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Welcome to the Military Families Learning Network Webinar

Heuristics, Anchoring & Financial Management!

This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, and the Office of Family Policy, Children and Youth, U.S. Department of Defense under Award Numbers 2010-48869-20685 and 2012-48755-20306.


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Dr. Michael Gutter"Dr. Michael Gutter is an Assistant Professor and Financial Management State Specialist for the Department of Family, Youth, and Community Sciences, in the Institute for Food and Agricultural at the University of Florida. Dr. Gutter is also the Principle Investigator for the Military Families Learning Network’s Personal Finance Community of Practice. Dr. Gutter is the current Vice President of the Florida Jumpstart Coalition and serves on the editorial boards for the Journal of Consumer Affairs, Journal of Consumer Education, and the Journal of Financial Counseling and Planning. Dr. Gutter’s outreach projects include Managing in Tough Times, Florida Saves, Get Checking, and the Master Money Mentor. His projects focus on enabling access to resources and services as well as improving people’s knowledge and understanding about family resource management. These projects have had funding from the Consumer Federation of America and Bank of America."

Heuris(cs, Anchoring, Narrowing Choice

Dr. Michael S Gu:er Interim Family and Consumer Science Program Leader

MLFN Personal Finance Team @mikegu:er

msgu:[email protected]

Thinking About How Our Mind Works

•  GPA Example –  h:p://youtu.be/KyM3d4gQGhM

Mammalian

Einstein-‐ian

Interes(ng Idea •  So how do we view ourselves? •  Our status? •  What we have?

•  Depends on what others have around us…

•  h:p://youtu.be/_ERQEVdIinc

Are we predictably irra(onal? •  It is not surprising that we are not always

perfectly ra(onal •  But are our departures from perfect

ra(onality completely random? •  Or are these departures predictable? •  If we can find predictable pa:erns of

irra(onality in human behavior, then we can improve economic theory

Mo(va(ons and Objec(ves •  The two main mo(va(ons for behavioral economics

concern apparent weaknesses in standard economic theory: –  People some(mes make choices that are difficult to explain with

standard economic theory –  Standard economic theory can lead to seemingly unreasonable

conclusions about consumer welfare •  Behavioral economics grew out of research in psychology •  The objec(ve is to modify, supplement, and enrich

economic theory by adding insights from psychology –  Sugges(ng that people care about things standard theory typically

ignores, like fairness or status –  Allowing for the possibility of mistakes

13-13

Methods •  Behavioral economics uses many of the same

tools and frameworks as standard economics –  Assumes individuals have well-‐defined objec(ves,

that objec(ves and ac(ons are connected, and ac(ons affect well-‐being

–  Relies on mathema(cal models –  Subjects theories to careful empirical tes(ng

•  Important difference is use of experiments using human subjects

•  Behavioral economists tend to use experimental data to test their theories rather than drawing data from the real world

13-14

A Representa(veness Example •  Consider the following descrip(on: “Steve is very shy and withdrawn, invariably

helpful, but with li6le interest in people, or in the world of reality. A meek and <dy soul, he has a need for order and structure, and a passion for detail.”

•  Is Steve a farmer, a librarian, a physician, an airline pilot, or a salesman?

Rules of Thumb/Heuris(cs •  Thinking through every alterna(ve for

complex economic decisions is difficult •  May rely on simple rules of thumb that

have served well in the past •  Popular rules may be choices that are

nearly op(mal, using one is not necessarily a mistake

•  Allow judgment and decision making in cases where specific and accurate solu(ons are either unknown or unknowable

13-16

Rules of Thumb/Heuris(cs •  Example: saving

–  In economic models finding the best rate of savings involves complex calcula(ons

–  In prac(ce people seem to follow rules of thumb such as 10% of income

–  These rules appear to ignore factors that theory says should be important, such as expected future income

•  Availability, anchoring and adjustment, and representa(veness are frequently considered “metaheuris(cs” since they engender many specific effects

Three Major Human Probability-‐Assessment Heuris(cs/Biases

(Tversky and Kahneman, 1974) •  Representa=veness

–  The more object X is similar to class Y, the more likely we think X belongs to Y

•  Availability –  The easier it is to consider instances of class Y, the more frequent we think it is

•  Anchoring –  Ini(al es(mated values affect the final es(mates, even aler considerable adjustments

