hasbeenconfirmedwithbothrealand hypothetical monetarypayoffs (the present resultsare with real money), withhumanlivesasoutcomes,andwith a nonsequential

representation of the chanceprocess.We attribute the failure of

invariance to the interactionoftwofactors:theframingofprobabilities and thenonlinearity of decisionweights. More specifically,weproposethatinProblem5people ignore the first phase,which yields the sameoutcome regardless of thedecision that is made, andfocus their attention on what

happens if they do reach thesecond stage of the game. Inthatcase,ofcourse,theyfacea sure gain if they chooseoptionAandan80%chanceof winning if they prefer togamble. Indeed, people’schoices in the sequentialversion are practicallyidentical to the choices theymakebetween a sure gain of$30 and an 85% chance towin$45.Becauseasurethingis overweighted in

comparison with events ofmoderate or highprobability,theoption thatmay lead to againof$30ismoreattractivein the sequentialversion.Wecall this phenomenon thepseudo-certainty effectbecause an event that isactuallyuncertainisweightedasifitwerecertain.A closely related

phenomenon can bedemonstrated at the low endof the probability range.

Suppose you are undecidedwhether or not to purchaseearthquake insurancebecausethepremiumisquitehigh.Asyou hesitate, your friendlyinsurance agent comes forthwithanalternativeoffer:“Forhalf theregularpremiumyoucan be fully covered if thequake occurs on an odd dayof themonth. This is a gooddealbecauseforhalfthepriceyouarecoveredformorethanhalf the days.”Whydomost

peoplefindsuchprobabilisticinsurance distinctlyunattractive? Figure 2suggests an answer. Startinganywhereintheregionoflowprobabilities, the impact onthe decision weight of areduction of probability fromp to p/2 is considerablysmaller than the effect of areduction from p/2 to 0.Reducing the risk by half,then, is not worth half thepremium.

Theaversiontoprobabilisticinsurance is significant forthree reasons. First, itundermines the classicalexplanation of insurance interms of a concave utilityfunction. According toexpected utility theory,probabilisticinsuranceshouldbe definitely preferred tonormal insurance when thelatter is just acceptable (seeKahneman and Tversky1979). Second, probabilistic

insurance represents manyforms of protective action,such as having a medicalcheckup,buyingnewtires,orinstalling a burglar alarmsystem. Such actionstypically reduce theprobability of some hazardwithout eliminating italtogether. Third, theacceptabilityofinsurancecanbe manipulated by theframingof the contingencies.An insurance policy that

covers fire but not flood, forexample, could be evaluatedeither as full protectionagainst a specific risk (e.g.,fire),or as a reduction in theoverall probability ofproperty loss. Figure 2suggests that people greatlyundervaluea reduction in theprobability of a hazard incomparison to the completeelimination of that hazard.Hence, insurance shouldappear more attractive when

itisframedastheeliminationof risk than when it isdescribed as a reduction ofrisk. Indeed, Slovic,Fischhoff, and Lichtenstein(1982) showed that ahypotheti ct arnative calvaccine that reduces theprobability of contracting adisease from 20% to 10% isless attractive if it isdescribed as effective in halfof the cases than if it ispresented as fully effective

against one of two exclusiveand equally probable virusstrains that produce identicalsymptoms.

FormulationEffectsSo far we have discussedframing as a tool todemonstrate failures ofinvariance. We now turnattentiontotheprocessesthatcontrol the framing ofoutcomes and events. The

public health problemillustratesaformulationeffectinwhichachangeofwordingfrom “lives saved” to “liveslost” induced a marked shiftof preference from riskaversion to risk seeking.Evidently, the subjectsadopted the descriptions ofthe outcomes as given in thequestion and evaluated theoutcomes accordingly asgains or losses. Anotherformulation effect was

reported by McNeil, Pauker,Sox, and Tversky (1982).They found that preferencesof physicians and patientsbetween hypotheticaltherapies for lung cancervaried markedly when theirprobable outcomes weredescribed in terms ofmortality or survival.Surgery, unlike radiationtherapy,entailsariskofdeathduring treatment. As aconsequence, the surgery

option was relatively lessattractive when the statisticsof treatment outcomes weredescribed in terms ofmortalityratherthanintermsofsurvival.A physician, and perhaps a

presidential advisor as well,could influence the decisionmadebythepatientorbythePresident, without distortingor suppressing information,merely by the framing ofoutcomes and contingencies.

Formulationeffectscanoccurfortuitously, without anyonebeingawareof the impactofthe frame on the ultimatedecision. They can also beexploited deliberately tomanipulate the relativeattractiveness of options. Forexample,Thaler(1980)notedthat lobbyists for the creditcardindustryinsistedthatanypricedifferencebetweencashand credit purchases belabeledacashdiscountrather

than a credit card surcharge.The two labels frame theprice difference as a gain oras a loss by implicitlydesignating either the lowerorthehigherpriceasnormal.Because losses loom largerthangains,consumersarelesslikely to accept a surchargethantoforgoadiscount.Asisto be expected, attempts toinfluence framing arecommon in the marketplaceandinthepoliticalarena.

Theevaluationofoutcomesis susceptible to formulationeffects because of thenonlinearity of the valuefunction and the tendency ofpeople to evaluate options inrelationtothereferencepointthat is suggested or impliedby the statement of theproblem. It isworthyofnotethat in other contexts peopleautomatically transformequivalent messages into thesame representation. Studies

of language comprehensionindicate that people quicklyrecode much of what theyhear into an abstractrepresentation that no longerdistinguisheswhethertheideawasexpressedinanactiveorin a passive form and nolonger discriminates whatwas actually said from whatwas implied, presupposed, orimplicated (Clark and Clark1977). Unfortunately, themental machinery that

performs these operationssilentlyandeffortlesslyisnotadequate to perform the taskof recoding the two versionsof the public health problemor the mortality survivalstatistics into a commonabstractform.

TransactionsandTrades

Our analysis of framing andof value can be extended to

choices betweenmultiattributeoptions,suchasthe acceptability of atransaction or a trade. Wepropose that, in order toevaluate a multiattributeoption,apersonsetsupamencset optiotal account thatspecifies the advantages andthe disadvantages associatedwith the option, relative to amultiattribute reference state.Theoverallvalueofanoptionisgivenby thebalanceof its

advantages and itsdisadvantages in relation tothe reference state. Thus, anoption is acceptable if thevalue of its advantagesexceeds the value of itsdisadvantages. This analysisassumes psychological—butnot physical—separability ofadvantages anddisadvantages. The modeldoesnotconstrainthemannerin which separate attributesarecombined to formoverall

measuresofadvantageandofdisadvantage, but it imposeson these measuresassumptionsofconcavityandoflossaversion.Our analysis of mental

accountingowesa largedebtto the stimulating work ofRichardThaler (1980, 1985),whoshowed therelevanceofthis process to consumerbehavior. The followingproblem, based on examplesof Savage (1954) and Thaler

(1980), introduces some ofthe rules that govern theconstruction of mentalaccounts and illustrates theextensionof theconcavityofvalue to the acceptability oftransactions.

Problem7: Imagine thatyou are about topurchase a jacket for$125andacalculatorfor$15. The calculatorsalesman informs you

that the calculator youwishtobuyisonsalefor$10 at the other branchof the store, located 20minutes’ drive away.Would you make a triptotheotherstore?

This problem is concernedwith the acceptability of anoption that combines adisadvantage ofinconvenience with afinancial advantage that can

be framed as a minimal,topical, or comprehensiveaccount. The minimalaccount includes only thedifferences between the twooptions and disregards thefeaturesthattheyshare.Intheminimal account, theadvantage associated withdriving to the other store isframed as a gain of $5. Atopical account relates theconsequences of possiblechoices to a reference level

that is determined by thecontext within which thedecision arises. In thepreceding problem, therelevant topic is thepurchaseof the calculator, and thebenefitofthetripisthereforeframed as a reduction of theprice, from $15 to $10.Because the potential savingis associated only with thecalculator, the price of thejacket is not included in thetopical account. The price of

the jacket, as well as otherexpenses, could well beincluded in a morecomprehensive account inwhich the saving would beevaluated in relation to, say,monthlyexpenses.The formulation of the

preceding problem appearsneutral with respect to theadoption of a minimal,topical, or comprehensiveaccount. We suggest,however, that people will

spontaneously framedecisions in terms of topicalaccounts that, in the contextof decision making, play arole analogous to that of“good forms” in perceptionand of basic-level categoriesin cognition. Topicalorganization, in conjunctionwith the concavity of value,entails that thewillingness totravel to theother store for asaving of $5 on a calculatorshouldbeinverselyrelatedto

thepriceofthecalculatorandshould be independent of theprice of the jacket. To testthis prediction, weconstructed another versionof the problem in which theprices of the two itemswereinterchanged.Thepriceofthecalculatorwas given as $125in the first store and $120 inthe other branch, and thepriceof the jacketwas set at$15. As predicted, theproportions of respondents

who said they would makethetripdifferedsharplyinthetwo problems. The resultsshowed that 68% of therespondents (N = 88) werewilling to drive to the otherbranch to save $5 on a $15calculator, but only 29% of93 respondents were willingtomakethesametriptosave$5ona$125calculator.Thisfinding cThinchsupports thenotionoftopicalorganizationof accounts, since the two

versions are identical both interms of a minimal and acomprehensiveaccount.The significance of topical

accounts for consumerbehavior is confirmed by theobservation that the standarddeviation of the prices thatdifferentstoresinacityquotefor the same product isroughly proportional to theaverage price of that product(Pratt,Wise, and Zeckhauser1979).Sincethedispersionof

prices is surely controlled byshoppers’ efforts to find thebestbuy,theseresultssuggestthat consumers hardly exertmore effort to save $15 on a$150purchasethantosave$5ona$50purchase.The topical organization of

mental accounts leads peopletoevaluategainsandlossesinrelative rather than inabsolute terms, resulting inlarge variations in the rate atwhich money is exchanged

for other things, such as thenumber of phone calls madeto find a good buy or thewillingness to drive a longdistance to get one. Mostconsumers will find it easiertobuyacar stereosystemoraPersianrug,respectively,inthecontextofbuyingacarora house than separately.These observations, ofcourse, run counter to thestandard rational theory ofconsumer behavior, which

assumes invariance and doesnot recognize the effects ofmentalaccounting.The following problems

illustrate another example ofmental accounting in whichthe posting of a cost to anaccount is controlled bytopicalorganization:

Problem 8 (N= 200):Imagine that you havedecidedtoseeaplayandpaid theadmissionprice

of$10perticket.Asyouenter the theater, youdiscover that you havelost the ticket. The seatwasnotmarked,andtheticket cannot berecovered.Wouldyoupay$10foranotherticket?Yes(46%)No(54%)

Problem 9 (N= 183):Imagine that you have

decided to see a playwhere admission is $10per ticket. As you enterthetheater,youdiscoverthatyouhave losta$10bill.Would you still pay$10 for a ticket for theplay?Yes(88%)No(12%)

The difference between theresponses to the twoproblems is intriguing. Why

aresomanypeopleunwillingtospend$10afterhavinglostaticket,iftheywouldreadilyspendthatsumafterlosinganequivalent amount of cash?Weattributethedifferencetothe topical organization ofmentalaccounts.Goingtothetheaterisnormallyviewedasa transaction in which thecostoftheticketisexchangedfor the experience of seeingthe play. Buying a secondticket increases the cost of

seeingtheplaytoalevelthatmany respondents apparentlyfindunacceptable.Incontrast,the loss of the cash is notposted to the account of theplay, and it affects thepurchase of a ticket only bymaking the individual feelslightlylessaffluent.An interesting effect was

observed when the twoversionsof theproblemwerepresented to the samesubjects. The willingness to

replacealostticketincreasedsignificantly when thatproblem followed the lost-cash version. In contrast, thewillingness to buy a ticketafter losing cash was notaffectedbypriorpresentationof the other problem. Thejuxtaposition of the twoproblems apparentclemosition ly enabled thesubjects to realize that itmakes sense to think of thelostticketaslostcash,butnot

viceversa.The normative status of the

effects of mental accountingisquestionable.Unlikeearlierexamples, such as the publichealth problem, inwhich thetwo versions differed only inform,itcanbearguedthatthealternative versions of thecalculatorandticketproblemsdiffer also in substance. Inparticular, it may be morepleasurable to save $5 on a$15purchasethanonalarger

purchase,anditmaybemoreannoyingtopaytwiceforthesame ticket than to lose $10in cash. Regret, frustration,and self-satisfaction can alsobe affected by framing(Kahneman and Tversky1982). If such secondaryconsequences are consideredlegitimate, then the observedpreferencesdonotviolatethecriterion of invariance andcannotreadilyberuledoutasinconsistentorerroneous.On

the other hand, secondaryconsequences may changeupon reflection. Thesatisfactionofsaving$5ona$15itemcanbemarrediftheconsumer discovers that shewould not have exerted thesame effort to save $10 on a$200 purchase. We do notwish to recommend that anytwo decision problems thathave the same primaryconsequences should beresolvedinthesameway.We

propose, however, thatsystematic examination ofalternative framings offers auseful reflective device thatcan help decision makersassess the values that shouldbe attached to the primaryand secondary consequencesoftheirchoices.LossesandCostsManydecisionproblems taketheformofachoicebetweenretaining the status quo and

accepting an alternative to it,which is advantageous insome respects anddisadvantageous in others.Theanalysisofvaluethatwasapplied earlier tounidimensional riskyprospects can be extended tothiscasebyassumingthatthestatus quo defines thereference level for allattributes. The advantages ofalternative options will thenbe evaluated as gains and

their disadvantages as losses.Because losses loom largerthan gains, the decisionmakerwillbebiasedinfavorofretainingthestatusquo.Thaler (1980) coined the

term “endowment effect” todescribe the reluctance ofpeopletopartfromassetsthatbelong to their endowment.When it is more painful togive up an asset than it ispleasurable to obtain it,buying prices will be

significantly lower thanselling prices. That is, thehighest price that anindividualwillpaytoacquirean asset will be smaller thanthe minimal compensationthat would induce the sameindividual to give up thatasset, once acquired. Thalerdiscussed some examples ofthe endowment effect in thebehavior of consumers andentrepreneurs.Severalstudieshave reported substantial

discrepanciesbetweenbuyingand selling prices in bothhypothetical and realtransactions (Gregory 1983;Hammack and Brown 1974;Knetsch and Sinden 1984).These results have beenpresented as challenges tostandard economic theory, inwhich buying and sellingprices coincide except fortransaction costs and effectsof wealth.We also observedreluctance to trade ina study

of choices betweenhypotheticaljobsthatdifferedin weekly salary (S) and inthe temperature (T) of theworkplace. Our respondentswere asked to imagine thattheyheldaparticularposition(S1,T1) andwereoffered theoption of moving to adifferent position (S2, T2),which was better in onerespectandworse inanother.We found that most subjects

whowereassignedto(S1,T1)did notwish tomove to (S2,T2), and c2< that mostsubjects who were assignedto the latter position did notwish to move to the former.Evidently, the samedifference in pay or inworking conditions loomslarger as a disadvantage thanasanadvantage.In general, loss aversion

favors stability over change.

Imagine two hedonicallyidentical twins who find twoalternative environmentsequally attractive. Imaginefurther that by force ofcircumstance the twins areseparated and placed in thetwoenvironments.Assoonastheyadopttheirnewstatesasreference points and evaluatethe advantages anddisadvantagesofeachother’senvironments accordingly,the twins will no longer be

indifferent between the twostates,andbothwillprefertostaywheretheyhappentobe.Thus, the instability ofpreferences produces apreference for stability. Inaddition to favoring stabilityoverchange,thecombinationof adaptation and lossaversion provides limitedprotection against regret andenvy by reducing theattractiveness of foregonealternatives and of others’

endowments.Loss aversion and the

consequentendowmenteffectare unlikely to play asignificant role in routineeconomic exchanges. Theownerofastore,forexample,does not experience moneypaidtosuppliersaslossesandmoney received fromcustomers as gains. Instead,the merchant adds costs andrevenuesoversomeperiodoftime and only evaluates the

balance.Matchingdebits andcredits are effectivelycanceled prior to evaluation.Paymentsmadebyconsumersare also not evaluated aslosses but as alternativepurchases. In accord withstandard economic analysis,money isnaturallyviewedasa proxy for the goods andservices that it could buy.This mode of evaluation ismade explicit when anindividual has in mind a

particularalternative,suchas,“I can either buy a newcameraoranewtent.”Inthisanalysis, a personwill buy acamera if itssubjectivevalueexceedsthevalueofretainingthemoneyitwouldcost.There are cases in which a

disadvantage can be framedeitherasacostorasaloss.Inparticular, the purchase ofinsurancecanalsobe framedas a choice between a sureloss and the risk of a greater

loss. In such cases the cost-loss discrepancy can lead tofailures of invariance.Consider, for example, thechoicebetweenasurelossof$50anda25%chancetolose$200. Slovic, Fischhoff, andLichtenstein (1982) reportedthat 80% of their subjectsexpressed a risk-seekingpreference for the gambleover the sure loss. However,only35%of subjects refusedto pay $50 for insurance

against a 25% risk of losing$200. Similar results werealso reported by Schoemakerand Kunreuther (1979) andby Hershey and Schoemaker(1980). We suggest that thesame amount of money thatwas framed as anuncompensated loss in thefirst problem was framed asthe cost of protection in thesecond.Themodalpreferencewas reversed in the twoproblems because losses are

moreaversivethancosts.Wehaveobservedasimilar

effect in thepositivedomain,asillustratedbythefollowingpairofproblems:

Problem 10:Would youaccept a gamble thatoffers a 10% chance towin $95 and a 90%chancetolose$5?

Problem 11:Would youpay$5toparticipateina

lotterythatoffersa10%chance towin $100 anda 90% chance to winnothing?

Atotalof132undergraduatesanswered the two questions,which were separated by ashort filler problem. Theorder of the questions wasreversed for half therespondents. Although it iseasilyconfirmed that the twoproblemsofferobjecticoffler

problevely identical options,55 of the respondentsexpressed differentpreferences in the twoversions. Among them, 42rejected the gamble inProblem 10 but accepted theequivalent lottery in Problem11. The effectiveness of thisseemingly inconsequentialmanipulation illustrates boththe cost-loss discrepancy andthe power of framing.Thinking of the $5 as a

payment makes the venturemoreacceptablethanthinkingofthesameamountasaloss.The preceding analysis

implies that an individual’ssubjective state can beimproved by framingnegative outcomes as costsrather than as losses. Thepossibility of suchpsychological manipulationsmay explain a paradoxicalform of behavior that couldbe labeled the dead-loss

effect. Thaler (1980)discussed the example of aman who develops tenniselbow soon after paying themembership fee in a tennisclubandcontinues toplay inagony to avoid wasting hisinvestment. Assuming thattheindividualwouldnotplayif he had not paid themembershipfee, thequestionarises: How can playing inagony improve theindividual’s lot? Playing in

pain, we suggest, maintainsthe evaluation of themembership fee as a cost. Ifthe individual were to stopplaying, he would be forcedtorecognizethefeeasadeadloss, which may be moreaversivethanplayinginpain.

ConcludingRemarksThe concepts of utility andvalue are commonly used intwo distinct senses: (a)

experience value, the degreeof pleasure or pain,satisfaction or anguish in theactual experience of anoutcome; and (b) decisionvalue, the contribution of ananticipated outcome to theoverall attractiveness oraversivenessofanoptioninachoice. The distinction israrely explicit in decisiontheory because it is tacitlyassumed that decision valuesand experience values

coincide. This assumption ispart of the conception of anidealizeddecisionmakerwhois able to predict futureexperiences with perfectaccuracyandevaluateoptionsaccordingly. For ordinarydecision makers, however,the correspondence ofdecision values betweenexperiencevalues is far fromperfect (March 1978). Somefactors that affect experiencearenoteasilyanticipated,and

some factors that affectdecisions do not have acomparable impact on theexperienceofoutcomes.In contrast to the large

amount of research ondecision making, there hasbeen relatively littlesystematic exploration of thepsychophysics that relatehedonic experience toobjective states. The mostbasic problem of hedonicpsychophysics is the

determination of the level ofadaptation or aspiration thatseparates positive fromnegative outcomes. Thehedonic reference point islargely determined by theobjective status quo, but it isalso affected by expectationsand social comparisons. Anobjectiveimprovementcanbeexperienced as a loss, forexample, when an employeereceives a smaller raise thaneveryone else in the office.

