1.
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.

2.
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

3.
POLL

4.
Connect with the Personal
Finance Team
» Facebook: PersonalFinance4PFMs
» Twitter: #MFLNPF

5.
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/

6.
For Resources, Recording, and More Information:
https://learn.extension.org/events/1555#.U4S4Va1dXrU

7.
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.

8.
Heuristics, Anchoring,
Narrowing Choice
Dr. Michael S Gutter
Interim Family and Consumer Science Program Leader
MLFN Personal Finance Team
@mikegutter
msgutter@ufl.edu

9.
Thinking About How Our
Mind Works
• GPA Example
– http://youtu.be/KyM3d4gQGhM
Mammalian
Einstein-ian

10.
Interesting Idea
• So how do we view ourselves?
• Our status?
• What we have?
• Depends on what others have around us…
• http://youtu.be/_ERQEVdIinc

11.
Are we predictably irrational?
• It is not surprising that we are not always
perfectly rational
• But are our departures from perfect
rationality completely random?
• Or are these departures predictable?
• If we can find predictable patterns of
irrationality in human behavior, then we
can improve economic theory

12.
Motivations and Objectives
• The two main motivations for behavioral economics concern
apparent weaknesses in standard economic theory:
– People sometimes 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 objective is to modify, supplement, and enrich
economic theory by adding insights from psychology
– Suggesting that people care about things standard theory typically
ignores, like fairness or status
– Allowing for the possibility of mistakes
13-13

13.
Methods
• Behavioral economics uses many of the same
tools and frameworks as standard economics
– Assumes individuals have well-defined objectives,
that objectives and actions are connected, and
actions affect well-being
– Relies on mathematical models
– Subjects theories to careful empirical testing
• 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

14.
A Representativeness Example
• Consider the following description:
“Steve is very shy and withdrawn, invariably
helpful, but with little interest in people, or
in the world of reality. A meek and tidy
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?

15.
Rules of Thumb/Heuristics
• Thinking through every alternative 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 optimal, using one is not necessarily
a mistake
• Allow judgment and decision making in
cases where specific and accurate solutions
are either unknown or unknowable
13-16

16.
Rules of Thumb/Heuristics
• Example: saving
– In economic models finding the best rate of
savings involves complex calculations
– In practice 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
representativeness are frequently
considered “metaheuristics” since they
engender many specific effects

17.
Three Major Human Probability-Assessment
Heuristics/Biases
(Tversky and Kahneman, 1974)
• Representativeness
– 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
– Initial estimated values affect the final estimates, even
after considerable adjustments

18.
The Representativeness
Heuristic
• We often judge whether object X belongs to class Y by how
representative X is of class Y
• For example, people order the potential occupations by
probability and by similarity in exactly the same way
• The problem is that similarity ignores multiple biases

19.
Representative Bias (1):
Insensitivity to Prior
Probabilities
• The base rate of outcomes should be a major factor in
estimating their frequency
• However, people often ignore it (e.g., there are more
farmers than librarians)
– E.g., the lawyers vs. engineers experiment:
• Reversing the proportions (0.7, 0.3) in the group had no effect on
estimating a person’s profession, given a description
• Giving worthless evidence caused the subjects to ignore the odds
and estimate the probability as 0.5
– Thus, prior probabilities of diseases are often ignored when
the patient seems to fit a rare-disease description

20.
Representative Bias (2):
Insensitivity to Sample Size
• The size of a sample withdrawn from a population 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

21.
Representative Bias (3):
Misconception of Chance
• People expect random sequences to be “representatively
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
– After a run of reds in a roulette, black will make the overall run more
representative (chance as a self-correcting process??)
• Even experienced research psychologists believe in a law of
small numbers (small samples are representative of the
population they are drawn from)

22.
Representative Bias (4):
Insensitivity to Predictability
• People predict future performance mainly by similarity of
description to future results
• For example, predicting future performance as a teacher
based on a single practice lesson
– Evaluation percentiles (of the quality of the lesson) were
identical to predicted percentiles of 5-year future
standings as teachers

23.
The Availability Heuristic
• The frequency of a class or event is often
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

24.
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 relative
frequency of famous names
• Salience affects “retrievability”
– E.g., watching a car accident increases subjective
assessment of traffic accidents

25.
The Anchoring and Adjustment Heuristic
• People often estimate by adjusting an
initial value until a final value is reached
• Initial values might be due to the problem
presentation or due to partial
computations
• Adjustments are typically insufficient and
are biased towards initial values, the
anchor

26.
Anchoring and Adjustment Biases (1): Insufficient
Adjustment
• Anchoring may occur due to incomplete calculation, such
as estimating 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 (Quattrone et al., 1984)
• Anchoring occurs even when experts (real-estate agents)
estimate real-estate prices (Northcraft and Neale, 1987)

27.
Anchoring and Adjustment Biases (2): Evaluation of
Conjunctive and Disjunctive Events
• People tend to overestimate the
probability of conjunctive events
(e.g., success of a plan that requires
success of multiple steps)
• People underestimate the probability
of disjunctive 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

28.
Anchoring
• http://youtu.be/HefjkqKCVpo

29.
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 participant
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

30.
Incoherent Choices:
Anchoring• Anchoring occurs when someone’s choices are linked to
prominent but irrelevant information
• 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 suggestion
– Skeptics note that subjects had little experience purchasing the goods in
the experiment
– Might have been less sensitive to suggestion if used familiar products
• Significance of anchoring effects for many economic choices
remains unclear
13-32

31.
Changing the Anchor:
Getting in Line Behind Yourself
• Why does someone pay so much for
Starbuck’s Coffee?
• http://youtu.be/FaO3aGmuNFc
• Can we lower the anchor?