The Representa(veness Heuris(c

•  We olen judge whether object X belongs to class Y by how representa=ve X is of class Y

•  For example, people order the poten(al occupa(ons by probability and by similarity in exactly the same way

•  The problem is that similarity ignores mul(ple biases

Representa(ve Bias (1): Insensi(vity to Prior

Probabili(es •  The base rate of outcomes should be a major factor in

es(ma(ng their frequency •  However, people olen ignore it (e.g., there are more

farmers than librarians) –  E.g., the lawyers vs. engineers experiment:

•  Reversing the propor(ons (0.7, 0.3) in the group had no effect on es(ma(ng a person’s profession, given a descrip(on

•  Giving worthless evidence caused the subjects to ignore the odds and es(mate the probability as 0.5

–  Thus, prior probabili(es of diseases are olen ignored when the pa(ent seems to fit a rare-‐disease descrip(on

Representa(ve Bias (2): Insensi(vity to Sample Size

•  The size of a sample withdrawn from a popula(on should greatly affect the likelihood of obtaining certain results in it

•  People, however, ignore sample size and only use the superficial similarity measures

•  For example, people ignore the fact that larger samples are less likely to deviate from the mean than smaller samples

Representa(ve Bias (3): Misconcep(on of Chance

•  People expect random sequences to be “representa(vely random” even locally –  E.g., they consider a coin-‐toss run of HTHTTH to be more likely than HHHTTT or HHHHTH

•  The Gambler’s Fallacy –  Aler a run of reds in a roule:e, black will make the overall run more representa(ve (chance as a self-‐correc(ng process??)

•  Even experienced research psychologists believe in a law of small numbers (small samples are representa(ve of the popula(on they are drawn from)

Representa(ve Bias (4): Insensi(vity to Predictability •  People predict future performance mainly by similarity of

descrip(on to future results •  For example, predic(ng future performance as a teacher

based on a single prac(ce lesson –  Evalua<on percen(les (of the quality of the lesson) were iden(cal to predicted percen(les of 5-‐year future standings as teachers

The Availability Heuris(c •  The frequency of a class or event is olen

assessed by the ease with which instances of it can be brought to mind

•  The problem is that this mental availability might be affected by factors other than the frequency of the class

Availability Biases (1): Ease of Retrievability

•  Classes whose instances are more easily retrievable will seem larger –  For example, judging if a list of names had more men or women depends on the rela(ve frequency of famous names

•  Salience affects “retrievability” –  E.g., watching a car accident increases subjec(ve assessment of traffic accidents

The Anchoring and Adjustment Heuris(c

•  People olen es(mate by adjus(ng an ini(al value un(l a final value is reached

•  Ini(al values might be due to the problem presenta(on or due to par(al computa(ons

•  Adjustments are typically insufficient and are biased towards ini(al values, the anchor

Anchoring and Adjustment Biases (1): Insufficient Adjustment

•  Anchoring may occur due to incomplete calcula(on, such as es(ma(ng by two high-‐school student groups –  the expression 8x7x6x5x4x3x2x1 (median answer: 512) –  with the expression 1x2x3x4x5x6x7x8 (median answer: 2250)

•  Anchoring occurs even with outrageously extreme anchors (Qua:rone et al., 1984)

•  Anchoring occurs even when experts (real-‐estate agents) es(mate real-‐estate prices (Northcral and Neale, 1987)

Anchoring and Adjustment Biases (2): Evalua(on of Conjunc(ve and Disjunc(ve Events

•  People tend to overes(mate the probability of conjunc(ve events (e.g., success of a plan that requires success of mul(ple steps)

•  People underes(mate the probability of disjunc(ve events (e.g. the Birthday Paradox)

•  In both cases there is insufficient adjustment from the probability of an individual event

Probability that at least two people in N share a birthday

Hint think of the # of possible pairing not people

Anchoring •  h:p://youtu.be/HetkqKCVpo

Anchoring •  55 subjects were shown a series of six common products with

average retail price of $70 •  For each product, the experiment had three steps: Each par(cipant

was asked –  his/her SSN –  whether he/she would buy the product at a price equal to the last 2 digits of SSN

–  The maximum he/she would be willing to pay

Incoherent Choices: Anchoring

•  Anchoring occurs when someone’s choices are linked to prominent but irrelevant informa(on

•  Suggests that some choices are arbitrary and can’t reflect meaningful preferences

•  Example: experiment showing subjects’ willingness to pay for various goods was closely related to the last two digits of their social security number, by sugges(on –  Skep(cs note that subjects had li:le experience purchasing the goods in

the experiment –  Might have been less sensi(ve to sugges(on if used familiar products

•  Significance of anchoring effects for many economic choices remains unclear

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Changing the Anchor: Gevng in Line Behind Yourself •  Why does someone pay so much for

Starbuck’s Coffee?