Theexperienceofpleasureorpainassociatedwithachangeof state is also criticallydependentonthedynamicsofhedonicadaptation.Brickmanand Campbell’s (1971)concept of the hedonictreadmill suggests the radicalhypothesis that rapidadaptation will cause theeffects of any objectiveimprovement to be short-lived. The complexity andsubtlety of hedonic

experience make it difficultfor the decision maker toanticipate the actualexperiencethatoutcomeswillproduce.Manyapersonwhoordered a meal whenravenously hungry hasadmitted to a big mistakewhen the fifth course arrivedon the table. The commonmismatch of decision valuesand experience valuesintroduces an additionalelement of uncertainty in

manydecisionproblems.The prevalence of framing

effects and violations ofinvariance furthercomplicates the relati cesmakerwon between decisionvaluesandexperiencevalues.The framing of outcomesoften inducesdecisionvaluesthat have no counterpart inactual experience. Forexample, the framing ofoutcomes of therapies forlung cancer in terms of

mortality or survival isunlikely to affect experience,although it can have apronounced influence onchoice. In other cases,however, the framing ofdecisions affects not onlydecision but experience aswell. For example, theframing of an expenditure asan uncompensated loss or asthe price of insurance canprobably influence theexperience of that outcome.

In such cases, the evaluationofoutcomesinthecontextofdecisionsnotonlyanticipatesexperiencebutalsomoldsit.

References Allais,M.,andO.Hagen,

eds. 1979. ExpectedUtility Hypotheses andthe Allais Paradox.Hingham, MA: D.Reidel.Bernoulli, D. 1954

[1738]. “Expositionof aNew Theory on theMeasurement of Risk.”Econometrica 22: 23–36.Brickman, P., and D. T.Campbell. 1971.“Hedonic Relativismand Planning the GoodSociety.” In AdaptationLevel Theory: ASymposium, ed. M. H.Appley. New York:Academic Press, 287–

302.Clark, H. H., and E. V.Clark. 1977.Psychologyand Language. NewYork:Harcourt.Erakar, S. E., and H. C.Sox. 1981. “Assessmentof Patients’ Preferencesfor TherapeuticOutcomes.” MedicalDecisionMaking 1: 29–39.Fischhoff, B. 1983.“Predicting Frames.”

Journal of ExperimentalPsychology: Learning,Memory and Cognition9:103–16.Fischhoff, B., P. Slovic,and S. Lichtenstein.1980. “Knowing WhatYou Want: MeasuringLabile Values.” InCognitive Processes inChoice and DecisionBehavior, ed. T.Wallsten. Hillsdale, NJ:Erlbaum,117–41.

Fishburn,P.C.,andG.A.Kochenberger. 1979.“Two-Piece vonNeumann–MorgensternUtility Functions.”Decision Sciences 10:503–18.Gregory, R. 1983.“Measures ofConsumer’s Surplus:Reasons for theDisparity in ObservedValues.” Unpublishedmanuscript, Keene State

College,Keene,NH.Hammack, J., and G. M.Brown Jr. 1974.Waterfowl andWetlands: TowardBioeconomic Analysis.Baltimore: JohnsHopkins UniversityPress.Hershey, J. C., and P. J.H. Schoemaker. 1980.“Risk Taking andProblem Context in theDomain of Losses: An

Expected-UtilityAnalysis.” Journal ofRisk and Insurance 47:111–32.Kahneman, D., and A.Tversky. 1979.“Prospect Theory: AnAnalysis of Decisionunder Risk.”Econometrica 47: 263–91.———. 1982. “TheSimulationHeuristic.”InJudgment Under

Uncertainty: Heuristicsand Biases, ed. D.Kahneman, P. Slovic,and A. Tver c, aistsky.New York: CambridgeUniversity Press, 201–208.Knetsch,J.,andJ.Sinden.1984. “Willingness toPay and CompensationDemanded:Experimental Evidenceof an UnexpectedDisparityinMeasuresof

Value.” QuarterlyJournal of Economics99:507–21.March, J. G. 1978.“Bounded Rationality,Ambiguity, and theEngineering of Choice.”Bell Journal ofEconomics9:587–608.McNeil,B.,S.Pauker,H.SoxJr.,andA.Tversky.1982.“OntheElicitationof Preferences forAlternative Therapies.”

NewEnglandJournalofMedicine306:1259–62.Payne, J. W., D. J.Laughhunn, and R.Crum. 1980.“TranslationofGamblesand Aspiration LevelEffects in Risky ChoiceBehavior.” ManagementScience26:1039–60.Pratt,J.W.,D.Wise,andR. Zeckhauser. 1979.“Price Differences inAlmost Competitive

Markets.” QuarterlyJournal of Economics93:189–211.Savage, L. J. 1954. TheFoundationof Statistics.NewYork:Wiley.Schlaifer, R. 1959.Probability andStatistics for BusinessDecisions. New York:McGraw-Hill.Schoemaker, P.J.H., andH.C.Kunreuther. 1979.“AnExperimentalStudy

of InsuranceDecisions.”Journal of Risk andInsurance46:603–18.Slovic, P., B. Fischhoff,and S. Lichtenstein.1982. “Response Mode,Framing, andInformationProcessingEffects in RiskAssessment.” In NewDirections forMethodology of SocialandBehavioral Science:Question Framing and

Response Consistency,ed. R. Hogarth. SanFrancisco: Jossey-Bass,21–36.Thaler,R.1980.“Towarda Positive Theory ofConsumer Choice.”Journal of EconomicBehavior andOrganization1:39–60.———. 1985. “UsingMental Accounting in aTheory of ConsumerBehavior.” Marketing

Science4:199–214.Tversky, A. 1977. “Onthe Elicitation ofPreferences: Descriptiveand PrescriptiveConsiderations.” InConflictingObjectivesinDecisions, ed. D. Bell,R. L. Kenney, and H.Raiffa. New York:Wiley,209–22.Tversky, A., and D.Kahneman. 1981. “TheFraming of Decisions

and the Psychology ofChoice.” Science 211:453–58.vonNeumann, J., and O.Morgenstern. 1947.Theory of Games andEconomicBehavior,2nded. Princeton: PrincetonUniversityPress.

P

AlsobyDanielKahneman

InternationalDifferencesinWell-Bf,aisan

(writtenwithEdDienerandJohnF.Helliwell)

HeuristicsandBiases:ThePsychologyofIntuitive

Judgment(editedwithThomasGilovich

andDaleGriffin)Choices,Values,andFrames(editedwithAmosTversky)Well-Being:TheFoundations

ofHedonicPsychology(editedwithEdwardDienerandNorbertSchwartz)

JudgmentUnderUncertainty:HeuristicsandBiases

(editedwithPaulSlovicandAmosTversky)

AttentionandEffort

P

Acknowledgments

I am fortunate to havemanyfriends and no shame aboutaskingforhelp.Everyoneofmy friends has beenapproached, some of themmanytimes,withrequestsforinformation or editorialsuggestions. I apologize fornot listing them all. A few

individuals played a majorrole in making the bookhappen.MythanksgofirsttoJason Zweig, who urged meinto the project and patientlytriedtoworkwithmeuntil itbecame clear to both of usthat I am impossible toworkwith. Throughout, he hasbeen generous with hiseditorial advice and enviableerudition, and sentences thathe suggested dot the book.Roger Lewin turned

transcriptsofasetoflecturesinto chapter draft s. MaryHimmelstein providedvaluable assistancethroughout. John Brockmanbeganasanagentandbecamea trusted friend. Ran Hassinprovided advice andencouragement when it wasmost needed. In the finalstagesofalongjourneyIhadtheindispensablehelpofEricChinski, my editor at Farrar,Straus and Giroux. He knew

thebookbetterthanIdidandthe work became anenjoyable collaboration—Ihad not imagined that aneditor could do as much asEric did. My daughter,LenoreShoham,ralliedroundtohelpmethroughthehecticfinal months, providingwisdom, a sharp critical eye,andmanyof thesentences inthe “Speaking of” sections.My wife, Anne Treisman,went through a lot and did a

lot—I would have given uplong ago without her steadysupport,wisdom,andendlesspatience.

P

Notes

Introductionprone to collect too fewobservations:We had read abook that criticizedpsychologists for using smallsamples, but did not explaintheir choices: Jacob Cohen,StatisticalPowerAnalysisfor

the Behavioral Sciences(Hillsdale, NJ: Erlbaum,1969).questionaboutwords: I haveslightly altered the originalwording, which referred toletters in the first and thirdpositionofwords.negative viewof themind:Aprominent Germanpsychologist has been ourmost persistent critic. GerdGigerenzer, “How to MakeCognitive Illusions

Disappear,”EuropeanReviewof Social Psychology 2(1991): 83–115. GerdGigerenzer, “PersonalReflections on Theory andPsychology,” Theory &Psychology 20 (2010): 733–43. Daniel Kahneman andAmos Tversky, “On theReality of CognitiveIllusions,” PsychologicalReview103(1996):582–91.offeredplausiblealternatives:Some examples from many

are Valerie F. Reyna andFarrell J. Lloyd, “PhysicianDecision-MakingandCardiacRisk: Effects of Knowledge,Risk Perception, RiskTolerance and Fuzzy-Processing,” Journal ofExperimental Psychology:Applied 12 (2006): 179–95.Nicholas Epley and ThomasGilovich, “The Anchoring-and-Adjustment Heuristic,”Psychological Science 17(2006): 311–18. Norbert

Schwarz et al., “Ease ofRetrieval of Information:Another Look at theAvailability Heuristic,”Journal of Personality andSocialPsychology61(1991):195–202. Elke U. Weber etal.,“AsymmetricDiscountingin Intertemporal Choice,”Psychological Science 18(2007): 516–23. George F.Loewenstein et al., “Risk asFeelings,” PsychologicalBulletin127(2001):267–86.

Nobel Prize that I received:The prize awarded ineconomics is namedBank ofSweden Prize in EconomicSciences in Memory ofAlfred Nobel. It was firstgivenin1969.Somephysicalscientists were not pleasedwith the addition of a NobelPrize in social science, andthe distinctive label of theeconomics prize was acompromise.prolonged practice: Herbert

Simon and his students atCarnegieMelloninthe1980sset the foundations for ourunderstanding of expertise.For an excellent popularintroduction to the subject,see Joshua Foer,Moonwalking with Einstein:The Art and Science ofRemembering (New York:Penguin Press, 2011). Hepresents work that isreviewed in more technicaldetail in K. Anders Ericsson

et al., eds., The CambridgeHandbook of Expertise andExpert Performance (NewYork: Cambridge UniversityPress,2006.)kitchenwas on fire: GaryA.Klein, Sources of Power(Cambridge,MA:MITPress,1999).studied chess masters:HerbertSimonwasoneofthegreatscholarsofthetwentiethcentury, whose discoveriesand inventions ranged from

political science (where hebegan his career) toeconomics (inwhich hewona Nobel Prize) to computerscience (in which he was apioneer)andtopsychology.“The situation…recognition”: Herbert A.Simon, “What Is anExplanation of Behavior?”Psychological Science 3(1992):150–61.affect heuristic: The conceptof the affect heuristic was

developed by Paul Slovic, aclassmate of Amos’s atMichigan and a lifelongfriend.without noticing thesubstitution:.

1:TheCharactersoftheStory

offered many labels: Forreviews of the field, seeJonathanSt.B.T.EvansandKeith Frankish, eds., In Two

Minds: Dual Processes andBeyond (New York: OxfordUniversity Press, 2009);Jonathan St. B. T. Evans,“Dual-Processing Accountsof Reasoning, Judgment, andSocial Cognition,” AnnualReview of Psychology 59(2008):25{59eight="0%"5–78. Among the pioneers areSeymour Epstein, JonathanEvans, StevenSloman,KeithStanovich,andRichardWest.I borrow the terms System 1

and System 2 from earlywritings of Stanovich andWest that greatly influencedmy thinking: Keith E.Stanovich and Richard F.West,“IndividualDifferencesin Reasoning: Implicationsfor the Rationality Debate,”Behavioral and BrainSciences23(2000):645–65.subjective experience ofagency: This sense of freewill is sometimes illusory, asshown inDanielM.Wegner,

The Illusion of ConsciousWill (Cambridge, MA:BradfordBooks,2003).attention is totally focusedelsewhere: Nilli Lavie,“Attention, Distraction andCognitive Control UnderLoad,”CurrentDirections inPsychological Science 19(2010):143–48.conflict between the twosystems:IntheclassicStrooptask,youareshownadisplayofpatchesofdifferentcolors,

orofwordsprintedinvariouscolors.Yourtaskistocalloutthe names of the colors,ignoring thewords. The taskis extremely difficult whenthe colored words arethemselves names of color(e.g., GREEN printed in red,followed by Y ELLOWprintedingreen,etc.).psychopathic charm:Professor Hare wrote me tosay, “Your teacher wasright,” March 16, 2011.

Robert D. Hare, WithoutConscience: The DisturbingWorld of the PsychopathsAmong Us (New York:Guilford Press, 1999). PaulBabiak and Robert D. Hare,Snakes in Suits: WhenPsychopaths Go to Work(NewYork:Harper,2007).little people: Agents withinthe mind are calledhomunculi and are (quiteproperly) objects ofprofessionalderision.

space in your workingmemory: Alan D. Baddeley,“Working Memory: LookingBackandLookingForward,”Nature Reviews:Neuroscience 4 (2003): 829–38. Alan D. Baddeley, YourMemory: A User’s Guide(New York: Firefly Books,2004).

2:AttentionandEffort

AttentionandEffort:Muchofthe material of this chapterdraws on my Attention andEffort (1973). It is availablefor free download on mywebsite(www.princeton.edu/~kahneman/docs/attention_and_effort/Attention_hi_quality.pdfThemain themeof thatbookistheideaofalimitedabilityto pay attention and exertmental effort. Attention andeffort were consideredgeneral resources that could

be used to support manymental tasks. The idea ofgeneral capacity iscontroversial, but it has beenextended by otherpsychologists andneuroscientists, who foundsupport for it in brainresearch. SeeMarcel A. JustandPatriciaA.Carpenter,“ACapacity Theory ofComprehension: IndividualDifferences in WorkingMemory,” Psychological

Review 99 (1992): 122–49;Marcel A. Just et al.,“Neuroindices of CognitiveWorkload: Neuroimaging,Pupillometric and Event-Related Potential Studies ofBrain Work,” TheoreticalIssuesinErgonomicsScience4(2003):56–88.Thereisalsogrowing experimentalevidence for general-purposeresources of attention, as inEvie Vergauwe et al., “DoMental Processes Share a

Domain-General Resource?”Psychological Science 21(2010): 384–90. There isimaging evidence that themere anticipation of a high-effort task mobilizes activityin many areas of the brain,relativetoalow-efforttaskofthe same kind. Carsten N.Boehler et al., “Task-Load-Dependent Activation ofDopaminergic MidbrainAreas in the Absence ofReward,” Journal of

Neuroscience 31 (2011):4955–61.pupil of the eye: Eckhard H.Hess, “Attitude and PupilSize,” Scientific American212(1965):46–54.on the subject’s mind: Theword subject reminds somepeople of subjugation andslavery, and the AmericanPsychological Associationenjoins us to use the moredemocratic participant.Unfortunately, the politically

correct label is a mouthful,which occupies memoryspace and slows thinking. Iwill do my best to useparticipant wheneverpossible but will switch tosubjectwhennecessary.heart rate increases: DanielKahneman et al., “Pupillary,Heart Rate, and SkinResistanceChangesDuringaMental Task,” Journal ofExperimental Psychology 79(1969):164–67.

rapidly flashing letters:Daniel Kahneman, JacksonBeatty, and Irwin Pollack,“Perceptual Deficit During aMental Task,” Science 15(1967): 218–19. We used ahalfway mirror so that theobservers saw the lettersdirectlyinfrontofthemwhilefacing the camera. In acontrol condition, theparticipants looked at theletter through a narrowaperture,topreventanyeffect

of thechangingpupilsizeontheir visual acuity. Theirdetection results showed theinverted-V pattern observedwithothersubjects.Much like the electricitymeter:Attemptingtoperformseveraltasksatoncemayruninto difficulties of severalkinds. For example, it isphysically impossible to saytwodifferentthingsatexactlythesame time,and itmaybeeasiertocombineanauditory

and a visual task than tocombine two visual or twoauditory tasks. Prominentpsychological theories haveattempted to attribute allmutual interference betweentasks to competition forseparate mechanisms. SeeAlan D. Baddeley, WorkingMemory (New York: OxfordUniversityPress,1986).Withpractice, people’s ability tomultitask in specific waysmay improve. However, the

widevarietyofverydifferenttasks that interferewith eachother supports the existenceof a general resource ofattention or effort that isnecessaryinmanytasks.Studiesof thebrain:MichaelE. Smith, LindaK.McEvoy,and Alan Gevins,“Neurophysiological IndicesofStrategyDevelopment andSkill Acquisition,” CognitiveBrain Research 7 (1999):389–404.AlanGevins et al.,

“High-Resolution EEGMapping of CorticalActivation Related toWorkingMemory:Effects ofTask Difficulty, Type ofProcessing and Practice,”Cerebral Cortex 7 (1997):374–85.less effort to solve the sameproblems: For example,SylviaK.AhernandJacksonBeatty showed thatindividualswhoscoredhigheron the SAT showed smaller

pupillary dilations than lowscorers in responding to thesame task. “PhysiologicalSigns of InformationProcessing Vary withIntelligence,” Science 205(1979):1289–92.“lawof leasteffort”:WouterKool et {ute979): 1289al.,“Decision Making and theAvoidance of CognitiveDemand,” Journal ofExperimental Psychology—General 139 (2010):665–82.

Joseph T. McGuire andMatthewM.Botvinick, “TheImpact of AnticipatedDemand on Attention andBehavioral Choice,” inEffortlessAttention,ed.BrianBruya (Cambridge, MA:BradfordBooks,2010),103–20.balanceofbenefitsandcosts:Neuroscientists haveidentified a region of thebrainthatassessestheoverallvalue of an actionwhen it is

completed. The effort thatwasinvestedcountsasacostin this neural computation.Joseph T. McGuire andMatthew M. Botvinick,“PrefrontalCortex,CognitiveControl, and theRegistrationof Decision Costs,” PNAS107(2010):7922–26.read distracting words:BrunoLaengetal.,“PupillaryStroop Effects,” CognitiveProcessing12(2011):13–21.associate with intelligence:

Michael I. Posner and MaryK. Rothbart, “Research onAttention Networks as aModel for the Integration ofPsychological Science,”AnnualReviewofPsychology58 (2007): 1–23. JohnDuncan et al., “A NeuralBasis for GeneralIntelligence,” Science 289(2000):457–60.under timepressure: StephenMonsell, “Task Switching,”Trends in Cognitive Sciences

7(2003):134–40.working memory: Baddeley,WorkingMemory.tests of general intelligence:AndrewA.Conway,MichaelJ. Kane, and Randall W.Engle, “Working MemoryCapacity and Its Relation toGeneral Intelligence,”Trendsin Cognitive Sciences 7(2003):547–52.Israeli Air Force pilots:Daniel Kahneman, RachelBen-Ishai, and Michael

Lotan, “Relationof aTestofAttention to RoadAccidents,” Journal ofApplied Psychology 58(1973): 113–15. DanielGopher, “A SelectiveAttention Test as a Predictorof Success in FlightTraining,”HumanFactors24(1982):173–83.