32.
Have merchants like Starbucks influenced our
thinking?

33.
Thinking About Coffee
• Have marketers shifted how we think about
coffee and our price point
• To what extent can we filter external
influences?

34.
Anchoring
Source: Dan Ariely, Predictably Irrational: Chapter 2 Supply and
Demand video at
http://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 actions.

35.
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”

36.
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 = ratio between
differences
• Anchoring index = 0 for people able to
ignore anchor

37.
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

38.
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

39.
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”

40.
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 listing price did not influence

41.
Real-Estate Experiment Results
• Anchoring index for real-estate
professionals was 41%
• Anchoring index for business school
students was found to be 48%

42.
Negotiation and Anchoring
• Sellers point of view – anchor your thinking to a higher price
• Price presented
• Focus attention and search memory for arguments against the
anchor

43.
Incoherent Choices:
Anchoring
• Anchoring occurs when someone’s choices are linked to
prominent but irrelevant information
• Suggests that some choices are arbitrary and can’t reflect
meaningful preferences
Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint

44.
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 suggestion
– Skeptics note that subjects had little experience
purchasing the goods in the experiment
– Might have been less sensitive to suggestion if
used familiar products
Source: Dr. Michael Gutter, Behavioral Economics, PowerPoint

45.
Anchoring
• Significance of anchoring effects for many
economic choices remains unclear
• What do you think?

46.
Endowment Effect
• Half the participants 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

47.
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
valuation
• Some economists think this reflects something
fundamental about the nature of preferences
• Incorporating the endowment effect into standard
theory implies an indifference curve kinked at the
consumer’s initial consumption bundle
– Smooth changes in price yield abrupt changes in
consumption
13-50

48.
A Special Type of Bias: Framing
• Risky prospects can be framed in different ways- as
gains or as losses
• Changing the description 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 negative effect of a loss is
larger than the positive 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 evaluation of the same prospect
• May resolve a number of puzzles related to risky
decisions

49.
A Value Function
in Prospect Theory
GainsLosses
- +

50.
Default effect: retirement
• Prior to April 1, 1998, the default option was nonparticipation in the
retirement plan
• After 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 option changed

51.
Bias Toward the Status Quo:
Default Effect
• When confronted with many alternatives, people
sometimes avoid making a choice and end up with
the option that is assigned as a default
• Example: Experiment showing that more subjects
kept $1.50 participation fee rather than trading it
for a more valuable prize when the list of prizes to
choose from was lengthened
• Possible explanation is that psychological costs of
decision-making rise as number of alternatives rises,
increasing number of people who accept the default
• Retirement saving example illustrates the default
effect when the stakes are high
• OPT OUT strategy
13-55

52.
Lets Explore A Subscription
• http://youtu.be/xOhb4LwAaJk

53.
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 justify
objectively
• Examples:
– Far more people are willing to pay $10 to see a
play after losing $10 entering a theater vs. losing
the ticket 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

54.
Please put your notes down
for a moment

55.
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 still buy a ticket to see
the play?
• Q2: Imagine you have bought a $10 ticket
to see a play. As you enter the theatre you
discover that you have lost the ticket.
Would you buy a new ticket to see the
play?

56.
• 88% say yes to Q1
• 56% say yes to Q2

57.
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?

58.
• 68% say yes to Q1
• 29% to Q2

59.
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

60.
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)

61.
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

62.
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)

63.
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 automatically [in a 401k
plan] unless they specifically choose
otherwise.

64.
– If we want to increase the supply of transplant
organs in the United States, we could presume
that people want to donate, rather than treating
non-donation 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 automatically given to
charities...
– 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 options.

65.
Subscription Choice
• Dan Ariely demonstration
• Economist.com subscription choices:
1. 1 year online access - $59.00
2. 1 year print subscription - $125
3. 1 year online & print - $125

66.
Subscription Choice
• Example demonstrated by Dan Ariely
• Experiment with MIT students asked
what they would choose
Economist.com subscription choices:
1. 1 year online access - $59.00. 16%
2. 1 year print subscription - $125. 0%
3. 1 year online & print - $125. 84%

67.
Subscription Choice
• Experiment 2, with students. Removed option 2, print
subscription
Economist.com subscription choices
1. 1 year online access - $59.00. 16% 68%
2. 1 year online & print - $125. 84% 32%

68.
Framing Example:
Which sounds more
attractive?
• Cold Cuts
• 90% Fat Free
• Cold Cuts
• 10% Fat
Source: TFS, Kahneman