•  h:p://youtu.be/FaO3aGmuNFc

•  Can we lower the anchor?

Have merchants like Starbucks influenced our thinking?

Thinking About Coffee •  Have marketers shiled how we think about

coffee and our price point •  To what extent can we filter external

influences?

Anchoring

Source: Dan Ariely, Predictably Irra<onal: Chapter 2 Supply and Demand video at h:p://www.youtube.com/watch?v=FaO3aGmuNFc&feature=youtu.be

The process of seeding a thought in a person’s mind and having that thought influence their later ac(ons.

Anchoring •  Is the height of the tallest redwood tree

more or less than 1,200 feet? •  What is your best guess about the height of

the tallest redwood?

Source: Daniel Kahneman, “Thinking, Fast and Slow”

Results of Redwood Experiment

•  Is the height of the tallest redwood tree more or less than 1,200 feet? – Mean answer: 844 feet

•  Is the height of the tallest redwood tree more or less than 180 feet? – Mean answer: 282 feet

•  Anchoring Index = ra(o between differences

•  Anchoring index = 0 for people able to ignore anchor

Results of Redwood Experiment

•  height more or less than 1,200 feet? – Mean answer: 844 feet

•  height more or less than 180 feet? – Mean answer: 282 feet

•  Anchoring index = 844-‐282 / 1200 – 180 = 55%

•  Anchoring index = 0% for people able to ignore anchor and 100% controlled by it

Anchoring •  Is the average price of a German car in the

US more or less than $100,000? •  What type of cars does this bring to mind?

Source: Daniel Kahneman http://youtu.be/HefjkqKCVpo

Real-‐ Estate Experiment •  Real-‐estate agents asked to assess the

value of a house actually on the market •  Visited house •  Given booklets about house that include ap

price –  ½ of agents saw booklets w/price higher than actual listed price

–  ½ saw price that was lower than listed price

Source: Daniel Kahneman, “Thinking, Fast and Slow”

Real-‐estate Experiment •  Viewed house & booklet •  Gave opinion about what they thought was

a reasonable buying price and selling price •  Also asked what factors influenced their

opinion –  Said lis(ng price did not influence

Real-‐Estate Experiment Results

•  Anchoring index for real-‐estate professionals was 41%

•  Anchoring index for business school students was found to be 48%

Nego(a(on and Anchoring •  Sellers point of view – anchor your thinking to a higher price •  Price presented •  Focus a:en(on and search memory for arguments against

the anchor


•  Anchoring occurs when someone’s choices are linked to prominent but irrelevant informa(on

•  Suggests that some choices are arbitrary and can’t reflect meaningful preferences

Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint


•  Example: Experiment showing subjects’ willingness to pay for various goods was closely related to the last two digits of their social security number, by sugges(on –  Skep(cs note that subjects had li:le experience

purchasing the goods in the experiment

–  Might have been less sensi(ve to sugges(on if used familiar products Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint

Anchoring •  Significance of anchoring effects for many

economic choices remains unclear •  What do you think?

Endowment Effect •  Half the par(cipants were given mugs available at the campus bookstore

for $6 •  The other half were allowed to examine the mugs •  Each student who had a mug was asked to name the lowest sale price •  Each student who did not have a mug was asked to name the highest

purchase price •  Supply and demand curves were constructed and the equilibrium price

was obtained •  Trade followed •  There were four rounds of this

Bias Toward the Status Quo: Endowment Effect

•  The endowment effect is people’s tendency to value something more highly when they own it than when they don’t

•  Example: experiment in which median owner value for mugs was roughly twice the median non-‐owner valua(on

•  Some economists think this reflects something fundamental about the nature of preferences