3:TheLazyController

“optimal experience”:Mihaly Csikszentmihalyi,Flow: The Psychology ofOptimal Experience (NewYork:Harper,1990).sweet tooth: Baba Shiv andAlexanderFedorikhin,“Heartand Mind in Conflict: TheInterplay of Affect andCognition in ConsumerDecisionMaking,”JournalofConsumer Research 26(1999):278–92.MalteFriese,

Wilhelm Hofmann, andMichaela Wänke, “WhenImpulses Take Over:Moderated PredictiveValidity of Implicit andExplicitAttitudeMeasures inPredicting Food Choice andConsumption Behaviour,”British Journal of SocialPsychology 47 (2008): 397–419.cognitively busy: Daniel T.Gilbert, “How MentalSystems Believe,” American

Psychologist46(1991):107–19.C.NeilMacraeandGalenV. Bodenhausen, “SocialCognition: ThinkingCategorically about Others,”AnnualReviewofPsychology51(2000):93–120.po{"><21;:SianL.Beilockand ThomasH. Carr, “WhenHigh-Powered People Fail:Working Memory andChoking Under Pressure inMath,”PsychologicalScience16(2005):101–105.

exertion of self-control:MartinS.Haggeretal.,“EgoDepletion and the StrengthModel of Self-Control: AMeta-Analysis,”Psychological Bulletin 136(2010):495–525.resist the effects of egodepletion:MarkMuravenandElisaveta Slessareva,“MechanismsofSelf-ControlFailure: Motivation andLimited Resources,”Personality and Social

Psychology Bulletin 29(2003): 894–906. MarkMuraven, Dianne M. Tice,and Roy F. Baumeister,“Self-Control as a LimitedResource: RegulatoryDepletion Patterns,” Journalof Personality and SocialPsychology 74 (1998): 774–89.more than a mere metaphor:Matthew T. Gailliot et al.,“Self-Control Relies onGlucoseasaLimitedEnergy

Source: Willpower Is MoreThanaMetaphor,”JournalofPersonality and SocialPsychology 92 (2007): 325–36. Matthew T. Gailliot andRoy F. Baumeister, “ThePhysiology of Willpower:Linking Blood Glucose toSelf-Control,” Personalityand Social PsychologyReview11(2007):303–27.egodepletion:Gailliot,“Self-ControlReliesonGlucoseasaLimitedEnergySource.”

depletioneffects in judgment:Shai Danziger, JonathanLevav, and Liora Avnaim-Pesso,“ExtraneousFactorsinJudicial Decisions,” PNAS108(2011):6889–92.intuitive—incorrect—answer:Shane Frederick, “CognitiveReflection and DecisionMaking,” Journal ofEconomic Perspectives 19(2005):25–42.syllogism as valid: Thissystematic error is known as

thebeliefbias.Evans,“Dual-Processing Accounts ofReasoning, Judgment, andSocialCognition.”call them more rational:Keith E. Stanovich,RationalityandtheReflectiveMind (New York: OxfordUniversityPress,2011).cruel dilemma: WalterMischel and Ebbe B.Ebbesen,“Attention inDelayof Gratification,” Journal ofPersonality and Social

Psychology 16 (1970): 329–37.“There were no toys…distress”: Inge-Marie Eigstiet al., “Predicting CognitiveControl from Preschool toLateAdolescenceandYoungAdulthood,” PsychologicalScience17(2006):478–84.higher scores on tests ofintelligence: Mischel andEbbesen,“Attention inDelayof Gratification.” WalterMischel, “Processes inDelay

ofGratification,”inAdvancesin Experimental SocialPsychology, Vol. 7, ed.Leonard Berkowitz (SanDiego, CA: Academic Press,1974), 249–92. WalterMischel, Yuichi Shoda, andMonicaL.Rodriguez,“DelayofGratification inChildren,”Science 244 (1989): 933–38.Eigsti, “Predicting CognitiveControl from Preschool toLateAdolescence.”improvementwasmaintained:

M. Rosario Rued { Rocencaet al., “Training, Maturation,andGeneticInfluencesontheDevelopment of ExecutiveAttention,”PNAS102(2005):14931–36.conventional measures ofintelligence: Maggie E.Toplak,RichardF.West,andKeith E. Stanovich, “TheCognitive Reflection Test asaPredictorofPerformanceonHeuristics-and-BiasesTasks,”Memory & Cognition (in

press).

4:TheAssociativeMachine

Associative Machine: CareyK. Morewedge and DanielKahneman, “AssociativeProcesses in IntuitiveJudgment,” Trends inCognitiveSciences14(2010):435–40.beyond your control: Toavoid confusion, I did not

mention in the text that thepupil also dilated. The pupildilatesbothduringemotionalarousal and when arousalaccompanies intellectualeffort.think with your body: PaulaM. Niedenthal, “EmbodyingEmotion,” Science 316(2007):1002–1005.WASH primes SOAP: Theimage is drawn from theworkingofapump.The firstfewdrawsonapumpdonot

bring up any liquid, but theyenable subsequent draws tobeeffective.“findsheityellowinstantly”:John A. Bargh, Mark Chen,and Lara Burrows,“Automaticity of SocialBehavior: Direct Effects ofTrait Construct andStereotype Activation onAction,” Journal ofPersonality and SocialPsychology 71 (1996): 230–44.

words related to old age:Thomas Mussweiler, “DoingIs for Thinking! StereotypeActivation by StereotypicMovements,” PsychologicalScience17(2006):17–21.The Far Side: Fritz Strack,Leonard L. Martin, andSabine Stepper, “InhibitingandFacilitatingConditionsofthe Human Smile: ANonobtrusive Test of theFacialFeedbackHypothesis,”Journal of Personality and

SocialPsychology54(1988):768–77.upsetting pictures: UlfDimberg, Monika Thunberg,and Sara Grunedal, “FacialReactions to EmotionalStimuli: AutomaticallyControlled EmotionalResponses,” Cognition andEmotion16(2002):449–71.listen to messages: Gary L.Wells and Richard E. Petty,“The Effects of Overt HeadMovements on Persuasion:

Compatibility andIncompatibility ofResponses,” Basic andApplied Social Psychology 1(1980):219–30.increase the funding ofschools: Jonah Berger, MarcMeredith, and S. ChristianWheeler, “ContextualPriming:Where People VoteAffects How They Vote,”PNAS105(2008):8846–49.Reminders of money:Kathleen D. Vohs, “The

Psychological Consequencesof Money,” Science 314(2006):1154–56.appealofauthoritarianideas:Jeff Greenberg et al.,“Evidence for TerrorManagement Theory II: TheEffect of Mortality Salienceon Reactions to Those WhoThreaten or Bolster theCulturalWorldview,”Journalof Personality and SocialPsychology{gy“Lady Macbeth effect”:

Chen-Bo Zhong and KatieLiljenquist, “Washing AwayYour Sins: ThreatenedMorality and PhysicalCleansing,” Science 313(2006):1451–52.preferred mouthwash oversoap: Spike Lee andNorbertSchwarz, “Dirty Hands andDirty Mouths: EmbodimentoftheMoral-PurityMetaphorIs Specific to the MotorModality Involved in MoralTransgression,”

Psychological Science 21(2010):1423–25.at a British university:Melissa Bateson, DanielNettle, and Gilbert Roberts,“Cues of Being WatchedEnhance Cooperation in aReal-WorldSetting,”BiologyLetters2(2006):412–14.introduced to that stranger:Timothy Wilson’s Strangersto Ourselves (Cambridge,MA: Belknap Press, 2002)presents a concept of an

“adaptive unconscious” thatissimilartoSystem1.

5:CognitiveEase“Easy” and “Strained”: Thetechnical term for cognitiveeaseisfluency.diverse inputs and outputs:AdamL.AlterandDanielM.Oppenheimer, “Uniting theTribes of Fluency to Form aMetacognitive Nation,”Personality and Social

Psychology Review 13(2009):219–35.“Becoming FamousOvernight”:LarryL. Jacoby,Colleen Kelley, JudithBrown, and JenniferJasechko,“BecomingFamousOvernight: Limits on theAbilitytoAvoidUnconsciousInfluences of the Past,”Journal of Personality andSocialPsychology56(1989):326–38.nicely stated the problem:

Bruce W. A. Whittlesea,Larry L. Jacoby, and KristaGirard, “Illusions ofImmediate Memory:Evidence of an AttributionalBasis for Feelings ofFamiliarity and PerceptualQuality,”Journal ofMemoryand Language 29 (1990):716–32.Theimpressionoffamiliarity:Normally, when you meet afriend you can immediatelyplace and name him; you

often know where you methim last, what he waswearing,andwhatyousaidtoeach other. The feeling offamiliarity becomes relevantonly when such specificmemoriesarenotavailable.Itis a fallback. Although itsreliability is imperfect, thefallback is much better thannothing. It is the sense offamiliarity that protects youfrom the embarrassment ofbeing (andacting) astonished

when you are greeted as anold friend by someone whoonlylooksvaguelyfamiliar.“body temperature of achicken”: Ian Begg, VictoriaArmour, and Thérèse Kerr,“On Believing What WeRemember,” CanadianJournal of BehaviouralScience17(1985):199–214.low credibility: Daniel M.Oppenheimer,“Consequences of EruditeVernacular Utilized

Irrespective of Necessity:Problems with Using LongWords Needlessly,” AppliedCognitive Psychology 20(2006):139–56.when they rhymed: MatthewS. Mc Glone and JessicaTofighbakhsh, “Birds of aFeather FlockConjointly (?):Rhyme as Reas{RhyPsychological Science11(2000):424–28.fictitious Turkish companies:AnujK.ShahandDanielM.

Oppenheimer,“EasyDoesIt:The Role of Fluency in CueWeighting,” Judgment andDecision Making Journal 2(2007):371–79.engaged and analytic mode:Adam L. Alter, Daniel M.Oppenheimer, NicholasEpley, and Rebecca Eyre,“Overcoming Intuition:Metacognitive DifficultyActivates AnalyticReasoning,” Journal ofExperimental Psychology—

General136(2007):569–76.pictures of objects: PiotrWinkielman and John T.Cacioppo,“MindatEasePutsa Smile on the Face:PsychophysiologicalEvidence That ProcessingFacilitationIncreasesPositiveAffect,” Journal ofPersonality and SocialPsychology 81 (2001): 989–1000.small advantage: Adam L.Alter and Daniel M.

Oppenheimer, “PredictingShort-Term StockFluctuations by UsingProcessing Fluency,” PNAS103 (2006). Michael J.Cooper, Orlin Dimitrov, andP. Raghavendra Rau, “ARose.com by Any OtherName,” Journal of Finance56(2001):2371–88.clunky labels: Pascal Pensa,“Nomen Est Omen: HowCompany Names InfluenceShortand Long-Run Stock

Market Performance,” SocialScience Research NetworkWorking Paper, September2006.mere exposure effect: RobertB. Zajonc, “AttitudinalEffects of Mere Exposure,”Journal of Personality andSocial Psychology 9 (1968):1–27.favorite experiments: RobertB.ZajoncandD.W.Rajecki,“Exposure and Affect: AField Experiment,”

Psychonomic Science 17(1969):216–17.never consciously sees:Jennifer L. Monahan, SheilaT. Murphy, and Robert B.Zajonc, “Subliminal MereExposure: Specific, General,and Diffuse Effects,”Psychological Science 11(2000):462–66.inhabiting the shell: D. W.Rajecki, “Effects of PrenatalExposure to Auditory orVisual Stimulation on

Postnatal DistressVocalizations in Chicks,”Behavioral Biology 11(1974):525–36.“The consequences…socialstability”: Robert B. Zajonc,“Mere Exposure:AGatewayto the Subliminal,” CurrentDirections in PsychologicalScience10(2001):227.triadofwords:AnnetteBolte,Thomas Goschke, and JuliusKuhl,“EmotionandIntuition:Effects of Positive and

Negative Mood on ImplicitJudgments of SemanticCoherence,” PsychologicalScience14(2003):416–21.association is retrieved: Theanalysisexcludesall cases inwhich the subject actuallyfound the correct solution. Itshowsthatevensubjectswhowill ultimately fail to find acommon association havesomeideaofwhetherthereisonetobefound.increase cognitive ease:

Sascha Topolinski and FritzStrack, “The Architecture ofIntuition: Fluency andAffectDetermine {ectition IntuitiveJudgments of Semantic andVisual Coherence andJudgmentsofGrammaticalityin Artificial GrammarLearning,” Journal ofExperimental Psychology—General138(2009):39–63.doubled accuracy: Bolte,Goschke,andKuhl,“EmotionandIntuition.”

form a cluster: BarbaraFredrickson, Positivity:Groundbreaking ResearchRevealsHow toEmbrace theHidden Strength of PositiveEmotions, OvercomeNegativity, and Thrive (NewYork:RandomHouse,2009).Joseph P. Forgas andRebekah East, “On BeingHappy and Gullible: MoodEffectsonSkepticismandtheDetection of Deception,”Journal of Experimental

SocialPsychology44(2008):1362–67.smiling reaction: SaschaTopolinski et al., “The Faceof Fluency: SemanticCoherence AutomaticallyElicits a Specific Pattern ofFacial Muscle Reactions,”Cognition and Emotion 23(2009):260–71.“previous research…individuals”: SaschaTopolinski and Fritz Strack,“The Analysis of Intuition:

Processing Fluency andAffect in Judgments ofSemantic Coherence,”Cognition and Emotion 23(2009):1465–1503.

6:Norms,Surprises,andCauses

An observer: DanielKahneman and Dale T.Miller, “Norm Theory:Comparing Reality to ItsAlternatives,” Psychological

Review93(1986):136–53.“tattoo on my back”: Jos J.A. Van Berkum,“Understanding Sentences inContext: What Brain WavesCan Tell Us,” CurrentDirections in PsychologicalScience17(2008):376–80.theword pickpocket: RanR.Hassin, John A. Bargh, andJames S. Uleman,“Spontaneous CausalInferences,” Journal ofExperimental Social

Psychology 38 (2002): 515–22.indicate surprise: AlbertMichotte, The Perception ofCausality (Andover, MA:Methuen, 1963). Alan M.Leslie andStephanieKeeble,“Do Six-Month-Old InfantsPerceive Causality?”Cognition25(1987):265–88.explosive finale: FritzHeiderand Mary-Ann Simmel, “AnExperimental Study ofApparent Behavior,”

American Journal ofPsychology 13 (1944): 243–59.identify bullies and victims:Leslie and Keeble, “Do Six-Month-Old Infants PerceiveCausality?”as we die: Paul Bloom, “IsGod an Accident?” Atlantic,December2005.

7:AMachineforJumpingto

Conclusionselegantexperiment:DanielT.Gilbert,DouglasS.Krull,andPatrick S. Malone,“Unbelieving theUnbelievable: SomeProblems in the Rejection ofFalse Information,” Journalof Personality and SocialPsychology 59 (1990): 601–13.descriptions of two people:Solomon E. Asch, “Forming

{#823.Impressions of Personality,”Journal of Abnormal andSocialPsychology41(1946):258–90.allsixadjectives:Ibid.Wisdom of Crowds: JamesSurowiecki, The Wisdom ofCrowds (New York: AnchorBooks,2005).one-sided evidence: Lyle A.Brenner, Derek J. Koehler,and Amos Tversky, “On theEvaluation of One-Sided

Evidence,” Journal ofBehavioral Decision Making9(1996):59–70.

8:HowJudgmentsHappen

biological roots: AlexanderTodorov,SeanG.Baron,andNikolaas N. Oosterhof,“Evaluating FaceTrustworthiness: A Model-Based Approach,” SocialCognitive and Affective

Neuroscience 3 (2008): 119–27.friendlyorhostile:AlexanderTodorov, Chris P. Said,Andrew D. Engell, andNikolaas N. Oosterhof,“UnderstandingEvaluationofFacesonSocialDimensions,”Trends in Cognitive Sciences12(2008):455–60.may spell trouble: AlexanderTodorov, Manish Pakrashi,and Nikolaas N. Oosterhof,“Evaluating Faces on

Trustworthiness AfterMinimal Time Exposure,”Social Cognition 27 (2009):813–33.Australia, Germany, andMexico: Alexander Todorovet al., “Inference ofCompetence from FacesPredict Election Outcomes,”Science308(2005):1623–26.Charles C. Ballew andAlexander Todorov,“PredictingPoliticalElectionsfrom Rapid and Unreflective

Face Judgments,” PNAS 104(2007): 17948–53.Christopher Y. Olivola andAlexander Todorov, “Electedin 100 Milliseconds:Appearance-Based TraitInferences and Voting,”Journal of NonverbalBehavior34(2010):83–110.watch less television: GabrielLenz and Chappell Lawson,“LookingthePart:TelevisionLeadsLessInformedCitizenstoVoteBasedonCandidates’

Appearance,” AmericanJournal of Political Science(forthcoming).absenceofaspecifictaskset:Amos Tversky and DanielKahneman, “ExtensionalVersus Intuitive Reasoning:The Conjunction Fallacy inProbability Judgment,”Psychological Review 90(1983):293–315.Exxon Valdez: William H.Desvousges et al.,“MeasuringNaturalResource

Damages with ContingentValuation: Tests of Validityand Reliability,” inContingent Valuation: ACriticalAssessment,ed.JerryA. Hausman (Amsterdam:North-Holland, 1993), 91–159.sense of injustice: Stanley S.Stevens, Psychophysics:Introduction to ItsPerceptual, Neural, andSocial Prospect (New York:Wiley,1975).

detected that the wordsrhymed: Mark S. Seidenbergand Michael K. Tanenhaus,“Orthographic Effects onRhymeMonitoring,” Journalof Experimental Psychology—Human Learning andMemory5(1979):546–54.95–96 sentence was literallytrue: Sam Glucksberg,Patricia Gildea, and HowardG.Boo{How>Journal of Verbal Learningand Verbal Behavior 21

(1982):85–98.

9:AnsweringanEasierQuestion

anintuitiveanswertoitcamereadily to mind: Analternative approach tojudgment heuristics has beenproposed by GerdGigerenzer, Peter M. Todd,and the ABC ResearchGroup, in Simple HeuristicsThat Make Us Smart (New

York: Oxford UniversityPress, 1999). They describe“fast and frugal” formalproceduressuchas“Takethebest [cue],” which undersome circumstances generatequite accurate judgments onthebasisoflittleinformation.As Gigerenzer hasemphasized,hisheuristicsaredifferent from those thatAmos and I studied, and hehas stressed their accuracyrather than the biases to

which they inevitably lead.Much of the research thatsupports fast and frugalheuristic uses statisticalsimulationstoshowthat theycould work in some real-lifesituations, but the evidencefor the psychological realityof these heuristics remainsthin and contested. Themostmemorable discoveryassociatedwith this approachis the recognition heuristic,illustratedbyanexamplethat

has become well-known: asubjectwhoisaskedwhichoftwo cities is larger andrecognizes one of themshouldguessthat theonesherecognizes is larger. Therecognition heuristic worksfairly well if the subjectknows that the city sherecognizes is large; if sheknows it to be small,however, she will quitereasonably guess that theunknown city is larger.

Contrary to the theory, thesubjects use more than therecognition cue: Daniel M.Oppenheimer, “Not So Fast!(and Not So Frugal!):Rethinking the RecognitionHeuristic,” Cognition 90(2003): B1–B9. A weaknessof the theory is that, fromwhat we know of the mind,thereisnoneedforheuristicsto be frugal. The brainprocesses vast amounts ofinformation in parallel, and

the mind can be fast andaccurate without ignoringinformation. Furthermore, ithas been known since theearly days of research onchess masters that skill neednotconsistof learning touseless information. On thecontrary, skill is more oftenan ability to deal with largeamounts of informationquicklyandefficiently.bestexamplesofsubstitution:Fritz Strack, Leonard L.

Martin,andNorbertSchwarz,“Priming andCommunication: SocialDeterminants of InformationUse in Judgments of LifeSatisfaction,” EuropeanJournalofSocialPsychology18(1988):429–42.correlations betweenpsychological measures: Thecorrelationwas.66.dominates happiness reports:Other substitution topicsinclude marital satisfaction,

job satisfaction, and leisuretime satisfaction: NorbertSchwarz, Fritz Strack, andHans-Peter Mai,“Assimilation and ContrastEffects in Part-WholeQuestion Sequences: AConversational LogicAnalysis,” Public OpinionQuarterly55(1991):3–23.evaluate their happiness: Atelephone survey conductedin Germany included aquestion about general

happiness. When the self-reports of happiness werecorrelated with the localweather at the time of theinterview, a pronouncedcorrelationwas found.Moodis known to vary with theweather, and substitutionexplains the effect onreportedhappiness.However,another version of thetelephone survey yielded asomewhat different result.These respondents were

asked about the currentweather before they wereasked the happiness quest{ppiournal ofion. For them,weather had no effect at allon reported happiness! Theexplicit priming of weatherprovided them with anexplanation of their mood,undermining the connectionthatwouldnormallybemadebetween current mood andoverallhappiness.view of the benefits: Melissa

L. Finucane et al., “TheAffectHeuristicinJudgmentsof Risks and Benefits,”Journal of BehavioralDecision Making 13 (2000):1–17.

10:TheLawofSmallNumbers

“It is both…withoutadditives”: Howard Wainerand Harris L. Zwerling,“Evidence That Smaller

Schools Do Not ImproveStudent Achievement,” PhiDelta Kappan 88 (2006):300–303. The example wasdiscussedbyAndrewGelmanandDeborahNolan,TeachingStatistics: A Bag of Tricks(New York: OxfordUniversityPress,2002).50% risk of failing: JacobCohen, “The StatisticalPower of Abnormal-SocialPsychological Research: AReview,” Journal of

Abnormal and SocialPsychology 65 (1962): 145–53.“Belief in the Law of SmallNumbers”: Amos Tverskyand Daniel Kahneman,“Belief in the Law of SmallNumbers,” PsychologicalBulletin76(1971):105–10.“statistical intuitions…whenever possible”: Thecontrast that we drewbetween intuition andcomputation seems to

foreshadow the distinctionbetweenSystems1and2,butwewerealongwayfromtheperspective of this book.Weused intuition to coveranything but a computation,any informal way to reach aconclusion.German spies: WilliamFeller, Introduction toProbability Theory and ItsApplications (New York:Wiley,1950).randomness in basketball:

Thomas Gilovich, RobertVallone, and Amos Tversky,“TheHotHandinBasketball:On the Misperception ofRandom Sequences,”Cognitive Psychology 17(1985):295–314.

11:Anchors“‘reasonable’ volume”:Robyn Le Boeuf and EldarShafir, “The Long and Shortof It: Physical Anchoring

Effects,” Journal ofBehavioral Decision Making19(2006):393–406.nod their head: NicholasEpley and Thomas Gilovich,“Putting Adjustment Back inthe Anchoring andAdjustment Heuristic:Differential Processing ofSelf-Generated andExperimenter-ProvidedAnchors,” PsychologicalScience12(2001):391–96.stay closer to the anchor:

Epley and Gilovich, “TheAnchoring-and-AdjustmentHeuristic.”associative coherence:Thomas Mussweiler, “TheUse of Category andExemplar Knowledge in theSolution of AnchoringTasks,” Journal ofPersonality and SocialPsychology78(2000):1038–52.San FranciscoExploratorium: Karen E.

