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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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.
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"
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Welcome to the Military Families Learning Network !
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/"
For Resources, Recording, and More Information: "https://learn.extension.org/events/1555#.U4S4Va1dXrU
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
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
13-32
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
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
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
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
• 56% say yes to Q2
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?
• 68% say yes to Q1
• 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%
– 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:
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"