•  Incorpora(ng the endowment effect into standard theory implies an indifference curve kinked at the consumer’s ini(al consump(on bundle –  Smooth changes in price yield abrupt changes in

consump(on 13-50

A Special Type of Bias: Framing •  Risky prospects can be framed in different ways-‐ as

gains or as losses •  Changing the descrip(on of a prospect should not

change decisions, but it does, in a way predicted by Tversky and Kahneman’s (1979) Prospect Theory

•  In Prospect Theory, the nega(ve effect of a loss is larger than the posi(ve effect of a gain

•  Framing a prospect as a loss rather than a gain, by changing the reference point, changes the decision by changing the evalua(on of the same prospect

•  May resolve a number of puzzles related to risky decisions

A Value Func(on in Prospect Theory

Gains Losses

-‐ +

Default effect: re(rement •  Prior to April 1, 1998, the default op(on was nonpar(cipa(on in

the re(rement plan •  Aler April 1, 1998, all employees were by default enrolled in a plan

that invested 3% of salary in money market mutual funds •  Only the default op(on changed

Bias Toward the Status Quo: Default Effect

•  When confronted with many alterna(ves, people some(mes avoid making a choice and end up with the op(on that is assigned as a default

•  Example: Experiment showing that more subjects kept $1.50 par(cipa(on fee rather than trading it for a more valuable prize when the list of prizes to choose from was lengthened

•  Possible explana(on is that psychological costs of decision-‐making rise as number of alterna(ves rises, increasing number of people who accept the default

•  Re(rement saving example illustrates the default effect when the stakes are high

•  OPT OUT strategy

13-55

Lets Explore A Subscrip(on •  h:p://youtu.be/xOhb4LwAaJk

Choice Architecture: Narrow Framing

•  Narrow framing is the tendency to group items into categories and, when making choices, to consider only other items in the same category

•  Can lead to behavior that is hard to jus(fy objec(vely

•  Examples: –  Far more people are willing to pay $10 to see a

play aler losing $10 entering a theater vs. losing the (cket on the way in

–  Calculator and jacket example, decisions about whether to drive 20 minutes to save $5

•  These choices may be mistakes or may reflect the consumers’ true preferences

13-57

Please put your notes down for a moment

Narrow Framing •  Q1: Imagine you have decided to see a play

where admission is $10. As you enter the theatre you discover that you have lost a $10 bill. Would you s(ll buy a (cket to see the play?

•  Q2: Imagine you have bought a $10 (cket to see a play. As you enter the theatre you discover that you have lost the (cket. Would you buy a new (cket to see the play?

•  88% say yes to Q1


Narrow Framing •  Q1: Imagine you are about to buy a jacket for

$125 and a calculator for $15. The calculator salesman informs you that a store 20 minutes away offers the same calculator for $10. Would you make the trip to the other store?

•  Q2: Imagine you are about to buy a jacket for $15 and a calculator for $125. The calculator salesman informs you that a store 20 minutes away offers the same calculator for $120. Would you make the trip to the other store?


•  29% to Q2

Framing Experiment (I) •  Imagine the US is preparing for the

outbreak of an Asian disease, expected to kill 600 people (N = 152 subjects): –  If program A is adopted, 200 people will be saved

–  If program B is adopted, there is one third probability that 600 people will be saved and two thirds probability that no people will be saved

Framing Experiment (I) •  Imagine the US is preparing for the

outbreak of an Asian disease, expected to kill 600 people (N = 152 subjects): –  If program A is adopted, 200 people will be saved (72% preference)

–  If program B is adopted, there is one third probability that 600 people will be saved and two thirds probability that no people will be saved (28% preference)

Framing Experiment (II) •  Imagine the US is preparing for the

outbreak of an Asian disease, expected to kill 600 people (N = 155 subjects): –  If program C is adopted, 400 people will die

–  If program D is adopted, there is one third probability that nobody will die and two thirds probability that 600 people will die

Framing Experiment (II) •  Imagine the US is preparing for the

outbreak of an Asian disease, expected to kill 600 people (N = 155 subjects): –  If program C is adopted, 400 people will die (22% preference)

–  If program D is adopted, there is one third probability that nobody will die and two thirds probability that 600 people will die (78% preference)

What Choices Do we Give? •  How can our programs work with this?

–  Encourage default savings rates? –  Provide ranges for people to select using narrow choice

–  If we want to increase savings by workers, we could ask employers to ... enroll them automa(cally [in a 401k plan] unless they specifically choose otherwise.