Jacowitz and DanielKahneman, “Measures ofAnchoring in EstimationTasks,”Person{pantionalityand Social PsychologyBulletin21(1995):1161–66.substantially lower: GregoryB. Northcraft and MargaretA. Neale, “Experts,Amateurs, and Real Estate:An Anchoring-and-Adjustment Perspective onProperty Pricing Decisions,”Organizational Behavior and

Human Decision Processes39 (1987): 84–97. The highanchor was 12% above thelisted price, the low anchorwas12%belowthatprice.rolled a pair of dice: BirteEnglich,ThomasMussweiler,and Fritz Strack, “PlayingDice with CriminalSentences: The Influence ofIrrelevant Anchors onExperts’ Judicial DecisionMaking,” Personality andSocialPsychologyBulletin32

(2006):188–200.NO LIMIT PER PERSON: BrianWansink,RobertJ.Kent,andStephen J. Hoch, “AnAnchoring and AdjustmentModel of Purchase QuantityDecisions,” Journal ofMarketing Research 35(1998):71–81.resist the anchoring effect:Adam D. Galinsky andThomas Mussweiler, “FirstOffers asAnchors: TheRoleof Perspective-Taking and

NegotiatorFocus,”JournalofPersonality and SocialPsychology 81 (2001): 657–69.otherwise be much smaller:Greg Pogarsky and LindaBabcock, “Damage Caps,Motivated Anchoring, andBargainingImpasse,”Journalof Legal Studies 30 (2001):143–59.amount of damages: For anexperimental demonstration,see Chris Guthrie, Jeffrey J.

Rachlinski, and Andrew J.Wistrich, “Judging byHeuristic-Cognitive IllusionsinJudicialDecisionMaking,”Judicature86(2002):44–50.

12:TheScienceofAvailability

“theeasewithwhich”:AmosTversky and DanielKahneman, “Availability: AHeuristic for JudgingFrequency and Probability,”

Cognitive Psychology 5(1973):207–32.self-assessed contributions:Michael Ross and FioreSicoly, “EgocentricBiases inAvailabilityandAttribution,”Journal of Personality andSocialPsychology37(1979):322–36.Amajoradvance:Schwarzetal., “Ease of Retrieval asInformation.”role of fluency: SabineStepper and Fritz Strack,

“ProprioceptiveDeterminantsof Emotional andNonemotional Feelings,”Journal of Personality andSocialPsychology64(1993):211–20.experimenters dreamed up:For a review of this area ofresearch, see RainerGreifeneder, Herbert Bless,andMichel T. Pham, “WhenDoPeopleRelyonAffectiveand Cognitive Feelings inJudgment? A Review,”

Personality and SocialPsychology Review 15(2011):107–41.affect their cardiac health:Alexander Rotliman andNorbert Schwarz,“Constructing Perceptions ofVulnerability: PersonalRelevance and the Use ofExperimental Information inHealth Judgments,”Personality and SocialPsychology Bulletin 24(1998):1053–64.

effortful task at the sametime: RainerGreifeneder andHerbert Bless, “Relying onAccessible Content VersusAccessibility Experiences:The Case of ProcessingCapacity,” Social Cognition25(2007):853–81.happy episode in their life:Markus Ruder and HerbertBless, “Mood and theReliance on the Ease ofRetrieval Heuristic,” Journalof Personality and Social

Psychology85(2003):20–32.low on a depression scale:Rainer Greifeneder andHerbert Bless, “Depressionand Reliance on Ease-of-Retrieval Experiences,”European Journal of SocialPsychology 38 (2008): 213–30.knowledgeable novices:Chezy Ofir et al., “Memory-BasedStorePriceJudgments:The Role of Knowledge andShopping Experience,”

Journal of Retailing 84(2008):414–23.true experts: Eugene M.Caruso, “Use ofExperiencedRetrieval Ease in Self andSocial Judgments,” Journalof Experimental SocialPsychology 44 (2008): 148–55.faith in intuition: JohannesKeller and Herbert Bless,“Predicting Future AffectiveStates:HowEaseofRetrievaland Faith in Intuition

Moderate the Impact ofActivated Content,”European Journal of SocialPsychology38(2008):1–10.if they are…powerful: MarioWeick and Ana Guinote,“When SubjectiveExperiences Matter: PowerIncreases Reliance on theEaseofRetrieval,”JournalofPersonality and SocialPsychology 94 (2008): 956–70.

13:Availability,Emotion,andRisk

because of brain damage:Damasio’s idea is known asthe “somatic markerhypothesis” and it hasgathered substantial support:Antonio R. Damasio,Descartes’ Error: Emotion,Reason, and the HumanBrain (New York: Putnam,1994). Antonio R. Damasio,“The Somatic Marker

Hypothesis and the PossibleFunctions of the PrefrontalCortex,” PhilosophicalTransactions: BiologicalSciences351(1996):141–20.risks of each technology:Finucane et al., “The AffectHeuristic in Judgments ofRisks and Benefits.” PaulSlovic, Melissa Finucane,Ellen Peters, and Donald G.MacGregor, “The AffectHeuristic,” in ThomasGilovich, Dale Griffin, and

Daniel Kahneman, eds.,Heuristics and Biases (NewYork: Cambridge UniversityPress, 2002), 397–420. PaulSlovic, Melissa Finucane,Ellen Peters, and Donald G.MacGregor, “Risk asAnalysis and Risk asFeelings: Some ThoughtsAbout Affect, Reason, Risk,and Rationality,” RiskAnalysis 24 (2004): 1–12.PaulSlovic,“Trust,Emotion,Sex, Politics, and Science:

Surveying the Risk-AssessmentBattlefield,”RiskAnalysis19(1999):689–701.British Toxicology Society:Slovic,“Trust,Emotion,Sex,Politics, and Science.” Thetechnologies and substancesused in these studies are notalternative solutions to thesame problem. In realisticproblems, where competitivesolutions are considered, thecorrelationbetweencostsandbenefitsmustbenegative;the

solutions that have {nsproblems,the largest benefitsare also the most costly.Whether laypeople and evenexperts might fail torecognize the correctrelationship even in thosecases is an interestingquestion.“wags the rational dog”:Jonathan Haidt, “TheEmotional Dog and ItsRational Tail: A SocialInstitutionist Approach to

Moral Judgment,”Psychological Review 108(2001):814–34.“‘Risk’ does not exist”: PaulSlovic, The Perception ofRisk (Sterling, VA:EarthScan,2000).availability cascade: TimurKuran and Cass R. Sunstein,“Availability Cascades andRisk Regulation,” StanfordLawReview 51 (1999): 683–768. CERCLA, theComprehensive

Environmental Response,Compensation, and LiabilityAct,passedin1980.nothing in between: PaulSlovic, who testified for theapple growers in the Alarcase, has a rather differentview: “The scare wastriggered by the CBS 60Minutesbroadcastthatsaid4,000 children will die ofcancer(noprobabilitiesthere)along with frighteningpicturesofbald children in a

cancerward—andmanymoreincorrectstatements.Alsothestory exposed EPA’s lack ofcompetence in attending toand evaluating the safety ofAlar, destroying trust inregulatorycontrol.Giventhis,I think the public’s responsewas rational.” (Personalcommunication, May 11,2011.)

14:TomW’s

Specialty“a shy poetry lover”: Iborrowed this example fromMax H. Bazerman and DonA. Moore, Judgment inManagerialDecisionMaking(NewYork:Wiley,2008).always weighted more:Jonathan St. B. T. Evans,“Heuristic and AnalyticProcesses in Reasoning,”BritishJournalofPsychology75(1984):451–68.

the opposite effect: NorbertSchwarz et al., “Base Rates,Representativeness, and theLogic of Conversation: TheContextual Relevance of‘Irrelevant’ Information,”Social Cognition 9 (1991):67–84.told to frown: Alter,Oppenheimer, Epley, andEyre, “OvercomingIntuition.”Bayes’s rule: The simplestform of Bayes’s rule is in

odds form, posterior odds =prior odds × likelihood ratio,where the posterior odds arethe odds (the ratio ofprobabilities) for twocompeting hypotheses.Consider a problem ofdiagnosis. Your friend hastested positive for a seriousdisease. The disease is rare:only1in600ofthecasessentin for testingactuallyhas thedisease. The test is fairlyaccurate. Its likelihood ratio

is25:1,whichmeansthattheprobabilitythatapersonwhohas the disease will testpositive is 25 times higherthantheprobabilityofafalsepositive. Testing positive isfrightening news, but theodds that your friend has thedisease have risen only from1/600 to 25/600, and theprobabilityis4%.ForthehypothesisthatTom

Wisacomputerscientist,theprior odds that correspond to

abaserateof3%are(.03/.97= .031). Assuming alikelihood ratio of 4 (thedescription is 4 times aslikely if Tom W is acomputer scientist than if heisnot),theposterioroddsare4× .031=12.4.From theseodds you can { odes as lcompute that the posteriorprobabilityofTomWbeingacomputer scientist is now11% (because 12.4/112. 4 =.11).

15:Linda:LessisMore

the role of heuristics: AmosTversky and DanielKahneman, “ExtensionalVersus Intuitive Reasoning:The Conjunction Fallacy inProbability Judgment,”Psychological Review90(1983),293-315.“a little homunculus”:Stephen JayGould,Bully forBrontosaurus (New York:

Norton,1991).weakened or explained: See,amongothers,RalphHertwigand Gerd Gigerenzer, “The‘Conjunction Fallacy’Revisited: How IntelligentInferences Look LikeReasoningErrors,”JournalofBehavioral Decision Making12 (1999): 275–305; RalphHertwig, Bjoern Benz, andStefan Krauss, “TheConjunction Fallacy and theMany Meanings of And,”

Cognition 108 (2008): 740–53.settleourdifferences:BarbaraMellers, Ralph Hertwig, andDaniel Kahneman, “DoFrequency RepresentationsEliminate ConjunctionEffects? An Exercise inAdversarial Collaboration,”Psychological Science 12(2001):269–75.

16:CausesTrump

Statisticscorrect answer is 41%:Applying Bayes’s rule inoddsform,theprioroddsarethe odds for the Blue cabfrom the base rate, and thelikelihoodratioistheratiooftheprobabilityof thewitnesssaying thecab isBlue if it isBlue, divided by theprobability of the witnesssaying thecab isBlue if it isGreen: posterior odds =

(.15/.85) × (.80/.20) = .706.The odds are the ratio of theprobability that the cab isBlue, divided by theprobability that the cab isGreen. To obtain theprobability that the cab isBlue, we compute:Probability (Blue) = .706/1.706 = .41. The probabilitythatthecabisBlueis41%.nottoofarfromtheBayesian:Amos Tversky and DanielKahneman, “Causal Schemas

in Judgments UnderUncertainty,” in Progress inSocialPsychology,ed.MorrisFishbein (Hillsdale, NJ:Erlbaum,1980),49–72.University of Michigan:Richard E. Nisbett andEugeneBorgida,“Attributionand the Psychology ofPrediction,” Journal ofPersonality and SocialPsychology 32 (1975): 932–43.relieved of responsibility:

John M. Darley and BibbLatane, “BystanderIntervention in Emergencies:Diffusion of Responsibility,”Journal of Personality andSocial Psychology 8 (1968):377–83.

17:RegressiontotheMean

help of the most brilliantstatisticians:MichaelBulmer,Francis Galton: Pioneer of

Heredity and Biometry(Baltimore: Johns HopkinsUniversityPress,2003).standard scores: Researcherstransform each original scoreinto a standard score bysubtracting the mean anddividing the result by thestandard deviation. Standardscores have a mean of zeroandastandarddeviationof1,can be compared acrossvariables(especiallywhenthestatistica {he deviatiol

distributions of the originalscores are similar), and havemany desirable mathematicalproperties,whichGalton hadtoworkouttounderstandthenature of correlation andregression.correlation between parentand child: This will not betrue in an environment inwhich some children aremalnourished. Differences innutrition will becomeimportant, the proportion of

shared factors will diminish,and with it the correlationbetweentheheightofparentsand the height of children(unless the parents ofmalnourished children werealso stunted by hunger inchildhood).height and weight: Thecorrelationwascomputed fora very large sample of thepopulation of the UnitedStates (the Gallup-Healthways Well-Being

Index).income and education: Thecorrelation appearsimpressive, but I wassurprisedtolearnmanyyearsago from the sociologistChristopher Jencks that ifeveryone had the sameeducation, the inequality ofincome (measured bystandard deviation)would bereduced only by about 9%.Therelevantformulaisv(1–

r2),whereristhecorrelation.correlation and regression:This is true when bothvariables are measured instandard scores—that is,where each score istransformed by removing themean and dividing the resultbythestandarddeviation.confusing mere correlationwith causation: HowardWainer, “The MostDangerous Equation,”

AmericanScientist95(2007):249–56.

18:TamingIntuitivePredictions

farmoremoderate:Theproofof the standard regression asthe optimal solution to theprediction problem assumesthat errors are weighted bythe squared deviation fromthe correct value. This is theleast-squares criterion, which

is commonlyaccepted.Otherloss functions lead todifferentsolutions.

19:TheIllusionofUnderstanding

narrative fallacy: NassimNicholas Taleb, The BlackSwan: The Impact of theHighly Improbable (NewYork:RandomHouse,2007).one attribute that isparticularlysignificant:.

throwing the ball: MichaelLewis,Moneyball:TheArtofWinning an Unfair Game(NewYork:Norton,2003).sell their company: SethWeintraub, “Excite PassedUp Buying Google for$750,000 in 1999,” Fortune,September29,2011.ever felt differently: RichardE. Nisbett and Timothy D.Wilson, “Telling More ThanWe Can Know: VerbalReports on Mental

Processes,” PsychologicalReview84(1977):231–59.United States and the SovietUnion:BaruchFischhoff andRuth Beyth, “I Knew ItWouldHappen:RememberedProbabilities of Once FutureThings,” OrganizationalBehavior and HumanPerformance 13 (1975): 1–16.quality of a decision:Jonathan Baron and John C.Hershey, “Outcome Bias in

Decision{siiv>Evaluation,”Journal of Personality andSocialPsychology54(1988):569–79.should have hired themonitor: Kim A. Kamin andJeffreyRachlinski,“ExPost?Ex Ante: DeterminingLiability in Hindsight,” Lawand Human Behavior 19(1995): 89–104. Jeffrey J.Rachlinski, “A PositivePsychological Theory ofJudging in Hindsight,”

University of Chicago LawReview65(1998):571–625.tidbit of intelligence: JeffreyGoldberg, “Letter fromWashington: Woodward vs.Tenet,”NewYorker,May21,2007, 35–38. Also TimWeiner,LegacyofAshes:TheHistory of the CIA (NewYork: Doubleday, 2007);“Espionage: Inventing theDots,”Economist, November3,2007,100.reluctance to take risks:

Philip E. Tetlock,“Accountability: TheNeglected Social Context ofJudgment and Choice,”Research in OrganizationalBehavior7(1985):297–332.before their currentappointment: MarianneBertrand and AntoinetteSchoar, “Managing withStyle: The Effect ofManagers on Firm Policies,”Quarterly Journal ofEconomics118(2003):1169–

1208. Nick Bloom and JohnVanReenen,“MeasuringandExplaining ManagementPractices Across Firms andCountries,” QuarterlyJournal of Economics 122(2007):1351–1408.“Howoftenwillyou find…”:I am indebted to ProfessorJames H. Steiger ofVanderbilt University, whodeveloped an algorithm thatanswers this question, underplausible assumptions.

Steiger’s analysis shows thatcorrelationsof.20and.40areassociated, respectively, withinversion rates of 43% and37%.his penetrating book: TheHalo Effect was praised asone of the best businessbooksoftheyearbyboththeFinancial Times and TheWall Street Journal: PhilRosenzweig,TheHaloEffect:…and the Eight OtherBusiness Delusions That

Deceive Managers (NewYork: Simon & Schuster,2007).SeealsoPaulOlkandPhil Rosenzweig, “The HaloEffect and the Challenge ofManagement Inquiry: ADialog Between PhilRosenzweig and Paul Olk,”Journal of ManagementInquiry19(2010):48–54.“a visionary company”:JamesC.CollinsandJerry I.Porras, Built to Last:Successful Habits of

Visionary Companies (NewYork:Harper,2002).flipofacoin: In fact,even ifyou were the CEO yourself,your forecasts would not beimpressively reliable; theextensive research on insidertradingshows thatexecutivesdobeatthemarketwhentheytradetheirownstock,butthemargin of theiroutperformance is barelyenough to cover the costs oftrading.SeeH.NejatSeyhun,

“The Information Content ofAggregate Insider Trading,”Journal of Business 61(1988): 1–24; JosefLakonishok and Inmoo Lee,“Are Insider TradesInformative?” Review ofFinancial Studies 14 (2001):79–111; Zahid Iqbal andShekar Shetty, “AnInvestigation of CausalityBetweenInsiderTransactionsand Stock Returns,”Quarterly Review of

Economics and Finance 42(2002):41–57.In Search of Excellence:Rosenz{lenlatweig,TheHaloEffect.“MostAdmiredCompanies”:Deniz Anginer, Kenneth L.Fisher, and Meir Statman,“Stocks of AdmiredCompanies and DespisedOnes,”workingpaper,2007.regressiontothemean:JasonZweig observes that the lackofappreciationfor regression

has detrimental implicationsfor the recruitment of CEOs.Struggling firms tend to turnto outsiders, recruitingCEOsfrom companies with highrecent returns. The incomingCEOthengetscredit,atleasttemporarily, for his newfirm’s subsequentimprovement. (Mean-while,his replacementathis formerfirm is now struggling,leading the new bosses tobelieve that they definitely

hired “the right guy.”)Anytime a CEO jumps ship,the new company must buyout his stake (in stock andoptions) at his old firm,setting a baseline for futurecompensation that hasnothing to do withperformanceat thenew firm.Tensofmillionsofdollars incompensationgetawardedfor“personal” achievements thatare driven mainly byregression and halo effects

(personal communication,December29,2009).

20:TheIllusionofValidity

this startling conclusion:BradM.BarberandTerranceOdean, “Trading IsHazardous to Your Wealth:The Common StockInvestment Performance ofIndividualInvestors,”Journalof Finance 55 (2002): 773–

806.men acted on their uselessideas: Brad M. Barber andTerrance Odean, “Boys WillBe Boys: Gender,Overconfidence, andCommon Stock Investment,”Quarterly Journal ofEconomics 116 (2006): 261–92.selling “winners”: This“disposition effect” isdiscussedfurther.responding tonews:BradM.

Barber and Terrance Odean,“AllThatGlitters:TheEffectofAttentionandNewsontheBuying Behavior ofIndividual and InstitutionalInvestors,” Review ofFinancial Studies 21 (2008):785–818.wealth from amateurs:Research on stock trades inTaiwan concluded that thetransfer of wealth fromindividuals to financialinstitutions amounts to a

staggering 2.2% of GDP:Brad M. Barber, Yi-TsungLee, Yu-Jane Liu, andTerrance Odean, “Just HowMuchDoIndividualInvestorsLosebyTrading?”ReviewofFinancial Studies 22 (2009):609–32.underperform the overallmarket: John C. Bogle,Common Sense on MutualFunds: New Imperatives forthe Intelligent Investor (NewYork:Wiley,2000),213.

persistentdifferences in skill:Mark Grinblatt and SheridanTitman, “The Persistence ofMutual Fund Performance,”Journal of Finance 42(1992): 1977–84. Edwin J.Eltonet al., “ThePersistenceof Risk-Adjusted MutualFund Performance,” Journalof Business 52 (1997): 1–33.Edwin Elton et al.,“Efficiency With CostlyInformation: A Re-interpretation of Evidence

from Managed Portfolios,”ReviewofFinancialStudies6(1993):1–21.“In this age of academichyperspecialization”: PhilipE. Tetlock, Expert PoliticalJudgment:̶>HowGood is It?How Can We Know?(Princeton: PrincetonUniversityPress,2005),233.

