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Please share your email address with us! We’d like to send you a link to this webinar’s recording and resources, and notifications for future webinars. Provide feedback and earn CE Credit with one link: We will provide this link at the end of the webinar 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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Page 1: Welcome to the Military Families Learning Network Webinar ...€¦ · This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department

Please  share  your  email  address  with  us!  We’d like to send you a link to this webinar’s recording and resources,

and notifications for future webinars.  Provide  feedback  and  earn  CE  Credit  with  one  link:    

We will provide this link at the end of the webinar!

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.

Page 2: Welcome to the Military Families Learning Network Webinar ...€¦ · This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department

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.

Research and evidenced-based professional development "

through engaged online communities."eXtension.org/militaryfamilies"

" ""

Welcome to the Military Families Learning Network !

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POLL"

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Connect with the Personal Finance Team

» Facebook: PersonalFinance4PFMs » Twitter: #MFLNPF

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Personal Finance Twitter Cohort"A 2-week learning experience beginning June 9 presented by the MFLN Personal Finance team and the Network Literacy Community of Practice.!!•  Become a part of a community of learners that will form and

build your online network."•  Engage in conversations within the Twitter community

centered around your interests."•  Learn from guides that help new users maximize their Twitter

experience."•  For more information and to register:

https://twittercohort.wordpress.com/"

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For Resources, Recording, and More Information: "https://learn.extension.org/events/1555#.U4S4Va1dXrU

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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."

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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]    

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Page 10: Welcome to the Military Families Learning Network Webinar ...€¦ · This material is based upon work supported by the National Institute of Food and Agriculture, U.S. Department

Thinking  About  How  Our  Mind  Works  

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

Mammalian  

Einstein-­‐ian  

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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    

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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  

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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

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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

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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?  

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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

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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  

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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

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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

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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

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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

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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)

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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

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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

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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

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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

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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)

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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  

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

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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  

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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  

13-32

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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?    

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Have  merchants  like    Starbucks  influenced  our  thinking?  

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Thinking  About  Coffee  •  Have  marketers  shiled  how  we  think  about  

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

influences?  

 

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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.    

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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”

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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  

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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    

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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

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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”

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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  

 

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Real-­‐Estate  Experiment  Results  

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

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

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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  

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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  

Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint

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Incoherent  Choices:  Anchoring  

•  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

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Anchoring  •  Significance  of  anchoring  effects  for  many  

economic  choices  remains  unclear  •  What  do  you  think?  

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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  

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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

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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  

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A  Value  Func(on    in  Prospect  Theory

Gains Losses

-­‐ +

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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  

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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

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

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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

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Please  put  your  notes  down  for  a  moment  

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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?  

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•  88%  say  yes  to  Q1    

•  56%  say  yes  to  Q2  

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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?  

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•  68%  say  yes  to  Q1    

•  29%  to  Q2  

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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

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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)

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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

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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)

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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.  

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–  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.  

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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    

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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%    

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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%    

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Framing  Example:  Which  sounds  more  

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

•  Cold  Cuts    •  10%  Fat    

Source:  TFS,  Kahneman  

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Product  Placement  •  How  will  the  placing  of  a  product  influence  

what  you  buy?  

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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  

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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  

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

v=TlXjdW0xQco  

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

•  Card  Y  •  14%  APR  •  $0  annual  fee  

What  do  you  think?  

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Free  •  Examples:    

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

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

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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  

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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  

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The  Problem  of  Dynamic  Inconsistency  

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

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We  should  ignore  sunk  costs  but  olen  do  not  

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

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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.    

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•  How  does  this  affect  planning  for  the  future?  

•  SMART  Goals  that  are  longer  term?    

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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  

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•  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  

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

1980-­‐81  season  

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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…  

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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…  

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Please  put  your  notes  down  again  

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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  

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

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Now  Pick    •  Op(on  C:  Win  $5  

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

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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  

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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    

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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  

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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  

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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  

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•  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  

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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!  

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•  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  

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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  

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•  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  

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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  

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•  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  

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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    

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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…    

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

–  Try  new  products?  –  Save  money?  

 

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Couponing  •  Does  buying  more  save  you  money?  •  Coupons  

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

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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?  

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

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Adver(sing  &  Emo(onal  Appeals  

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

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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?    

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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  

 

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Mental  Checklist  

•  What  should  you  consider  before  you  go  shopping?  

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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  

   

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What  is  the  problem  with  free?    

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

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

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

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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    

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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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Military Families Learning Network"

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.

Family Development "Military Caregiving Personal Finance" Network Literacy"

Find all upcoming and recorded webinars covering:

http://www.extension.org/62581