–  If we want to increase the supply of transplant organs in the United States, we could presume that people want to donate, rather than trea(ng non-‐dona(on as the default. ...

–  If we want to increase charitable giving, we might give people the opportunity to join a ... plan, in which some percentage of their future wage increases are automa(cally given to chari(es...

–  If we want to respond to the recent problems in [credit markets], we might design disclosure policies that ensure consumers can see exactly what they are paying and make easy comparisons among the possible op(ons.

Subscrip(on Choice •  Dan Ariely demonstra(on •  Economist.com subscrip(on choices:

1.  1 year online access -‐ $59.00 2.  1 year print subscrip(on -‐ $125 3.  1 year online & print -‐ $125

Subscrip(on Choice •  Example demonstrated by Dan Ariely •  Experiment with MIT students asked

what they would choose Economist.com subscrip(on choices: 1.  1 year online access -‐ $59.00. 16% 2.  1 year print subscrip(on -‐ $125. 0% 3.  1 year online & print -‐ $125. 84%

Subscrip(on Choice •  Experiment 2, with students. Removed op(on 2, print

subscrip(on Economist.com subscrip(on choices 1.  1 year online access -‐ $59.00. 16% 68% 2.  1 year online & print -‐ $125. 84% 32%

Framing Example: Which sounds more

a:rac(ve? •  Cold Cuts •  90% Fat Free

•  Cold Cuts •  10% Fat

Source: TFS, Kahneman

Product Placement •  How will the placing of a product influence

what you buy?

Product Placement •  An in-‐store experiment was performed to

inves(gate the effects of shelf placement (high, middle, low) on consumers' purchases of potato chips.

•  Placement of potato chips on the middle shelf was associated with the highest percentage of purchases.

Source: Valdimar Sigurdsson, Hugi Saevarsson, and Gordon Foxall, J Appl Behav Anal. 2009 Fall; 42(3): 741–745. doi: 10.1901/jaba.2009.42-‐741

Influence of Emo(onal Arousal •  People have 2 sides

–  Emo(onal side –  Unemo(onal side

•  Effects decision making •  Appeal to side making decisions

Source: Dr. Dan Ariely h:p://www.youtube.com/watch?v=mFMDgW0wDeI

Why Free is Not Free •  “Why Free is Dangerous” •  h:p://www.youtube.com/watch?

v=TlXjdW0xQco

Credit Card Choice •  Card X •  9% APR •  $100 annual fee

•  Card Y •  14% APR •  $0 annual fee

What do you think?

Free •  Examples:

–  Free Banking Services, •  Free checking, free online services

–  Credit Cards with points and rewards •  Are they free for everybody? •  Who pays?

Choices Involving Time •  Many behavioral economists see standard

theory of decisions involving (me as too restric(ve, it rules out pa:erns of behavior that are observed in prac(ce

•  For example, theory rules out these three observed behaviors –  Preferences over a set of alterna(ves available at a

future date are dynamically inconsistent if the preferences change as the date approaches

–  The sunk cost fallacy is the belief that, if you paid more for something, it must be more valuable to you

–  Projec;on bias is the tendency to evaluate future consequences based on current tastes and needs

13-80

The Problem of Dynamic Inconsistency

•  Thought to reflect a bias toward immediate gra(fica(on, know as present bias –  A person with present bias olen suffers from

lapses of self-‐control •  Laboratory experiments have documented the

existence of present bias •  Precommitment is useful in situa(ons in which

people don’t trust themselves to follow through on their inten(ons

•  Precommitment is a choice that removes future op(ons –  Example: A student who wants to avoid driving

while intoxicated hands his car keys to a friend before joining a party

13-81

The Problem of Dynamic Inconsistency

•  People olen waste expensive gym memberships –  The LIU gym plan for faculty

We should ignore sunk costs but olen do not

•  Uncomfortable shoes •  Bad movie rentals •  Season (cket discounts lead to lower ini(al a:endance

Projec(on bias in forecas(ng future tastes and needs

•  Hungry shoppers tend to buy more than sated shoppers when shopping for the week ahead –  We olen remind people to not shop when they are hungry.

–  Do not shop for other things when you need immediately (when possible to plan ahead)

•  People tend to underes(mate their adaptability to change –  Giving up some spending to save or pay more to debt

•  Giving up cable, etc.