21:Intuitionsvs.Formulas

“There is no controversy”:Paul Meehl, “Causes andEffects of My DisturbingLittle Book,” Journal ofPersonality Assessment 50(1986):370–75.a factor of 10 or more:During the 1990–1991auction season, for example,thepriceinLondonofacaseof 1960 Chateau Latouraveraged $464; a case of the1961vintage(oneof thebest

ever) fetched an average of$5,432.Experienced radiologists:PaulJ.Hoffman,PaulSlovic,and Leonard G. Rorer, “AnAnalysis-of-Variance Modelfor the Assessment ofConfiguralCueUtilization inClinical Judgment,”Psychological Bulletin 69(1968):338–39.internal corporate audits:PaulR.Brown,“IndependentAuditor Judgment in the

Evaluation of Internal AuditFunctions,” Journal ofAccounting Research 21(1983):444–55.41 separate studies: JamesShanteau, “PsychologicalCharacteristics andStrategiesof Expert Decision Makers,”ActaPsychologica68(1988):203–15.successive food breaks:Danziger, Levav, andAvnaim-Pesso, “ExtraneousFactors in Judicial

Decisions.”loweringvalidity:RichardA.DeVaul et al., “Medical-School Performance ofInitially Rejected Students,”JAMA 257 (1987): 47–51.Jason Dana and Robyn M.Dawes, “Belief in theUnstructured Interview: ThePersistence of an Illusion,”working paper, Departmentof Psychology, University ofPennsylvania, 2011. WilliamM. Grove et al., “Clinical

Versus MechanicalPrediction: A Meta-Analysis,” PsychologicalAssessment12(2000):19–30.Dawes’s famous article:Robyn M. Dawes, “TheRobust Beauty of ImproperLinear Models in DecisionMaking,” AmericanPsychologist34(1979):571–82.not affected by accidents ofsampling: Jason Dana andRobyn M. Dawes, “The

Superiority of SimpleAlternativestoRegressionforSocial Science Predictions,”Journal of Educational andBehavioral Statistics 29(2004):317–31.Dr. Apgar: Virginia Apgar,“A Proposal for a NewMethod of Evaluation of theNewborn Infant,” CurrentResearchesinAnesthesiaandAnalgesia32(1953):260–67.Mieczyslaw Finster andMargaretWood, “TheApgar

Score Has Survived the TestofTime,”Anesthesiology102(2005):855–57.virtues of checklists: AtulGawande, The ChecklistManifesto: How to GetThings Right (New York:MetropolitanBooks,2009).organic fruit: Paul Rozin,“The Meaning of ‘Natural’:ProcessMore Important thanContent,” PsychologicalScience16(2005):652–58.

2{cemoderated by an arbiter:Mellers, Hertwig, andKahneman, “Do FrequencyRepresentations EliminateConjunctionEffects?”articulated this position:Klein,SourcesofPower.kouros:TheGettyMuseuminLos Angeles brings in theworld’s leading experts onGreek sculpture to view akouros—amarble statue of a

striding boy—that it is abouttobuy.Oneafteranother,theexperts react with what onecalls “intuitive repulsion”—apowerful hunch that thekouros isnot2,500yearsoldbut a modern fake. None ofthe experts can immediatelysay why they think thesculpture is a forgery. Theclosest any of them couldcome to a rationale is anItalian art historian’scomplaint that something—

he does not know exactlywhat—“seemed wrong” withthe statue’s fingernails. AfamousAmericanexpert saidthat the first thought thatcame to his mind was theword fresh, and a Greekexpert flatly stated, “Anyonewhohaseverseenasculpturecoming out of the groundcould tell that that thing hasnever been in the ground.”Thelackofagreementonthereasons for the shared

conclusion is striking, andrathersuspect.admired as a hero: Simonwas one of the toweringintellectual figures of thetwentiethcentury.Hewroteaclassicondecisionmakinginorganizationswhilestillinhistwenties, and among manyother achievements he wenton to be one of the foundersof the field of artificialintelligence, a leader incognitive science, an

influential student of theprocess of scientificdiscovery, a forerunner ofbehavioral economics and,almost incidentally, a Nobellaureateineconomics.“nothing less thanrecognition”: Simon, “WhatIs an Explanation ofBehavior?” David G. Myers,Intuition: Its Powers andPerils (New Haven: YaleUniversityPress,2002),56.“without knowing how he

knows”: Seymour Epstein,“Demystifying Intuition:What It Is, What It Does,How It Does It,”Psychological Inquiry 21(2010):295–312.10,000 hours: Foer,MoonwalkingwithEinstein.

23:TheOutsideViewinside view and the outsideview: The labels are oftenmisunderstood. Numerous

authors believed that thecorrect terms were “insiderview” and “outsider view,”which are not even close towhatwehadinmind.very different answers: DanLovallo and DanielKahneman, “Timid Choicesand Bold Forecasts: ACognitive Perspective onRisk Taking,” ManagementScience 39 (1993): 17–31.Daniel Kahneman and DanLovallo, “Delusions of

Success: How OptimismUndermines Executives’Decisions,”HarvardBusinessReview81(2003):56–63.“Pallid” statisticalinformation: Richard E.Nisbett and Lee D. Ross,Human Inference: Strategiesand Shortcomings of SocialJudgment (EnglewoodCliffs,NJ:Prentice-Hall,1980).impersonality of procedures:Fo {i>How Doctors Think(New York: Mariner Books,

2008),6.planning fallacy: DanielKahneman and AmosTversky, “IntuitivePrediction: Biases andCorrective Procedures,”Management Science 12(1979):313–27.Scottish Parliament building:Rt. Hon. The Lord Fraser ofCarmyllie, “The HolyroodInquiry, Final Report,”September 8, 2004,www.holyroodinquiry.org/FINAL_report/report.htm

did not become more relianton it: Brent Flyvbjerg,MetteK. SkamrisHolm, and SørenL. Buhl, “How (In)accurateAre Demand Forecasts inPublic Works Projects?”Journal of the AmericanPlanning Association 71(2005):131–46.survey of Americanhomeowners: “2002 Cost vs.Value Report,” Remodeling,November20,2002.completion times: Brent

Flyvbjerg,“FromNobelPrizeto Project Management:GettingRisksRight,”ProjectManagement Journal 37(2006):5–15.sunk-cost fallacy: Hal R.ArkesandCatherineBlumer,“The Psychology of SunkCost,” OrganizationalBehavior and HumanDecision Processes 35(1985):124–40.HalR.Arkesand Peter Ayton, “The SunkCost and Concorde Effects:

Are Humans Less RationalThan Lower Animals?”Psychological Bulletin 125(1998):591–600.

24:TheEngineofCapitalism

you already feel fortunate:Miriam A. Mosing et al.,“Genetic and EnvironmentalInfluences on Optimism andIts Relationship to Mentaland Self-Rated Health: A

Study of Aging Twins,”BehaviorGenetics39(2009):597–604. David Snowdon,Aging with Grace: What theNunStudyTeachesUsAboutLeading Longer, Healthier,and More Meaningful Lives(New York: Bantam Books,2001).bright side of everything:Elaine Fox,AnnaRidgewell,and Chris Ashwin, “Lookingon the Bright Side: BiasedAttention and the Human

SerotoninTransporterGene,”Proceedings of the RoyalSociety B 276 (2009): 1747–51.“triumph of hope overexperience”:Manju Puri andDavid T. Robinson,“Optimism and EconomicChoice,”JournalofFinancialEconomics86(2007):71–99.more sanguine than midlevelmanagers: Lowell W.Busenitz and Jay B. Barney,“Differences Between

Entrepreneurs and Managersin Large Organizations:Biases and Heuristics inStrategic Decision-Making,”Journal of BusinessVenturing12(1997):9–30.admiration of others:Entrepreneurs who havefailed are sustained in theirconfidence by the probablymistakenbeliefthattheyhavelearnedagreatdeal from theexperience.GavinCassarandJustin Craig, “An

Investigation of HindsightBias in Nascent VentureActivity,” Journal ofBusinessVenturing24({>influence on the lives ofothers: Keith M. Hmieleskiand Robert A. Baron,“Entrepreneurs’ Optimismand New VenturePerformance: A SocialCognitive Perspective,”Academy of ManagementJournal 52 (2009): 473–88.Matthew L. A. Hayward,

DeanA. Shepherd, and DaleGriffin, “AHubrisTheoryofEntrepreneurship,”Management Science 52(2006):160–72.chance of failing was zero:Arnold C. Cooper, CarolynY. Woo, and William C.Dunkelberg, “Entrepreneurs’Perceived Chances forSuccess,”JournalofBusinessVenturing3(1988):97–108.given the lowest grade:Thomas Astebro and Samir

Elhedhli, “The Effectivenessof Simple DecisionHeuristics: ForecastingCommercial Success forEarly-Stage Ventures,”Management Science 52(2006):395–409.widespread, stubborn, andcostly:ThomasAstebro,“TheReturn to IndependentInvention: Evidence ofUnrealistic Optimism, RiskSeeking or SkewnessLoving?” Economic Journal

113(2003):226–39.bet small amounts of money:Eleanor F. Williams andThomas Gilovich, “DoPeople Really Believe TheyAre Above Average?”Journal of ExperimentalSocialPsychology44(2008):1121–28.“hubrishypothesis”: RichardRoll,“TheHubrisHypothesisof Corporate Takeovers,”Journal of Business 59(1986):197–216,part1.This

remarkable early articlepresented a behavioralanalysis of mergers andacquisitions that abandonedtheassumptionof rationality,long before such analysesbecamepopular.“value-destroying mergers”:Ulrike Malmendier andGeoffrey Tate, “Who MakesAcquisitions? CEOOverconfidence and theMarket’s Reaction,” Journalof Financial Economics 89

(2008):20–43.“engage in earningsmanagement”: UlrikeMalmendier and GeoffreyTate, “Superstar CEOs,”Quarterly Journal ofEconomics 24 (2009), 1593–1638.self-aggrandizement to acognitive bias: Paul D.Windschitl, Jason P. Rose,Michael T. Stalk-fleet, andAndrew R. Smith, “ArePeopleExcessiveorJudicious

in Their Egocentrism? AModeling Approach toUnderstanding Bias andAccuracy in People’sOptimism,” Journal ofPersonality and SocialPsychology 95 (2008): 252–73.averageoutcome isa loss:Aform of competition neglecthasalsobeenobservedinthetime of day at which sellerson eBay choose to end theirauctions. The easy question

is: At what time is the totalnumber of bidders thehighest?Answer:around7:00p.m. EST. The questionsellers should answer isharder: Considering howmany other sellers end theirauctionsduringpeakhours,atwhat time will there be themost bidders looking at myauction? The answer: aroundnoon, when the number ofbiddersislargerelativetothenumberofsellers.Thesellers

who remember thecompetition and avoid primetime get higher prices. UriSimonsohn, “eBay’sCrowded Evenings:Competition Neglect inMarket Entry Decisions,”Management Science 56(2010):1060–73.“diagnosisantemortem”: EtaS. Berner and Mark L.Graber,“OverconfidenceasaCause ofDiagnostic Error inMedicine,”AmericanJournal

ofMedicine 121 (2008): S2–S23.“disclosing uncertainty topatients”: Pat Croskerry andGeoff Norman,“Overconfidence in ClinicalDecisionMaking,”AmericanJournal of Medicine 121(2008):S24–S29.background of risk taking:Kahneman and Lovallo,“Timid Choices and BoldForecasts.”RoyalDutchShell:J.Edward

Russo and Paul J. H.Schoemaker, “ManagingOverconfidence,” SloanManagement Review 33(1992):7–17.

25:Bernoulli’sErrors

Mathematical Psychology:ClydeH.Coombs,RobynM.Dawes, and Amos Tversky,Mathematical Psychology:An Elementary Introduction

(Englewood Cliffs, NJ:Prentice-Hall,1970).fortherichandforthepoor:This rule appliesapproximately to manydimensions of sensation andperception. It is known asWeber’s law, after theGerman physiologist ErnstHeinrich Weber, whodiscovered it. Fechner drewonWeber’s lawtoderive thelogarithmic psychophysicalfunction.

$10 million from $100million: Bernoulli’s intuitionwas correct, and economistsstilluse the logof incomeorwealth inmanycontexts.Forexample,whenAngusDeatonplotted the average lifesatisfaction of residents ofmany countries against theGDP of these countries, heusedthelogarithmofGDPasa measure of income. Therelationship, it turns out, isextremelyclose:Residentsof

high-GDPcountriesaremuchmore satisfied with thequalityof their lives thanareresidents of poor countries,and a doubling of incomeyields approximately thesame increment ofsatisfaction in rich and poorcountriesalike.“St. Petersburg paradox”:Nicholas Bernoulli, a cousinof Daniel Bernoulli, asked aquestion that can beparaphrasedasfollows:“You

are invited to a game inwhich you toss a coinrepeatedly.You receive$2 ifit showsheads, and theprizedoubleswitheverysuccessivetoss that shows heads. Thegameendswhenthecoinfirstshowstails.Howmuchwouldyoupayforanopportunitytoplay that game?” People donotthinkthegambleisworthmore than a few dollars,althoughitsexpectedvalueisinfinite—because the prize

keeps growing, the expectedvalue is $1 for each toss, toinfinity. However, the utilityof the prizes grows muchmore slowly, which explainswhy the gamble is notattractive.“history of one’s wealth”:Other factors contributed tothe longevity of Bernoulli’stheory. One is that it isnatural to formulate choicesbetween gambles in terms ofgains, or mixed gains and

losses. Not many peoplethought about choices inwhich all options are bad,although we were by nomeansthefirsttoobserveriskseeking. Another fact thatfavors Bernoulli’s theory isthatthinkingintermsoffinalstates ofwealth and ignoringthe past is often a veryreasonable thing to do.Economistsweretraditionallyconcerned with rationalchoices, and Bernoulli’s

modelsuitedtheirgoal.

26:ProspectTheoryast="2%">subjective value of wealth:Stanley S. Stevens, “ToHonor Fechner and RepealHis Law,” Science 133(1961): 80–86. Stevens,Psychophysics.The threeprinciples:Writingthis sentence reminded methat the graph of the valuefunction has already been

used as an emblem. EveryNobel laureate receives anindividual certificate with apersonalized drawing, whichis presumably chosen by thecommittee. My illustrationwas a stylized rendition offigure10.“loss aversion ratio”: Theloss aversion ratio is oftenfoundtobeintherangeof1.5and2.5:NathanNovemskyand Daniel Kahneman, “TheBoundaries of Loss

Aversion,” Journal ofMarketing Research 42(2005):119–28.emotional reaction to losses:Peter Sokol-Hessner et al.,“Thinking Like a TraderSelectively ReducesIndividuals’ Loss Aversion,”PNAS106(2009):5035–40.Rabin’s theorem: For severalconsecutive years, I gave aguest lecture in theintroductory finance class ofmycolleagueBurtonMalkiel.

I discussed the implausibilityof Bernoulli’s theory eachyear. I noticed a distinctchange in my colleague’sattitude when I firstmentionedRabin’s proof.Hewasnowpreparedtotaketheconclusion much moreseriously than in the past.Mathematicalargumentshavea definitive quality that ismorecompellingthanappealsto common sense.Economists are particularly

sensitivetothisadvantage.rejects that gamble: Theintuition of the proof can beillustrated by an example.Suppose an individual’swealthisW,andsherejectsagamble with equalprobabilities to win $11 orlose $10. If the utilityfunction for wealth isconcave (bent down), thepreference implies that thevalueof$1hasdecreasedbyover 9% over an interval of

$21! This is anextraordinarily steep declineand the effect increasessteadily as the gamblesbecomemoreextreme.“Even a lousy lawyer”:Matthew Rabin, “RiskAversion and Expected-UtilityTheory:ACalibrationTheorem,” Econometrica 68(2000): 1281–92. MatthewRabinandRichardH.Thaler,“Anomalies: Risk Aversion,”Journal of Economic

Perspectives 15 (2001):219–32.economists andpsychologists: Severaltheorists have proposedversions of regret theoriesthatarebuilton the idea thatpeople are able to anticipatehow their future experienceswill be affected by theoptions that did notmaterialize and/or by thechoices they did not make:David E. Bell, “Regret in

Decision Making UnderUncertainty,” OperationsResearch 30 (1982): 961–81.Graham Loomes and RobertSugden, “Regret Theory: AnAlternative to RationalChoice Under Uncertainty,”EconomicJournal92(1982):805–25. Barbara A. Mellers,“Choice and the RelativePleasure of Consequences,”Psychological Bulletin 126(2000): 910–24. Barbara A.Mellers, Alan Schwartz, and

Ilana Ritov, “Emotion-BasedChoice,” Journal ofExperimental Psychology—General 128 (1999):332–45.Decision makers’ choicesbetween gambles depend onwhether they expect to knowthe outcome of the gamblethey did not choose. IlanaRitov,“ProbabilityofRegret:Anticipation of UncertaintyResolution in Choice,”Organiz {an>y did notationalBehavior andHuman

Decision Processes 66(1966):228–36.

27:TheEndowmentEffect

What is missing from thefigure: A theoretical analysisthat assumes loss aversionpredictsapronouncedkinkofthe indifference curve at thereference point: AmosTversky and DanielKahneman, “Loss Aversion

in Riskless Choice: AReference-DependentModel,”QuarterlyJournalofEconomics106(1991):1039–61. Jack Knetsch observedthese kinks in anexperimental study:“Preferences andNonreversibility ofIndifferenceCurves,”Journalof Economic Behavior &Organization17(1992):131–39.period of one year: Alan B.

Krueger and AndreasMueller,“JobSearchandJobFinding in a Period of MassUnemployment: Evidencefrom High-FrequencyLongitudinal Data,” workingpaper, Princeton UniversityIndustrial Relations Section,January2011.did not own the bottle:Technically, the theoryallows thebuyingprice tobeslightlylowerthanthesellingprice because of what

economists call an “incomeeffect”: The buyer and thesellerarenotequallywealthy,becausethesellerhasanextrabottle.However, theeffect inthis case is negligible since$50isaminutefractionoftheprofessor’s wealth. Thetheorywouldpredictthatthisincome effect would notchangehiswillingnesstopaybyevenapenny.would be puzzled by it: Theeconomist Alan Krueger

reported on a study heconductedon theoccasionoftakinghis father to theSuperBowl: “We asked fans whohad won the right to buy apair of tickets for $325 or$400 each in a lotterywhether they would havebeenwilling to pay $3,000 aticket if they had lost in thelottery and whether theywould have sold their ticketsif someone had offered them$3,000 apiece. Ninety-four

percent said they would nothave bought for $3,000, andninety-two percent said theywould not have sold at thatprice.” He concludes that“rationality was in shortsupply at the Super Bowl.”Alan B. Krueger, “Supplyand Demand: An EconomistGoes to the Super Bowl,”Milken Institute Review: AJournalofEconomicPolicy3(2001):22–29.giving up a bottle of nice

wine: Strictly speaking, lossaversion refers to theanticipatedpleasureandpain,which determine choices.These anticipations could bewrong in some cases.Deborah A. Kermer et al.,“Loss Aversion Is anAffective Forecasting Error,”Psychological Science 17(2006):649–53.market transactions:Novemsky and Kahneman,“The Boundaries of Loss

Aversion.”halfofthetokenswillchangehands: Imagine that all theparticipants are ordered in aline by the redemption valueassigned to them. Nowrandomly allocate tokens tohalf the individuals in theline.Halfofthepeopleinthefrontofthelinewillnothavea token, and half of thepeople at the end of the linewill own one. These people(halfofthetotal)areexpected

to move by trading placeswitheachother,sothatintheendeveryone in the firsthalfofthelinehasatoken,andnoonebehindthemdoes.Brain recordings: BrianKnutson et al., “NeuralAntecedents of theEndowment Effect,” Neuron58 (2008): 814–22. BrianKnutson an {an utson et adStephanie M. Greer,“Anticipatory Affect: NeuralCorrelates andConsequences

for Choice,” PhilosophicalTransactions of the RoyalSociety B 363 (2008): 3771–86.riskless and risky decisions:Areviewofthepriceofrisk,based on “international datafrom 16 different countriesduring over 100 years,”yieldedanestimateof2.3,“instriking agreement withestimatesobtainedintheverydifferent methodology oflaboratory experiments of

individual decision-making”:MosheLevy,“LossAversionand the Price of Risk,”Quantitative Finance 10(2010):1009–22.effect of price increases:Miles O. Bidwel, Bruce X.Wang, and J. Douglas Zona,“AnAnalysis ofAsymmetricDemand Response to PriceChanges: The Case of LocalTelephoneCalls,” Journal ofRegulatory Economics 8(1995): 285–98. Bruce G. S.

Hardie, Eric J. Johnson, andPeter S. Fader, “ModelingLossAversionandReferenceDependenceEffectsonBrandChoice,” Marketing Science12(1993):378–94.illustrate the power of theseconcepts: Colin Camerer,“Three Cheers—Psychological, Theoretical,Empirical—for LossAversion,” Journal ofMarketing Research 42(2005): 129–33. Colin F.

Camerer,“ProspectTheoryintheWild: Evidence from theField,” in Choices, Values,and Frames, ed. DanielKahneman and AmosTversky (New York: RussellSageFoundation,2000),288–300.condo apartments in Boston:David Genesove andChristopher Mayer, “LossAversion and SellerBehavior: Evidence from theHousing Market,” Quarterly

Journal of Economics 116(2001):1233–60.effect of trading experience:John A. List, “Does MarketExperience EliminateMarketAnomalies?” QuarterlyJournal of Economics 118(2003):47–71.Jack Knetsch also: Jack L.Knetsch, “The EndowmentEffect and Evidence ofNonreversible IndifferenceCurves,”AmericanEconomicReview79(1989):1277–84.

ongoing debate about theendowmenteffect:CharlesR.Plott and Kathryn Zeiler,“The Willingness to Pay–Willingness to Accept Gap,the ‘Endowment Effect,’Subject Misconceptions, andExperimental Procedures forEliciting Valuations,”American Economic Review95 (2005): 530–45. CharlesPlott, a leading experimentaleconomist, has been veryskeptical of the endowment

effect and has attempted toshow that it is not a“fundamental aspect ofhumanpreference”but ratheran outcome of inferiortechnique. Plott and Zeilerbelieve that participants whoshow the endowment effectare under somemisconception about whattheirtruevaluesare,andtheymodified the procedures ofthe original experiments toeliminatethemisconceptions.

They devised an elaboratetraining procedure in whichthe participants experiencedthe roles of both buyers andsellers, and were explicitlytaught to assess their truevalues. As expected, theendowment effectdisappeared. Plott and Zeilerview their method as animportant improvement oftechnique. Psychologistswould consider the methodseverely deficient, because it

communicates to theparticipants a message ofwhat the experimentersconsider appropriatebehavior, which happens tocoincide with theexperimenters’ theory. Plottand Zeiler’s favored versionofKne{ers):tsch’sexchangeexperiment is similarlybiased: It does not allow theowner of the good to havephysical possession of it,whichiscrucial to theeffect.

See Charles R. Plott andKathryn Zeiler, “ExchangeAsymmetries IncorrectlyInterpreted as Evidence ofEndowment Effect Theoryand Prospect Theory?”American Economic Review97 (2007): 1449–66. Theremay be an impasse here,where each side rejects themethods required by theother.Peoplewhoarepoor:Intheirstudies of decision making

under poverty, Eldar Shafir,Sendhil Mullainathan, andtheir colleagues haveobserved other instances inwhich poverty induceseconomic behavior that is insome respects more realisticandmorerationalthanthatofpeople who are better off.The poor are more likely torespondtorealoutcomesthantotheirdescription.MarianneBertrand, SendhilMullainathan, and Eldar

Shafir, “BehavioralEconomics andMarketing inAid of Decision MakingAmong thePoor,”JournalofPublic Policy & Marketing25(2006):8–23.in the United States and inthe UK: The conclusion thatmoney spent on purchases isnot experienced as a loss ismore likely to be true forpeople who are relativelywell-off. The key may bewhether you are awarewhen

you buy one good that youwill not be unable to affordanothergood.NovemskyandKahneman, “The Boundariesof Loss Aversion.” IanBateman et al., “TestingCompeting Models of LossAversion: An AdversarialCollaboration,” Journal ofPublicEconomics 89 (2005):1561–80.

28:BadEvents

heartbeataccelerated:PaulJ.Whalen et al., “HumanAmygdala Responsivity toMaskedFearfulEyeWhites,”Science 306 (2004): 2061.Individualswithfocal lesionsoftheamygdalashowedlittleor no loss aversion in theirrisky choices: Benedetto DeMartino, Colin F. Camerer,and Ralph Adolphs,“Amygdala DamageEliminates Monetary LossAversion,”PNAS107(2010):

3788–92.bypassing the visual cortex:Joseph LeDoux, TheEmotional Brain: TheMysterious Underpinnings ofEmotional Life (New York:Touchstone,1996).processed faster: Elaine Foxet al., “Facial Expressions ofEmotion: Are Angry FacesDetected More Efficiently?”Cognition & Emotion 14(2000):61–92.“popsout”:ChristineHansen

andRanaldHansen,“Findingthe Face in the Crowd: AnAnger Superiority Effect,”Journal of Personality andSocialPsychology54(1988):917–24.“acceptable/unacceptable”:Jos J.A.VanBerkum et al.,“Right or Wrong? TheBrain’s Fast Response toMorally ObjectionableStatements,” PsychologicalScience20(2009):1092–99.negativity dominance: Paul

Rozin and Edward B.Royzman, “Negativity Bias,Negativity Dominance, andContagion,” Personality andSocial Psychology Review 5(2001):296–320.resistant to disconfirmation:Roy F. Baumeister, EllenBratslavsky, CatrinFinkenauer, and Kathleen D.Vohs, “Bad IsStrongerThanGood,” Review of GeneralPsychology5(200{/spFac1):323.

biologically significantimprovement: MichelCabanac, “Pleasure: TheCommon Currency,” Journalof Theoretical Biology 155(1992):173–200.not equally powerful: ChipHeath, Richard P. Larrick,and George Wu, “Goals asReference Points,” CognitivePsychology 38 (1999): 79–109.rain-drenched customers:Colin Camerer, Linda

Babcock, GeorgeLoewenstein, and RichardThaler, “Labor Supply ofNew York City Cabdrivers:One Day at a Time,”Quarterly Journal ofEconomics 112 (1997): 407–41. The conclusions of thisresearch have beenquestioned: Henry S. Farber,“Is TomorrowAnother Day?The Labor Supply of NewYork Cab Drivers,” NBERWorkingPaper9706,2003.A

series of studies of bicyclemessengers in Zurichprovides strong evidence forthe effect of goals, in accordwith the original study ofcabdrivers: Ernst Fehr andLorenzGoette, “DoWorkersWork More if Wages AreHigh? Evidence from aRandomized FieldExperiment,” AmericanEconomic Review 97 (2007):298–317.communicate a reference

point: Daniel Kahneman,“Reference Points, Anchors,Norms,andMixedFeelings,”Organizational Behavior andHuman Decision Processes51(1992):296–312.“wins the contest”: JohnAlcock,AnimalBehavior:AnEvolutionary Approach(Sunderland, MA: SinauerAssociates, 2009), 278–84,cited by Eyal Zamir, “Lawand Psychology: TheCrucialRoleofReferencePointsand

Loss Aversion,” workingpaper, Hebrew University,2011.merchants, employers, andlandlords:DanielKahneman,JackL.Knetsch,andRichardH. Thaler, “Fairness as aConstraint onProfit Seeking:Entitlements in the Market,”The American EconomicReview76(1986):728–41.fairness concerns areeconomically significant:Ernst Fehr, Lorenz Goette,

and Christian Zehnder, “ABehavioral Account of theLabor Market: The Role ofFairness Concerns,” AnnualReview of Economics 1(2009): 355–84. Eric T.Anderson and Duncan I.Simester, “Price Stickinessand Customer Antagonism,”Quarterly Journal ofEconomics 125 (2010): 729–65.altruistic punishment isaccompanied: Dominique de

Quervain et al., “The NeuralBasis of AltruisticPunishment,” Science 305(2004):1254–58.actual losses and foregonegains:DavidCohenandJackL. Knetsch, “Judicial Choiceand Disparities BetweenMeasures of EconomicValue,” Osgoode Hall LawReview 30 (1992): 737–70.Russell Korobkin, “TheEndowmentEffect andLegalAnalysis,” Northwestern

University Law Review 97(2003):1227–93.asymmetrical effects onindividual well-being: Zamir,“LawandPsychology.”

29:TheFourfoldPattern

andotherdisasters:Includingexposure to a “Dutch book,”whichisasetofgamblesthatyour incorrect preferencescommit you to accept an {

to>puzzlethatAllaisconstructed:Readers who are familiarwiththeAllaisparadoxeswillrecognize that this version isnew. It is bothmuch simplerand actually a strongerviolation than the originalparadox.Theleft-handoptionis preferred in the firstproblem.Thesecondproblemisobtainedbyaddingamorevaluable prospect to the leftthantotheright,buttheright-

handoptionisnowpreferred.sorely disappointed: As thedistinguished economistKenneth Arrow recentlydescribed the event, theparticipants in the meetingpaidlittleattentiontowhathecalled “Allais’s littleexperiment.” Personalconversation, March 16,2011.estimatesforgains:Thetableshows decision weights forgains. Estimates for losses

wereverysimilar.estimatedfromchoices:MingHsu, Ian Krajbich, ChenZhao, andColin F. Camerer,“Neural Response to RewardAnticipation under Risk IsNonlinear in Probabilities,”Journal of Neuroscience 29(2009):2231–37.parentsofsmallchildren:W.Kip Viscusi, Wesley A.Magat, and Joel Huber, “AnInvestigation of theRationality of Consumer

ValuationsofMultipleHealthRisks,” RAND Journal ofEconomics 18 (1987): 465–79.psychology of worry: In arational model withdiminishing marginal utility,people should pay at leasttwo-thirdsasmuch to reducethe frequency of accidentsfrom15to5unitsastheyarewillingtopaytoeliminatetherisk. Observed preferencesviolatedthisprediction.

not made much of it: C.Arthur Williams, “AttitudesToward Speculative Risks asan Indicator of AttitudesTowardPureRisks,”Journalof Risk and Insurance 33(1966): 577–86. HowardRaiffa, Decision Analysis:Introductory Lectures onChoices under Uncertainty(Reading, MA: Addison-Wesley,1968).shadow of civil trials: ChrisGuthrie, “Prospect Theory,

Risk Preference, and theLaw,” NorthwesternUniversity Law Review 97(2003): 1115–63. Jeffrey J.Rachlinski, “Gains, Lossesand the Psychology ofLitigation,” SouthernCalifornia Law Review 70(1996): 113–85. Samuel R.Gross and Kent D. Syverud,“Getting to No: A Study ofSettlement Negotiations andthe Selection of Cases forTrial,”MichiganLawReview

90(1991):319–93.the frivolous claim: ChrisGuthrie, “Framing FrivolousLitigation: A PsychologicalTheory,” University ofChicago Law Review 67(2000):163–216.

30:RareEventswish to avoid it: George F.Loewenstein,ElkeU.Weber,ChristopherK.Hsee,andNedWelch, “Risk as Feelings,”

Psychological Bulletin 127(2001):267–86.vividness indecisionmaking:Ibid. Cass R. Sunstein,“Probability Neglect:Emotions, Worst Cases, andLaw,”YaleLawJournal 112(2002): 61–107. See notes tochapter 13: Damasio,Descartes’ Error. Slovic,Finucane, Peters, andMacGregor, “The {r, n>: C.AAffectHeuristic.”Amos’sstudent:CraigR.Fox,

“Strength of Evidence,Judged Probability, andChoice Under Uncertainty,”Cognitive Psychology 38(1999):167–89.focal event and its:Judgmentsoftheprobabilitiesof an event and itscomplement do not alwaysadd up to 100%. Whenpeopleareaskedaboutatopicthey know very little about(“What is your probabilitythat the temperature in

Bangkok will exceed 100°tomorrow at noon?”), thejudged probabilities of theeventanditscomplementadduptolessthan100%.receiving a dozen roses: Incumulative prospect theory,decision weights for gainsandlossesarenotassumedtobeequal, as theywere in theoriginal version of prospecttheorythatIdescribe.superficial processing: Thequestion about the two urns

was invented by Dale T.Miller,WilliamTurnbull,andCathy McFarland, “When aCoincidence Is Suspicious:The Role of MentalSimulation,” Journal ofPersonality and SocialPsychology 57 (1989): 581–89. Seymour Epstein and hiscolleagues argued for aninterpretationofitintermsoftwo systems: Lee A.Kirkpatrick and SeymourEpstein, “Cognitive-

Experiential Self-Theory andSubjective Probability:EvidenceforTwoConceptualSystems,” Journal ofPersonality and SocialPsychology 63 (1992): 534–44.judgeditasmoredangerous:Kimihiko Yamagishi, “Whena 12.86% Mortality Is MoreDangerous Than 24.14%:Implications for RiskCommunication,” AppliedCognitive Psychology 11

(1997):495–506.forensic psychologists:Slovic, Monahan, andMacGregor, “Violence RiskAssessment and RiskCommunication.”“1 of 1,000 capital cases”:Jonathan J. Koehler, “WhenAre People Persuaded byDNAMatchStatistics?”Lawand Human Behavior 25(2001):493–513.studies of choice fromexperience: Ralph Hertwig,

GregBarron,ElkeU.Weber,and Ido Erev, “Decisionsfrom Experience and theEffect of Rare Events inRiskyChoice,”PsychologicalScience 15 (2004): 534–39.RalphHertwig and IdoErev,“The Description-ExperienceGapinRiskyChoice,”Trendsin Cognitive Sciences 13(2009):517–23.notyetsettled:LiatHadarandCraig R. Fox, “InformationAsymmetry inDecision from

Description Versus Decisionfrom Experience,” Judgmentand Decision Making 4(2009):317–25.“chances of rare events”:Hertwig and Erev, “TheDescription-ExperienceGap.”

31:RiskPoliciesinferior option BC: Thecalculationisstraightforward.Eachofthetwocombinations

consistsofasure thingandagamble.Addthesurethingtoboth options of the gambleandyouwillfindADandBC.the equivalent of “lockingin”: Thomas Langer andMartin Weber, “MyopicProspect Theory vs. MyopicLossAversion:HowGeneralIsthePhenomenon?”Journalof E {>Joenon?&conomicBehavior & Organization 56(2005):25–38.

32:KeepingScoredrive into a blizzard: Theintuition was confirmed in afield experiment in which arandom selection of studentswhopurchasedseason ticketsto the university theaterreceived their tickets at amuch reduced price. Afollow-up of attendancerevealed that students whohad paid the full price fortheir ticketsweremorelikely

to attend, especially duringthe first half of the season.Missinga showonehaspaidfor involves the unpleasantexperience of closing anaccountinthered.ArkesandBlumer, “The Psychology ofSunkCosts.”the disposition effect: HershShefrin and Meir Statman,“The Disposition to SellWinners Too Early andRideLosersTooLong:TheoryandEvidence,” Journal of

Finance 40 (1985): 777–90.Terrance Odean, “AreInvestorsReluctanttoRealizeTheir Losses?” Journal ofFinance53(1998):1775–98.less susceptible: Ravi DharandNingZhu,“UpCloseandPersonal: InvestorSophistication and theDisposition Effect,”Management Science 52(2006):726–40.fallacy can be overcome:Darrin R. Lehman, Richard

O. Lempert, and Richard E.Nisbett, “The Effects ofGraduate Training onReasoning:FormalDisciplineand Thinking aboutEveryday-Life Events,”American Psychologist 43(1988):431–42.“a sinking feeling”: MarcelZeelenberg and Rik Pieters,“A Theory of RegretRegulation 1.0,” Journal ofConsumer Psychology 17(2007):3–18.

regret to normality:KahnemanandMiller,“NormTheory.”habitually takingunreasonable risks: Thehitchhiker question wasinspiredbyafamousexamplediscussed by the legalphilosophers Hart andHonoré: “A woman marriedtoamanwhosuffersfromanulcerated condition of thestomachmightidentifyeatingparsnips as the cause of his

indigestion.Thedoctormightidentify the ulceratedconditionasthecauseandthemeal as a mere occasion.”Unusualeventscallforcausalexplanations and also evokecounterfactual thoughts, andthe two are closely related.The same event can becomparedtoeitherapersonalnorm or the norm of otherpeople, leading to differentcounterfactuals, differentcausal attributions, and

different emotions (regret orblame): Herbert L. A. HartandTonyHonoré,Causationin the Law (New York:Oxford University Press,1985),33.remarkably uniform: DanielKahneman and AmosTversky, “The SimulationHeuristic,” in JudgmentUnder Uncertainty:Heuristics and Biases, ed.Daniel Kahneman, PaulSlovic, and Amos Tversky

(New York: CambridgeUniversityPress,1982),160–73.applies to blame: JanetLandman, “Regret andElationFollowingActionandInaction:AffectiveResponsesto Positive Versus NegativeOutcomes,” Personality andSocialPsychologyBulletin13(1987): 524–36. FaithGleicher et al., “TheRole ofCounterfactual Thinking inJudgment of Affect,”

Personality and SocialPsychology Bulletin 16(1990):284–95.actions that deviate from thedefault: Dale T. Miller andBrian R. Taylor,“Counterfactual Thought,Regret, and Superstition:How to Avoid KickingYourself,” in What MightHave Been: The SocialPsychologyofCounterfactualThinking, ed. Neal J. Roeseand James M. Olson

(Hillsdale, NJ: Erlbaum,1995),305–31.produce blame and regret:MarcelZeelenberg,Keesvanden Bos, Eric van Dijk, andRik Pieters, “The InactionEffect in the Psychology ofRegret,” Journal ofPersonality and SocialPsychology 82 (2002): 314–27.brand names over generics:Itamar Simonson, “TheInfluence of Anticipating

Regret andResponsibility onPurchaseDecisions,”Journalof Consumer Research 19(1992):105–18.clean up their portfolios:LilianNgandQinghaiWang,“InstitutionalTradingandtheTurn-of-the-Year Effect,”Journal of FinancialEconomics 74 (2004): 343–66.loss averse for aspects ofyour life: Tversky andKahneman, “Loss Aversion

in Riskless Choice.” Eric J.Johnson,SimonGächter, andAndreas Herrmann,“Exploring the Nature ofLoss Aversion,” Centre forDecision Research andExperimental Economics,University of Nottingham,Discussion Paper Series,2006. Edward J. McCaffery,Daniel Kahneman, andMatthewL.Spitzer,“Framingthe Jury: CognitivePerspectives on Pain and

Suffering,” Virginia LawReview81(1995):1341–420.classic on consumerbehavior: Richard H. Thaler,“TowardaPositiveTheoryofConsumer Choice,” Journalof Economic Behavior andOrganization 39 (1980): 36–90.taboo tradeoff: Philip E.Tetlock et al., “ThePsychology of theUnthinkable: Taboo Trade-Offs, Forbidden Base Rates,

and HereticalCounterfactuals,” Journal ofPersonality and SocialPsychology 78 (2000): 853–70.where the precautionaryprinciple: Cass R. Sunstein,The Laws of Fear: Beyondthe Precautionary Principle(New York: CambridgeUniversityPress,2005).“psychological immunesystem”:DanielT.Gilbert etal., “Looking Forward to

Looking Backward: TheMisprediction of Regret,”Psychological Science 15(2004):346–50.

33:Reversalsin the man’s regular store:Dale T. Miller and CathyMcFarland, “CounterfactualThinking and VictimCompensation: A Test ofNorm Theory,” Personalityand Social Psychology

Bulletin12(1986):513–19.reversals of judgment andchoice: The first step towardthecurrent interpretationwastaken by Max H. Bazerman,George F. Loewenstein, andSallyB.White,“ReversalsofPreference in AllocationDecisions: JudgingAlternatives Versus JudgingAmong Alternatives,”Administrative ScienceQuarterly37(1992):220–40.Christopher Hsee introduced

the terminology of joint andseparate evaluation, andformulated the importantevaluability hypothesis,which explains reversals bythe idea that some attributes{e a#822become evaluableonly in joint evaluation:“Attribute Evaluability: ItsImplications for Joint-Separate EvaluationReversals and Beyond,” inKahneman and Tversky,Choices,Values,andFrames.

conversation betweenpsychologists andeconomists: SarahLichtensteinandPaulSlovic,“Reversals of PreferenceBetweenBidsandChoicesinGambling Decisions,”Journal of ExperimentalPsychology89(1971):46–55.Asimilarresultwasobtainedindependently by Harold R.Lindman, “InconsistentPreferences AmongGambles,” Journal of

Experimental Psychology 89(1971):390–97.bewilderedparticipant: For atranscript of the famousinterview, see SarahLichtensteinandPaulSlovic,eds., The Construction ofPreference (New York:Cambridge University Press,2006).the prestigious AmericanEconomicReview:DavidM.GretherandCharlesR.Plott,“EconomicTheoryofChoice

and the Preference ReversalsPhenomenon,” AmericanEconomic Review 69 (1979):623–28.“contextinwhichthechoicesare made”: Lichtenstein andSlovic, The Construction ofPreference,96.one embarrassing finding:Kuhn famously argued thatthe same is true of physicalsciences as well: Thomas S.Kuhn, “The Function ofMeasurement in Modern

Physical Science,” Isis 52(1961):161–93.liking of dolphins: There isevidence thatquestionsaboutthe emotional appeal ofspeciesandthewillingnesstocontribute to their protectionyield the same rankings:Daniel Kahneman and IlanaRitov, “Determinants ofStatedWillingnesstoPayforPublicGoods:AStudyintheHeadlineMethod,”JournalofRisk and Uncertainty 9

(1994):5–38.superior on this attribute:Hsee, “AttributeEvaluability.”“requisite record-keeping”:Cass R. Sunstein, DanielKahneman, David Schkade,and IlanaRitov, “PredictablyIncoherent Judgments,”Stanford Law Review 54(2002):1190.

34:Framesand

Realityunjustified influences offormulation: Amos Tverskyand Daniel Kahneman, “TheFramingofDecisionsandthePsychology of Choice,”Science211(1981):453–58.paid with cash or on credit:Thaler, “Toward a PositiveTheory of ConsumerChoice.”10%mortality is frightening:Barbara McNeil, Stephen G.