•  How does this affect planning for the future?

•  SMART Goals that are longer term?

Prospect Theory Revisited: Trouble Assessing Probabili(es

•  People tend to make specific errors in assessing probabili(es •  Hot-‐hand fallacy is the belief that once an event has occurred

several (mes in a row it is more likely to repeat –  Arises when people can easily invent explana(ons for streaks, e.g.,

basketball

13-86

•  Gambler’s fallacy is the belief that once an event has occurred it is less likely to repeat –  Arises when people can’t easily invent explana(ons

for streaks, e.g., state lo:eries •  Both fallacies have important implica(ons for

economic behavior, e.g., clearly relevant in context of inves(ng

•  Overconfidence causes people to: –  Overstate the likelihood of favorable events –  Understate the uncertainty involved

Hot-‐hand fallacy •  Philadelphia 76ers, 48 home games,

1980-‐81 season

Gambler’s fallacy •  A study of nearly 1800 daily drawings

between 1988 and 1992 in a New Jersey lo:ery showed that aler a number came up a winner, be:ors tended to avoid it

•  Do we see this bias in investors? – Many investor’s chase returns…

Overconfidence •  In one study of US students with an

average age of 22, 82% ranked their driving ability among the top 30% of their age group – Well I was a great drive at 16…

•  In the manufacturing sector, more than 60% of new entrants exit within five years; nearly 80% exit within ten years –  Yet people start businesses…

Please put your notes down again

Preferences Toward Risk •  Two puzzles involving observed behavior and

risk preferences •  Low probability events:

–  Experimental subjects exhibit aversion to risk in gambles with moderate odds

–  However, some subjects appear risk loving in gambles with very high payoffs with very low probabili(es

•  Aversion to very small risks: –  Many people also appear reluctant to take even

very (ny shares of certain gambles that have posi(ve expected payoffs

–  Implies a level of risk aversion so high it is impossible to explain the typical person’s willingness to take larger financial risks

13-92

Pick one: •  Op(on A: Win $2,500 •  Op(on B: Win $5,000 with 1/2 probability

Now Pick •  Op(on C: Win $5

•  Op(on D: Win $5,000 with 1/1000 probability

Low probability events grab all the a:en(on

•  Op(on A: Win $2,500 •  Op(on B: Win $5,000 with 1/2 probability •  Most choose Op(on A over B, sugges(ng risk-‐

averse preferences •  Op(on C: Win $5 •  Op(on D: Win $5,000 with 1/1000 probability •  A sizable majority picks Op(on D over C, which

is puzzling because the choice suggests risk-‐loving preferences

Extreme risk aversion •  Op(on A: Win $1,010 with 50% probability

and lose $1,000 with 50% probability •  Op(on B: Win $10.10 with 50% probability

and lose $10.00 with 50% probability

Extreme risk aversion •  Op(on A: Win $1,010 with 50% probability

and lose $1,000 with 50% probability •  Most people refuse this gamble •  Op(on B: Win $10.10 with 50% probability

and lose $10.00 with 50% probability •  Most people refuse this gamble too,

sugges=ng extreme risk aversion

Choices Involving Strategy •  Some of game theory’s apparent

failures may be a:ributable to faulty assump(ons about people’s preferences –  May not be due to fundamental problems with the theory itself

•  Many applica(ons assume that people are mo(vated only by self-‐interest

•  Players some(mes make decisions that seem contrary to their own interests

13-98

Voluntary Contribu(on Games •  In a voluntary contribu;on game:

–  Each member of a group makes a contribu(on to a common pool –  Each player’s contribu(on benefits everyone

13-99

•  Creates a conflict between individual interests and collec(ve interests

•  Like a mul(-‐player version of the Prisoners’ Dilemma

•  Game theory predicts the behavior of experienced subjects reasonably well

•  For two-‐stage voluntary contribu(on game, predic(ons based on standard game theory are far off

•  Assump(ons about players’ preferences may be incorrect

Importance of Social Mo(ves: The Dictator Game

•  In the dictator game: –  The dictator divides a fixed prize between himself and the recipient

–  The recipient is a passive par(cipant –  Usually no direct contact during the game

–  Strictly speaking, not really a game!