Pauker, Harold C. Sox Jr.,and Amos Tversky, “On theElicitation of Preferences forAlternative Therapies,” NewEnglandJournalofMedicine306(1982):1259–62.“Asian disease problem”:Some people havecommented that the “Asian”label is unnecessary andpejorative. We probablywould not use it today, butthe example was written inthe1970s,whensensitivityto

group labels was lessdeveloped than it is today.Thewordwasaddedtomaketheexamplemoreconcretebyreminding respondents of theAsianfluepidem{anslessicof1957.Choice and Consequence:Thomas Schelling, Choiceand Consequence(Cambridge, MA: HarvardUniversityPress,1985).misleading frame:RichardP.Larrick and Jack B. Soll,

“TheMPGIllusion,”Science320(2008):1593–94.rate of organ donation inEuropean countries: Eric J.Johnson and DanielGoldstein,“DoDefaultsSaveLives?” Science 302 (2003):1338–39.

35:TwoSelves“wantability”: Irving Fisher,“Is‘Utility’theMostSuitableTerm for the Concept It Is

Used to Denote?” AmericanEconomic Review 8 (1918):335.at any moment: FrancisEdgeworth, MathematicalPsychics (NewYork:Kelley,1881).underwhichhistheoryholds:Daniel Kahneman, Peter P.Wakker, and Rakesh Sarin,“Back to Bentham?Explorations of ExperiencedUtility,”QuarterlyJournalofEconomics 112 (1997): 375–

405. Daniel Kahneman,“Experienced Utility andObjective Happiness: AMoment-Based Approach”and“EvaluationbyMoments:Past and Future,” inKahneman and Tversky,Choices,Values,andFrames,673–92,693–708.a physician and researcher:Donald A. Redelmeier andDaniel Kahneman, “Patients’MemoriesofPainfulMedicalTreatments: Real-time and

Retrospective Evaluations ofTwo Minimally InvasiveProcedures,”Pain 66 (1996):3–8.free to choose: DanielKahneman, Barbara L.Frederickson, Charles A.Schreiber, and Donald A.Redelmeier, “When MorePain Is Preferred to Less:Adding a Better End,”Psychological Science 4(1993):401–405.duration of the shock: Orval

H. Mowrer and L. N.Solomon, “Contiguity vs.Drive-Reduction inConditioned Fear: TheProximity and Abruptness ofDrive Reduction,” AmericanJournal of Psychology 67(1954):15–25.burst of stimulation: PeterShizgal, “On the NeuralComputation of Utility:Implications from Studies ofBrain Stimulation Reward,”in Well-Being: The

Foundations of HedonicPsychology, ed. DanielKahneman, Edward Diener,and Norbert Schwarz (NewYork: Russell SageFoundation,1999),500–24.

36:LifeasaStoryhad a lover: Paul Rozin andJenniferStellar,“PosthumousEvents Affect Rated Qualityand Happiness of Lives,”Judgment and Decision

Making4(2009):273–79.entire lives as well as briefepisodes: EdDiener,DerrickWirtz, and Shigehiro Oishi,“End Effects of Rated LifeQuality: The James DeanEffect,” PsychologicalScience 12 (2001): 124–28.The same series ofexperiments also tested forthe peak-end rule in anunhappy life and foundsimilar results: Jen was notjudged twice as unhappy if

she lived miserably for 60years rather than 30, but {thk-e she was regarded asconsiderably happier if 5mildly miserable years wereaddedjustbeforeherdeath.

37:ExperiencedWell-Being

life as a whole these days:Another question that hasbeen used frequently is,“Taken all together, how

would you say things arethese days? Would you saythat you are very happy,pretty happy, or not toohappy?” This question isincluded in the GeneralSocial Survey in the UnitedStates, and its correlationswithothervariablessuggestamix of satisfaction andexperienced happiness. Apure measure of lifeevaluationusedintheGallupsurveys is the Cantril Self-

Anchoring Striving Scale, inwhich the respondent rateshis or her current life on aladderscaleinwhich0is“theworst possible life for you”and 10 is “the best possiblelife for you.” The languagesuggests that people shouldanchoronwhattheyconsiderpossible for them, but theevidence shows that peopleall over the world have acommon standard for what agood life is, which accounts

for the extraordinarily highcorrelation(r = .84) betweentheGDPofcountriesandtheaverage ladder score of theircitizens. Angus Deaton,“Income, Health, and Well-Being Around the World:Evidence from the GallupWorld Poll,” Journal ofEconomic Perspectives 22(2008):53–72.“a dream team”: TheeconomistwasAlanKruegerof Princeton, noted for his

innovative analyses ofunusual data. Thepsychologists were DavidSchkade, who hadmethodological expertise;Arthur Stone, an expert onhealth psychology,experience sampling, andecological momentaryassessment;NorbertSchwarz,a social psychologist whowasalsoanexpertonsurveymethod and had contributedexperimental critiques of

well-being research,including the experiment onwhich a dime left on acopying machine influencedsubsequent reports of lifesatisfaction.intensity of various feelings:In some applications, theindividual also providesphysiological information,suchascontinuousrecordingsof heart rate, occasionalrecords of blood pressure, orsamples of saliva for

chemical analysis. Themethod is called EcologicalMomentary Assessment:Arthur A. Stone, Saul S.Shiffman, and Marten W.DeVries, “EcologicalMomentary AssessmentWell-Being:TheFoundationsof Hedonic Psychology,” inKahneman, Diener, andSchwarz,Well-Being,26–39.spend their time: DanielKahneman et al., “A SurveyMethod for Characterizing

Daily Life Experience: TheDayReconstructionMethod,”Science306(2004):1776–80.Daniel Kahneman and AlanB. Krueger, “Developmentsin the Measurement ofSubjective Well-Being,”Journal of EconomicPerspectives20(2006):3–24.physiological indications ofemotion: Previous researchhad documented that peopleare able to “relive” feelingsthey had in a past situation

whenthesituationisretrievedin sufficiently vivid detail.Michael D. Robinson andGerald L. Clore, “Belief andFeeling: Evidence for anAccessibility Model ofEmotional Self-Report,”Psychological Bulletin 128(2002):934–60.state the U-index: Alan B.Krueger, ed., Measuring theSubjective Well-Being ofNations: National Accountsof Time Use and Well-Being

(Chicago: University ofChicagoPress,2009).distributio {i>dll-Being: EdDiener, “Most People AreHappy,” PsychologicalScience7(1996):181–85.Gallup World Poll: For anumber of years I have beenone of several SeniorScientists associatedwith theefforts of the GallupOrganizationinthedomainofwell-being.morethan450,000responses:

DanielKahnemanandAngusDeaton, “High IncomeImproves Evaluation of Lifebut Not Emotional Well-Being,” Proceedings of theNational Academy ofSciences 107 (2010): 16489–93.worse for the very poor:DylanM.Smith,KennethM.Langa, Mohammed U.Kabeto, and Peter Ubel,“Health, Wealth, andHappiness: Financial

Resources Buffer SubjectiveWell-Being After the Onsetof a Disability,”Psychological Science 16(2005):663–66.$75,000inhigh-costareas:Ina TED talk I presented inFebruary2010 Imentionedapreliminary estimate of$60,000, which was latercorrected.eatabarofchocolate!: JordiQuoidbach, Elizabeth W.Dunn, K. V. Petrides, and

Moïra Mikolajczak, “MoneyGiveth,MoneyTakethAway:TheDualEffectofWealthonHappiness,” PsychologicalScience21(2010):759–63.

38:ThinkingAboutLife

German Socio-EconomicPanel: Andrew E. Clark, EdDiener, and YannisGeorgellis, “Lags and LeadsinLifeSatisfaction:ATestof

the Baseline Hypothesis.”Paper presented at theGerman Socio-EconomicPanel Conference, Berlin,Germany,2001.affective forecasting: DanielT. Gilbert and Timothy D.Wilson, “Why the BrainTalks to Itself: Sources ofError in EmotionalPrediction,” PhilosophicalTransactions of the RoyalSociety B 364 (2009): 1335–41.

only significant fact in theirlife: Strack, Martin, andSchwarz, “Priming andCommunication.”questionnaire on lifesatisfaction: The originalstudy was reported byNorbert Schwarz in hisdoctoral thesis (in German)“Mood as Information: Onthe Impact of Moods on theEvaluation of One’s Life”(Heidelberg:SpringerVerlag,1987). It has been described

in many places, notablyNorbert Schwarz and FritzStrack, “Reports ofSubjective Well-Being:Judgmental Processes andTheir MethodologicalImplications,” in Kahneman,Diener, and Schwarz, Well-Being,61–84.goals that young people set:The study was described inWilliamG.BowenandDerekCurtisBok,TheShapeof theRiver: Long-Term

ConsequencesofConsideringRace in College andUniversity Admissions(Princeton: PrincetonUniversity Press, 1998).Some of Bowen and Bok’sfindings were reported byCarol Nickerson, NorbertSchwarz, and Ed Diener,“Financial Aspirations,Financial Success, andOverall Life Satisfaction:Who? andHow?” Journal ofHappiness Studies 8 (2007):

467–515.“being very well-offfinancially”: AlexanderAstin,M.R.King,andG.T.Richardson, “The AmericanFreshman: National Normsfor Fall 1976,” CooperativeInstitutional ResearchProgram of the American C{heon,RouncilonEducationand the University ofCalifornia at Los Angeles,Graduate School ofEducation, Laboratory for

Research in HigherEducation,1976.money was not important:These results were presentedin a talk at the AmericanEconomicAssociationannualmeeting in 2004. DanielKahneman,“PuzzlesofWell-Being,”paperpresentedatthemeeting.happiness of Californians:The question of how wellpeople todaycanforecast thefeelings of their descendants

a hundred years fromnow isclearly relevant to the policyresponse to climate change,but it can be studied onlyindirectly, which is what weproposedtodo.aspects of their lives: Inposing the question, I wasguilty of a confusion that Inow try to avoid: Happinessand life satisfaction are notsynonymous.Lifesatisfactionrefers to your thoughts andfeelings when you think

about your life, whichhappens occasionally—including in surveys ofwell-being. Happiness describesthe feelings people have astheylivetheirnormallife.I had won the familyargument:However,mywifehas never conceded. Sheclaims that only residents ofNorthern California arehappier.students in California and inthe Midwest: Asian students

generally reported lowersatisfaction with their lives,andAsianstudentsmadeupamuchlargerproportionofthesamples inCalifornia than inthe Midwest. Allowing forthis difference, lifesatisfactioninthetworegionswasidentical.How much pleasure do youget from your car?: Jing Xuand Norbert Schwarz havefound that the quality of thecar (as measured by Blue

Book value) predicts theowners’ answer to a generalquestion about theirenjoymentofthecar,andalsopredicts people’s pleasureduring joyrides. But thequality of the car has noeffect on people’s moodduring normal commutes.Norbert Schwarz, DanielKahneman, and Jing Xu,“GlobalandEpisodicReportsof Hedonic Experience,” inR. Belli, D. Alwin, and F.

Stafford (eds.), UsingCalendarandDiaryMethodsin Life Events Research(Newbury Park, CA: Sage),pp.157–74.paraplegics spend in a badmood?: The study isdescribed in more detail inKahneman, “Evaluation byMoments.”think about their situation:Camille Wortman andRoxane C. Silver, “Copingwith Irrevocable Loss,

Cataclysms, Crises, andCatastrophes: Psychology inAction,” AmericanPsychological Association,Master Lecture Series 6(1987):189–235.studiesofcolostomypatients:Dylan Smith et al.,“MisrememberingColostomies?FormerPatientsGive Lower Utility Ratingsthan Do Current Patients,”HealthPsychology25(2006):688–95.George Loewenstein

and PeterA.Ubel, “HedonicAdaptation and the Role ofDecision and ExperienceUtility in Public Policy,”Journal of Public Economics92(2008):1795–1810.thewordmiswanting:DanielGilbert and Timothy D.Wilson, “Miswanting: SomeProblems in AffectiveForecasting,” in Feeling andThinking: The Role of Affectin Social Cognition, ed.JosephP.Forgas(NewYork:

Cambridge University Press,2000),178–97.

Conclusionstoo important to be ignored:Paul Dolan and DanielKahneman, “Interpretationsof Utility and TheirImplicationsfortheValuationofHealth,”EconomicJournal118 (2008): 215–234.Loewenstein and Ubel,“HedonicAdaptation and the

Role of Decision andExperience Utility in PublicPolicy.”guide government policies:Progress has been especiallyrapid in the UK, where theuseofmeasuresofwell-beingis now official governmentpolicy. These advances weredue in good part to theinfluence of Lord RichardLayard’s book Happiness:Lessons fromaNewScience,first published in 2005.

Layard is among theprominent economists andsocial scientists who havebeen drawn into the study ofwell-being and itsimplications.Other importantsources are: Derek Bok, ThePolitics of Happiness: WhatGovernment Can Learn fromthe New Research on Well-Being (Princeton: PrincetonUniversity Press, 2010). EdDiener, Richard Lucus,Ulrich Schmimmack, and

JohnF.Helliwell,Well-BeingforPublicPolicy(NewYork:Oxford University Press,2009). Alan B. Krueger, ed.,Measuring the SubjectiveWell-Being of Nations:National Account of TimeUse and Well-Being(Chicago: University ofChicagoPress,2009). JosephE.Stiglitz,AmartyaSen,andJean-Paul Fitoussi,Report ofthe Commission on theMeasurement of Economic

Performance and SocialProgress. Paul Dolan,Richard Layard, and RobertMetcalfe, MeasuringSubjective Well-being forPublic Policy:Recommendations onMeasures(London:OfficeforNationalStatistics,2011).Irrational is a strong word:The view of the mind thatDan Ariely has presented inPredictably Irrational: TheHidden Forces That Shape

Our Decisions (New York:Harper, 2008) is not muchdifferent from mine, but wedifferinouruseoftheterm.accept futureaddiction:GaryS. Becker and Kevin M.Murphy, “A Theory ofRational Addiction,” Journalof Political Economics 96(1988): 675–700. Nudge:Richard H. Thaler and CassR. Sunstein, Nudge:Improving Decisions AboutHealth, Wealth, and

Happiness(NewHaven:YaleUniversityPress,2008).can institute and enforce:AtulGawande,TheChecklistManifesto: How to GetThings Right (New York:Holt, 2009). DanielKahneman,DanLovallo,andOliverSibony,“TheBigIdea:Before You Make That BigDecision…” HarvardBusiness Review 89 (2011):50–60.distinctive vocabulary: Chip

Heath, Richard P. Larrick,and Joshua Klayman,“Cognitive Repairs: HowOrganizational Practices CanCompensate for IndividualShortcomings,” Research inOrganizational Behavior 20(1998):1–37.

P

Index

The index that appeared inthe print version of this titledoes not match the pages inyour eBook. Please use thesearch function on youreReadingdevicetosearchforterms of interest. For yourreference, the terms thatappear in the print index are

listedbelow.

adaptationlevel

Add-1taskadjustment;insufficientaffect heuristic;availabilityandaffectiveforecastingairplanecrashesAjzen,IcekAlarscarealgorithms;Apgar scores;

hostility to; multipleregressionAllais,Mauriceal-Qaedaambiguity,suppressionofAmerican EconomicReviewamygdalaanchoringindexanchors, anchoring; asadjustment; associativecoherencein;associativememory and;measurement of; as

priming effect; random,power of; in System 1and System 2; uses andabusesofanesthesiologistsangryfacesanomaliesanteriorcingulateApgar,VirginiaApgarscoresaphorismsAriely,DanArrow,Kennethartexperts

artifacts,inresearchAsch,SolomonAshenfelter,OrleyAsiandiseaseproblemassessments,basicassociations; activatedideas in; causality and;primingandassociative coherence; inanchoring; halo effectand; plausibility and,associative coherence(cont.);WYSIATI(whatyou see is all there is)

andassociative memory;abnormal events and;anchoring and; causalityand; confirmation biasand; creativity and; andestimates of causes ofdeathÅstebro,ThomasAtlantic,Theattention;inself-controlpaneight="0%"

width="-5%">Attention and Effort

(Kahneman)Auerbach,Redauthoritarianideasavailability; affect and;and awareness of one’sbiases; expectationsabout; media and;psychology of; riskassessment and, see riskassessmentavailabilitycascadesavailabilityentrepreneurs

badandgood,distinctionsbetweenbanksbanktellerproblemBarber,BradBargh,Johnbaseballbaseballcardsbaselinepredictionsbase rates; in cab driverproblem; causal; inhelpingexperiment;low;statistical; in Tom W

problem; in Yale examproblembasicassessmentsbasketballbasketballticketsbat-and-ballproblemBaumeister,RoyBayes,ThomasBayesianstatisticsBazerman,MaxBeane,BillyBeatty,JacksonBecker,Gary“Becoming Famous

Overnight”(Jacoby)behavioraleconomicsBehavioralInsightTeam“Belief in the Law ofSmall Numbers”(Tversky andKahneman)beliefs: bias for; past,reconstructionofBenartzi,ShlomoBentham,JeremyBerlin,IsaiahBernoulli,DanielBernouilli,Nicholas

Beyth,Ruthbicyclemessengers

Black Swan, The(Taleb)blameBlink(Gladwell)Borg,BjörnBorgida,Eugene“Boys Will BeBoys” (Barber andOdean)Bradlee,Benbrain; amygdala in;anterior cingulate

in; buying andselling and;emotional framingand;frontalareaof;pleasure and;prefrontal area of;punishment and;sugar in; threatsand; and variationsofprobabilitiesBritish ToxicologySocietybroadframingBrockman,John

broken-legrulebudgetforecastsBuilttoLast(CollinsandPorras)Bush,GeorgeW.business andleadershippractices;atGooglebusinesspundits

Cabanac,Michel

cabdriverproblemcabdrivers, New

YorkCityCaliforniansCamerer,Colincancer; surgery vs.radiationforCantril Self-Anchoring StrivingScaleCarroll,Lewiscars and driving;brakes in; drivingtests; fuel economyand;pleasurefromcashbox

categoriescausalbaseratescausalinterpretations;correlation and;regression effectsandcausalsituationscausalstereotypescauses,andstatisticsCEOs;optimisticcertaintyeffectCFOsChabris,Christopher

chance andrandomness;misconceptionsofchanging one’smindChecklistManifesto, A(Gawande)chesschildren:caringfor; depressed;time spentwith

ChinaChoice andConsequence(Schelling)choicearchitecturechoices: fromdescription;fromexperience;see alsodecisions,decisionmaking; risk

assessment“Choices,Values, andFrames”(KahnemanandTversky)CIAClark,AndrewclimateClinical vs.StatisticalPrediction: ATheoreticalAnalysisanda

Review of theEvidence(Meehl)Clinton,BillCoelho,Martacoffee mugexperimentscognitivebusynesscognitive ease;in basicassessments;and illusionsof

remembering;and illusionsof truth;moodand; andwritingpersuasivemessages;WYSIATI(what you seeis all there is)andcognitiveillusions;confusing

experienceswithmemories; ofpundits; ofremembering;of skill; ofstock-pickingskill; of truth;ofunderstanding;ofvalidityCognitiveReflectionTest(CRT)

cognitivestrainCohen,Davidcoherence; seealsoassociativecoherenceCohn,Beruriacoincidencecoin-on-the-machineexperimentcold-handexperimentCollins,Jim

colonoscopiescolostomypatientscompetence,judgingofcompetitionneglectcomplex vs.simplelanguageconcentration

cogndivheight="0%">

“Conditions for Intuitive

Expertise: A Failure toDisagree” (KahnemanandKlein)confidence; bias of, overdoubt; overconfidence;WYSIATI(whatyouseeisallthereis)andconfirmationbiasconjunctionfallacyconjunctive events,evaluationof“ConsequencesofEruditeVernacular UtilizedIrrespective of

Necessity: ProblemswithUsingLongWordsNeedlessly”(Oppenheimer)contiguity in time andplacecontrolcookieexperimentcorrelation;causationand;illusory; regression and;sharedfactorsandcorrelationcoefficientcost-benefitcorrelationcosts

creativity; associativememoryandcredibilityCsikszentmihalyi,Mihalycurriculumteam

Damasio,Antonio

datingquestionDawes,RobynDay ReconstructionMethod(DRM)death: causes of; lifestories and; organ

donation and; remindersofDeaton,Angusdecisions, decisionmaking; broad framingin; and choice fromdescription; and choicefrom experience;emotions and vividnessin; expectation principlein; in gambles, seegambles; globalimpressions and;hindsight bias and;

narrow framing in;optimistic bias in;planning fallacy and;poverty and; premortemand; reference points in;regret and; risk and, seeriskassessmentdecisionutilitydecision weights;overweighting; unlikelyevents and; in utilitytheory vs. prospecttheory; vivid outcomesand; vivid probabilities

anddecorrelatederrorsdefaultoptionsdenominatorneglectdepressionDetroit/MichiganproblemDiener,Ed

dierollproblemdinnerwareproblemdisclosuresdiseasethreatsdisgustdisjunctive events,evaluationof

dispositioneffectDNAevidencedolphinsDosi,Giovannidoubt; bias ofconfidence over;premortem and;suppressionofDukeUniversityDuluth, Minn.,bridgeindurationneglectdurationweighting

earthquakes

eatingeBayEconometricaeconomics;behavioral;Chicagoschool of;neuroeconomics;preferencereversalsand; rational-agentmodelineconomic

transactions,fairnessinEconsandHumansEdgeEdgeworth,Franciseducationeffectiveness ofsearchsetseffort; least, law of;inself-controlegodepletionelectricityelectricshocksemotional

coherence, see haloeffect emotionallearningemotions andmood:activities and;affect heuristic;availability biasesand; in basicassessments;cognitive ease and;indecisionmaking;in framing; moodheuristic forhappiness;

negative,measuring; andoutcomes producedby action vs.inaction;paraplegics and;perception of;substitution ofquestion on; invivid outcomes; invivid probabilities;weather and; workandemployers, fairness

rulesandendangeredspeciesendowment effect;and thinking like atraderenergy,mentalengagementEnquiry ConcerningHumanUnderstanding, An(Hume)entrepreneurs;competition neglectby

Epley,NickEpstein,Seymourequal-weightingschemesErev,Idoevaluabilityhypothesisevaluations: joint;joint vs. single;singleevidence: one-sided;ofwitnessesexecutivecontrolexpectationprinciple

expectationsexpected utilitytheory, see utilitytheoryexperiencedutilityexperiencesamplingexperiencing self;well-being of; seealsowell-beingexpert intuition;evaluating;illusionsof validity of;overconfidenceand;as recognition;

risk assessmentand; vs. statisticalpredictions;trustinexpertise,seeskillExpert PoliticalJudgment: HowGood Is It? HowCan We Know?(Tetlock)Exxon Valdez oilspilleyes, pupil dilationin

facereading

fairnessfallacies;conjunction;narrative; planning;sunk-costfamiliarityFar Side, The(Larson)fast and frugalheuristicfastthinking

fatiguefearFechner,GustavfeedbackFeller,Williamfinancial crisis of2008financial advisersandforecastersfirefightersfirstimpressionsFischhoff,Baruchflightinstructors

floodmonitorFloridaeffectflowflowerssyllogismFlyvbjerg,Bentfocusfocusingillusionfontsforecasts, seepredictions andforecastsfootballgameFord MotorCompany

formulas;algorithms; Apgarscores; hostility to;for interviews;multipleregressionformulationeffectsFortunefourfold pattern; inlegalcasesFox,CraigFox,Seymourframes, framing; inAsian diseaseproblem; in child

exemptionproblem;in disclosures;emotional; fueleconomy and;good; in KEEP-LOSE study; organdonation and;regulations on; insurvival-mortalityexperiment; inticketproblemFrederick,ShaneFreedman,Davidfreedom

Free to Choose(Friedman)frequencyrepresentationFrey,BrunoFriedman,Miltonfrowning;availabilityheuristic and;representativenessand

gains

Galinsky,AdamGallup-HealthwaysWell-BeingIndexGalton,Francisgambles; bundlingof; certainty effectand; emotionalframing in; lossaversion in; lottery;mixed; andoutcomes producedby action vs.inaction; possibilityeffect and;

psychologicalvalueof; regret and;simple; St.Petersburg paradoxand;vs.surethings;utility ongsv> seealso riskassessmentGatesFoundationGawande,AtulGeorgellis,YannisGerman Socio-EconomicPanelgestures

Gibbs,LoisGigerenzer,GerdGilbert,DanielGilovich,TomGladwell,Malcolmglobalwarmingglucosegoalsgolfgood and bad,distinctionsbetweenGooglegorillaexperiment

gossipGottman,JohnGould,StephenJaygrades and gradepoint averages(GPAs)grading students’essaysGrether,Davidgroup,joiningGuthrie,Chris

Haidt,Jonathan

haloeffectHalo Effect, The(Rosenzweig)happiness; ofCalifornians;datingquestion and;income and; lifestories and;marriageand;moodheuristic for; seealso well-beinghappyfaceshappywordsHarding,WarrenG.

Harvard MedicalSchoolHarvardUniversityhealth: diseasethreats and; well-being and; risksand; see alsomedicinehealth surveyproblemhealth violationpenaltiesHebrew UniversityofJerusalem

“Hedgehog and the Fox,The”(Berlin)hedonimeterHeider,FritzhelpingexperimentHertwig,RalphHess,Eckhardheuristic,definitionofhigh school curriculumteamhindsight: bias in; regretandhistoricaleventshitchhikerquestion

Hitler,AdolfHogarth,Robinhonestybox“How Mental SystemsBelieve”(Gilbert)HowtoSolveIt(Pólya)Hsee,ChristopherhubrishypothesisHumansandEconsHume,Davidhungerhypotheses,testing

ideomotoreffectillusions: cognitive, seecognitive illusions;Müller-Lyer;3-Dimaginability, immediategratificationincongruityindependentjudgmentsindifferencemapinheritancesinjectionpuzzleIn Search of Excellence(PetersandWaterman)

insideviewinsuranceintelligence; in marriage;pretentiouslanguageandintensitymatchingintentioninterviews; in IsraeliDefenseForcesIntroduction to thePrinciplesofMoralsandLegislation(Bentham)intuition:acquisiitiodution of;commonuseofword;of

experts, see expertintuition; predictive, seepredictions andforecasts;asrecognition;Simon’sdefinitionofInventor’s AssistancePrograminvestments: stockportfolios; sunk-costfallacyandInvisible Gorilla, The(ChabrisandSimons)irrationalityIsrael,bombingsin

Israeli Defense Forces:flight instructors in;interviews in; leaderlessgroupchallengeinIsraeli Ministry ofEducation

“Jabberwocky”(Carroll)

Jacoby,LarryJencks,Christopherjoint evaluations; singleevaluationsvs.judgmentheuristics

Judgment in ManagerialDecision Making(Bazerman)judgments; basicassessments in; ofexperts, see expertintuition; intensitymatching in; mentalshotgun in; predictive,see predictions andforecasts; sets andprototypes in; summary,of complex information;see also decisions,

decisionmaking“Judgment UnderUncertainty: Heuristicsand Biases” (TverskyandKahneman)Julieproblemjumping to conclusions;bias for belief andconfirmation in; haloeffectin,seehaloeffect;suppression ofambiguity and doubt in;WYSIATI in, see whatyouseeisallthereis

Kaye,Danny

keeping score; mentalaccountsand;regretand;responsibilityandKEEP-LOSEstudykidneycancerKillingGround,ThekitchenrenovationsKlein,GaryKnetsch,Jackknow,useofwordknowledge;

reconstruction of paststatesofkourosKrueger,Alan

Kunreuther,HowardKuran,Timur

labornegotiations

LadyMacbetheffectlanguage, complex vs.simpleLarrick,RichardLarson,Gary

law,seelegalcaseslawoflargenumberslawofsmallnumbers;andbias of confidence overdoubtlazinessofSystem2Layard,Richardleaderlessgroupchallengeleadership and businesspractices;atGoogleLeBoeuf,Robynlegalcases:civil,damagesin; DNA evidence in;fourfold pattern and;

frivolous; loss aversionin;malpractice;outcomebiasinleisuretimeless-is-morepatternLewis,MichaellibertarianpoliciesLichtenstein,Sarahlife: evaluationof; storiesin; satisfaction in;thinkingaboutLindaproblemList,Johnloans

logarithmicfunctionsloss aversion; in animals;enhanced; goals asreference points in; inlegal decisions; statusquoandlossaversionratiolosseslotteriesLovallo,DanLoveCanallucklying

Malkiel,Burton

Malmendier,UlrikemalpracticelitigationMaoZedongmarchofhistoryuote>

Markowitz,Harrymarriage; lifesatisfactionandMathematicalPsychology(Dawes, Tversky,andCoombs)

matter, relation ofmindtoMcFarland,Cathymedia, availabilityheuristicandmedical schooladmissionsmedical surveyproblemmedicine; expertisein; malpracticelitigation;overconfidence in;physicians; unique

cases in; unusualtreatmentsinMednick,SarnoffMeehl,Paulmeetingsmemory, memories;associative, seeassociativememory;availabilityheuristic and, seeavailability;duration neglect in;experienced utility

and; illusions of;and theremembering self;ofvacationsmentalaccountsmental effort, seeeffort mentalenergymentalshotgunmereexposureeffectmessages,persuasivemetaphorsMichigan/Detroitproblem

Michigan StateUniversityMichotte,AlbertMiller,Dalemind, relation ofmattertoMischel,WaltermiswantingMITmoney and wealth:cultural differencesin attitudes toward;happiness and;income vs. leisure;

mental accountsand; poverty;priming and; utilityofMoneyball(Lewis)mood, see emotionsand moodMorgenstern,OskarMosesillusionmotivationmovies“MPGIllusion,The”(LarrickandSoll)mugexperiments

Mullainathan,SendhilMüller-LyerillusionmultipleregressionMussweiler,Thomasmutualfunds

names: complicated;

offamouspeoplenarrativefallacynarrow framing;dispositioneffectNaturalisticDecision

Making(NDM)negativitydominancenegotiationsneuroeconomicsNew York Times,TheNew YorkUniversity9/11Nisbett,RichardNixon,RichardNobelPrizenorms

normtheorynoveltyNudge (Thaler andSunstein)nutrition

OaklandA’s

Obama,BarackobesityOdean,TerryOffice ofInformation andRegulatoryAffairs

one-sidedevidenceOppenheimer,Dannyoptimalexperienceoptimism; in CEOs;resilienceandoptimistic bias;competitionneglect; inentrepreneurs;overconfidence;planning fallacy;premortem and;risktakingand

Oregon ResearchInstituteorgandonationorganizationsoutcomebiasoutsideview

ou>

pain; chronic; cold-hand experimentand; colonoscopiesand; durationneglect and;

injection puzzleand; memory of;operationexperiment and;peak-end rule and;inrats

paraplegicsparolepast: and confusingexperiences withmemories;hindsightbiasand;regretandpastnesspatternseeking

Pavlov,Ivanpeak-endrulepersuasivemessagesphysicians; malpracticelitigationandpianoplayingandweight,measuringplanecrashesplanning fallacy;mitigatingplausibilitypleasure;inratsPlott,Charlespoignancy

politicalexpertspoliticalpreferencePólya,GeorgePope,DevinPorras,JerryI.positiveteststrategypossibilityeffect:gamblesand;threatsandpost-traumaticstresspovertyprecautionaryprinciplepredictability,insensitivitytopredictions and forecasts;

baseline; clinical vs.statistical; disciplining;of experts, see expertintuition; extreme, valueof; formulas for, seeformulas; increasingaccuracyin;low-validityenvironments and;nonregressive;objections tomoderating; optimisticbias in; outside view in;overconfidence in;planning fallacy and;

short-term trends and;valid, illusion of; seealsoprobabilitypreference reversals;unjustpremonition,useofwordpremortempretentiousnesslanguagepricingpoliciespriming;anchoringas

t="-5%">PrincetonUniversityprobability; base rates in,see base rates; decision

weights and, seedecision weights;definitions of; anddisciplining intuition;less-is-morepatternand;Linda problem and;overestimation of;plausibility and; andpredicting byrepresentativeness;prior,insensitivity to;professional stereotypesand; of rare events, seerare events;

representativeness and,see representativeness;similarity and;subjective; as sum-likevariable; see alsopredictionsandforecastsprobabilityneglectProceedings of theNational Academy ofSciencesprofessionalstereotypesprofessorialcandidatesprospect theory; inAlbertand Ben problem; blind

spots of; cumulative;decision weights andprobabilities in; fourfoldpattern in; frames and;graphoflossesandgainsin; loss aversion in;referencepointsin“Prospect Theory: AnAnalysis of DecisionUnderRisk”(KahnemanandTversky)prototypespsychiatricpatientspsychological immune

systempsychology,teachingpsychopathiccharmpsychophysicspsychotherapistspundits; see also expertintuition punishments:altruistic; rewards and;self-administeredpupildilation

questionnaire and gift

experiments

questions; substitutionof,seesubstitution

Rabin,Matthew

radiologistsrafters,skilledrailprojectsrandomness and chance;misconceptions ofRandom Walk DownWallStreet,A(Malkiel)rare events;overestimationof; regret

andrational-agentmodelrationalityRationality and theReflective Mind(Stanovich)">ratsReagan,Ronaldreciprocalprimingrecognitionrecognition-primeddecision(RPD)modelRedelmeier,Donreferenceclassforecasting

regression to the mean;causal interpretationsand; correlation and;difficulty in grasping;two-systemsviewof“Regression towardsMediocrityinHereditaryStature”(Galton)regretreligionrememberingselfRemote Association Test(RAT)reorganizations in

companiesrepetitionrepresentativeness; baserates and; see also baserates; in Linda problem;predicting by;professional stereotypesand; sins of; in TomWproblemresearch: artifacts in;hypothesis testing in;optimisminresemblance; inpredictions

resilienceresponsibilityretrievabilityofinstancesreversals;unjustrewards; self-administeredRice,Condoleezzarisk assessment;aggregation and; broadframing in; decisionweights in, see decisionweights; denominatorneglect and; by experts;and format of risk

expression; fourfoldpattern in; for healthrisks;hindsightbiasand;laws and regulationsgoverning; loss aversionin; narrow framing in;optimistic bias and;policies for; possibilityeffectand;precautionaryprinciple and;probability neglect and;public policies and;small risks and; oftechnologies; terrorism

and;seealsogamblesriskaversionriskseeking“Robust Beauty ofImproperLinearModelsin Decision Making,The”(Dawes)Rosett,RichardRosenzweig,PhilipRoyalDutchShellRoyalInstitutionRozin,Paul<PhilipRumsfeld,Donald

RussellSageFoundationRussia

SaddamHussein

sadnesssafety; health risks and;healthviolationpenaltiesand; precautionaryprincipleandsamples, sampling:accidentsof;andbiasofconfidence over doubt;law of large numbers;

law of small numbers;size of; small,exaggeratedfaithinSamuelson,PaulSan FranciscoExploratoriumSavage,JimmieSaveMoreTomorrowSchelling,ThomasSchkade,DavidschoolsizeSchwarz,NorbertSchweitzer,MauriceScience

ScientificAmericanscientificcontroversiesscientific research:artifacts in; hypothesistestingin;optimisminScottishParliamentself-controlself-criticismSeligman,Martinselves; experiencing;rememberingsetsShafir,Eldarsimilarityjudgments

Simmel,Mary-AnnSimon,HerbertSimons,DanielSimpson,O.J.single evaluations; jointevaluationsvs.skijumpeventskills; acquisition of;environment of;feedbackandpracticein;illusions of; in stock-pickingSlovic,PaulSlovic,Roz

slowthinking<="0>

smiles,infacereadingsmiling; availabilityheuristicandSmith,VernonsocializingsocialscienceSoll,Jacksomatic markerhypothesissoulSourcesofPower(Klein)SovietUnion

Spinoza,BaruchSportsIllustratedStalin,JosephStandard&Poor’s(S&P)StanfordUniversityStanovich,Keithstatistics and statisticalthinking; and accidentsof sampling; base ratesand, see base rates;Bayesian; and bias ofconfidence over doubt;causes and; chance in;deciding on size of

sample; extremeoutcomes and; faith insmall samples; law oflarge numbers; law ofsmall numbers; samplesize decisions and; seealsoprobabilitystatusquo,defendingSteiger,JamesH.stereotypes; causal; aboutprofessionsStevethelibrarianstockmarketstockpicking

stockportfoliosstocktrading,insiderStone,Arthurstories,lifeSt.PetersburgparadoxStrack,Fritzstrangers,assessmentofStrangers to Ourselves(Wilson)Streep,Merylstrength,assessmentsofstructuredsettlementsStumbling to Happiness(Gilbert)

substitution; and moodheuristic for happiness;and3-Dheuristicsuccess,uotsum-likevariablessunk-costfallacySunstein,CassSuperBowlsupplyanddemandsurgeonsSurowiecki,Jamessurprisesurvey and giftexperiments

survival-mortalityexperimentsymbolsSystem 1; characteristicsof; conflict betweenSystem2andSystem 2; conflictbetween System 1 and;lazinessof

Taleb,Nassim

talenttasksets

taskswitchingTate,Geoffreytaxes; child exemptionsandtemperamenttemptationTenet,GeorgeterrorismTetlock,PhilipThaler,Richardtheory-inducedblindnesstherapiststhinkinglikeatraderThomas,Lewis

threats; possibility effectand3-Dheuristictickets;buyingandsellingof;sunkcostintime;useoftimepressureTodorov,AlextokenexperimentTomWproblem“Trading IsHazardous toYour Wealth” (BarberandOdean)transactionsandtrades

Traviata,La(Verdi)Truman,Harrytrustworthiness,assessmentsoftruth,illusionsofTversky,Amos

understanding,illusionof

uniquecasesUniversity CollegeLondonUniversityofCaliforniaatBerkeley

UniversityofChicagoUniversityofMichiganUniversityofMinnesotaUniversityofOregonunlikely events, see rareevents unknownunknownsutility; decision;experienced;indifference map and;injection puzzle and;meaningsofutility theory; certaintyeffect and; decision

weightsandprobabilitiesin

vacations

vaccinesvalidity: of clinical vs.statistical predictions;evaluating;illusionofVallone,Robertvalue; see also utilityVancouverIslandVenndiagramsventurecapitalists

victimcompensationvividness;ofoutcomes;ofprobabilitiesvocabulary: of girls vs.boys; simple vs.pretentiousVohs,Kathleenvomit,effectofwordVonNeumann,Johnvoting

Wainer,Howard

walking

warsWashingtonPost,Thewealth, see money andwealthweatherWeber,Ernste>weightandpianoplaying,measuringWeiner,Howardwell-being; climate and;defining;dispositionfor;duration weighting and;seealsohappinessWest,Richard

whatyouseeisallthereis(WYSIATI); confidenceand; curriculum teamand; Julie problem and;optimistic bias and;premortem and;professorial candidateproblem and; soldiers’performance and; TomWproblemandwheeloffortune“wicked”environmentsWilson,TimothyWimbledontournament

wineWinterOlympicsWisdom of Crowds, The(Surowiecki)witnesses’evidenceWoods,Tigerwords: complex vs.simple; emotionally-loadedWorldCupWorldWarIIworryWYSIATI, see what youseeisallthereis

X-rays

Xu,Jing

YaleexamproblemYomKippurWar

Zajonc,Robert

Zamir,EyalZeller,KathrynZweig,JasonZwerling,Harris

P

Farrar,StrausandGiroux18West18thStreet,New

York10011Copyright©2011byDaniel

KahnemanAllrightsreserved

Grateful acknowledgment ismade for permission toreprint the followingpreviously publishedmaterial: “Judgment UnderUncertainty: Heuristics and

Biases” from Science, NewSeries, Vol. 185, No. 4157,copyright © 1974 by AmosTversky and Dan"0%" te>X-rays Science. “Choices,Values, and Frames” fromThe American Psychologist,copyright © 1983 by DanielKahneman and AmosTversky. Reprinted bypermission of the AmericanPsychologicalAssociation.Grateful acknowledgment is

made for permission toreprint the following images:Image courtesy of PaulEkman Group, LLC. Imagefrom “Cues of BeingWatched EnhanceCooperation in a Real-WorldSetting” byMelissa Bateson,Daniel Nettle, and GilbertRoberts, Biology Letters(2006); reprinted bypermission of BiologyLetters. Image from MindSights by Roger N. Shepard

(New York: W.H. Freemanand Company, 1990);reprinted by permission ofHenry Holt and Company.Image from “HumanAmygdala Responsivity toMasked Fearful EyeWhites”by Paul J. Whalen et al.,Science 306 (2004).Reprinted by permission ofScience.

LibraryofCongressCataloging-in-Publication

DataKahneman,Daniel,1934–Thinking, fast and slow /DanielKahneman.—1sted.

p.cm.Includes bibliographicalreferencesandindex.ISBN:978-0-3742-7563-11. Thought and thinking. 2.Decisionmaking.3.Intuition.4.Reasoning.I.Title.BF441.K2382011153.4'2—dc23

2011027143

www.fsgbooks.com

P

*5,47.

P

*Feature introduced in detailinpart4.

P

*Feature introduced in detailinpart4.

P

*Feature introduced in detailinpart4.

P

*Feature introduced in detailinpart4.

P

*Feature introduced in detailinpart4.

P

*This article originallyappearedinScience,vol.185,1974. The research wassupported by the AdvancedResearch Projects Agency ofthe Department of Defenseand was monitored by theOffice of Naval ResearchundercontractN00014-73-C-0438 to theOregonResearchInstitute, Eugene. Additionalsupportforthisresearchwassr"0%" wid provided by the

Research and DevelopmentAuthority of the HebrewUniversity,Jerusalem,Israel.

P

*This article was originallypresented as a DistinguishedScientific ContributionsAward address at theAmerican PsychologicalAssociation meeting, August1983. This work wassupported by grant NR 197-058 from the U.S. Office ofNaval Research. Originallypublished in AmericanPsychologist,vol.34,1984.

P

P