13-101

•  Most studies find significant generosity, a sizable frac(on of subjects divides the prize equally

•  Illustrates the importance of social mo(ves: altruism, fairness, status

Importance of Social Mo(ves: The Ul(matum Game

•  In the ul;matum game: –  The proposer offers to give the recipient some

share of a fixed prize –  The recipient then decides whether to accept or

reject the proposal –  If she accepts, the pie is divided as specified; if

she rejects, both players receive nothing

13-103

•  Theory says the proposer will offer a (ny frac(on of the prize; the recipient will accept

•  Studies show that many subjects reject very low offers; the threat of rejec(on produces larger offers

•  In social situa(ons, emo(ons such as anger and indigna(on influence economic decisions

Importance of Social Mo(ves: The Trust Game

•  In the trust game: –  The trustor decides how much money to invest –  The trustee divides up the principal and earnings

13-105

•  If players have no mo(ves other than monetary gain, theory says that trustees will be untrustworthy and trustors will forgo poten(ally profitable investments

•  Studies show that –  Trustors invested about half of their funds –  Trustees varied widely in their choices –  Overall, trustors received about $0.95 in return for

every dollar invested •  Many (but not all) people do feel obligated to jus(fy

the trust shown in them by others, thus many are willing to extend trust

•  This game helps us understand why business conducted on handshakes and verbal agreements works

Why is Saving So Difficult? •  We focus on what we give up? •  We are not really wired to focus on the future

–  takes energy to do so •  Money is abstract

–  Having more in re(rement by inves(ng? –  But money today money tomorrow is confusing choice for people

–  Cri(cal to present values in purchasing power or real terms

–  Talk to people in terms of annui(es –  h:p://youtu.be/-‐Cw4PiCB8X8

Example •  Instead of saying one needs 350,000 in

savings? –  Present as annuity –  If you save XYZ you can have ABC in re(rement income

•  PV 350,000, FV = 0, N = 20, I/Yr = 5 •  PMT = $28K per year •  Want more income? Save more…

Smart Couponing •  Are you familiar with prices? •  Comparison shop •  Shop with a list •  What is the goal?

–  Try new products? –  Save money?

Couponing •  Does buying more save you money? •  Coupons

–  Usually for non-‐generic, non-‐staples

Financial Habits •  What do you spend money on? •  How much is allocated for different

expenses? •  Where do you buy? •  When do you go shopping? •  What effect do your purchases have on

your goals?

Marke(ng to Your Personality •  Marketers study our habits •  Market to our perceived needs •  They also create needs and wants

Adver(sing & Emo(onal Appeals

•  Peer Approval or Social Acceptance •  Status •  Excitement •  Fear •  Other types?

Before Spending •  Why am I making this purchase?

–  Is there more than one reason? •  How will it effect me in the short & long

term? •  What will I be gevng & what will I be giving

up?

Before Shopping •  Comparison shop

–  Online –  big (cket items

•  Keep track of what you spend •  Be aware of your surroundings & marke(ng

influences –  Brick & Mortar

•  Design & Ambience –  Online

Mental Checklist

•  What should you consider before you go shopping?

Sources: •  Dan Ariely, Predictably Irra<onal, Videos on

You Tube •  Daniel Kahneman, Thinking, Fast & Slow, 2011

•  Valdimar Sigurdsson, Hugi Saevarsson, and Gordon Foxall, J Appl Behav Anal. 2009 Fall; 42(3): 741–745. doi: 10.1901/jaba.2009.42-‐741

What is the problem with free?

•  When free is dangerous… –  h:p://youtu.be/TlXjdW0xQco

Addi(onal Issues •  Influence of Arousal •  h:p://youtu.be/MuTP1XJWKmA

•  Cost of Social Norms •  h:p://youtu.be/AIqtbPKjf6Q

Some Addi(onal Cool Videos •  h:p://danariely.com/videos/#TOC24

•  Are We In Control of Our Decisions –  h:p://youtu.be/9X68dm92HVI

•  The IKEA Effect –  h:p://youtu.be/VQ_CncrR-‐uU

•  Paying More For Less –  h:p://youtu.be/vIS-‐OLgA8p4

Next Virtual Learning Event Webinar"The Culture of Personal Finance!•  June 5, 11 a.m. – 1 p.m. ET"•  Speaker: Dr. Barbara O’Neill"•  2 AFC CEUs available"•  More information:

https://learn.extension.org/events/1556#.U4S6Fa1dXrU"

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