69.
Product Placement
• How will the placing of a product influence
what you buy?

70.
Product Placement
• An in-store experiment was performed to
investigate 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

71.
Influence of Emotional Arousal
• People have 2 sides
– Emotional side
– Unemotional side
• Effects decision making
• Appeal to side making decisions
Source: Dr. Dan Ariely
http://www.youtube.com/watch?v=mFMDgW0wDeI

72.
Why Free is Not Free
• “Why Free is Dangerous”
• http://www.youtube.com/watch?v=TlXjdW
0xQco

73.
Credit Card Choice
• Card X
• 9% APR
• $100 annual fee
• Card Y
• 14% APR
• $0 annual fee
What do you think?

74.
Free
• Examples:
– Free Banking Services,
• Free checking, free online services
– Credit Cards with points and rewards
• Are they free for everybody?
• Who pays?

75.
Choices Involving Time
• Many behavioral economists see standard
theory of decisions involving time as too
restrictive, it rules out patterns of behavior that
are observed in practice
• For example, theory rules out these three
observed behaviors
– Preferences over a set of alternatives 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
– Projection bias is the tendency to evaluate future
consequences based on current tastes and needs
13-80

76.
The Problem of Dynamic
Inconsistency
• Thought to reflect a bias toward immediate
gratification, know as present bias
– A person with present bias often suffers from
lapses of self-control
• Laboratory experiments have documented the
existence of present bias
• Precommitment is useful in situations in which
people don’t trust themselves to follow
through on their intentions
• Precommitment is a choice that removes
future options
– Example: A student who wants to avoid driving
while intoxicated hands his car keys to a friend
before joining a party
13-81

77.
The Problem of Dynamic
Inconsistency
• People often
waste
expensive
gym
memberships
– The LIU gym
plan for
faculty

78.
We should ignore sunk costs
but often do not
• Uncomfortable shoes
• Bad movie rentals
• Season ticket discounts lead to lower initial attendance

79.
Projection bias in forecasting
future tastes and needs
• Hungry shoppers tend to buy more than sated
shoppers when shopping for the week ahead
– We often 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 underestimate their adaptability
to change
– Giving up some spending to save or pay
more to debt
• Giving up cable, etc.

80.
• How does this affect planning for the
future?
• SMART Goals that are longer term?

81.
Prospect Theory Revisited:
Trouble Assessing Probabilities
• People tend to make specific errors in assessing probabilities
• Hot-hand fallacy is the belief that once an event has occurred
several times in a row it is more likely to repeat
– Arises when people can easily invent explanations for streaks, e.g.,
basketball
13-86

82.
• 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 explanations
for streaks, e.g., state lotteries
• Both fallacies have important implications for
economic behavior, e.g., clearly relevant in context
of investing
• Overconfidence causes people to:
– Overstate the likelihood of favorable events
– Understate the uncertainty involved

83.
Hot-hand fallacy
• Philadelphia 76ers, 48 home games, 1980-
81 season

84.
Gambler’s fallacy
• A study of nearly 1800 daily drawings
between 1988 and 1992 in a New Jersey
lottery showed that after a number came
up a winner, bettors tended to avoid it
• Do we see this bias in investors?
– Many investor’s chase returns…

85.
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…

86.
Please put your notes down
again

87.
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
probabilities
• Aversion to very small risks:
– Many people also appear reluctant to take even
very tiny shares of certain gambles that have
positive 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

88.
Pick one:
• Option A: Win $2,500
• Option B: Win $5,000 with 1/2 probability

89.
Now Pick
• Option C: Win $5
• Option D: Win $5,000 with 1/1000
probability

90.
Low probability events grab all
the attention
• Option A: Win $2,500
• Option B: Win $5,000 with 1/2 probability
• Most choose Option A over B, suggesting risk-
averse preferences
• Option C: Win $5
• Option D: Win $5,000 with 1/1000 probability
• A sizable majority picks Option D over C, which
is puzzling because the choice suggests risk-
loving preferences

91.
Extreme risk aversion
• Option A: Win $1,010 with 50% probability
and lose $1,000 with 50% probability
• Option B: Win $10.10 with 50% probability
and lose $10.00 with 50% probability

92.
Extreme risk aversion
• Option A: Win $1,010 with 50% probability
and lose $1,000 with 50% probability
• Most people refuse this gamble
• Option B: Win $10.10 with 50% probability
and lose $10.00 with 50% probability
• Most people refuse this gamble too,
suggesting extreme risk aversion

93.
Choices Involving Strategy
• Some of game theory’s apparent
failures may be attributable to faulty
assumptions about people’s
preferences
– May not be due to fundamental problems
with the theory itself
• Many applications assume that people
are motivated only by self-interest
• Players sometimes make decisions that
seem contrary to their own interests
13-98

94.
Voluntary Contribution Games
• In a voluntary contribution game:
– Each member of a group makes a contribution to a common pool
– Each player’s contribution benefits everyone
13-99

95.
• Creates a conflict between individual
interests and collective interests
• Like a multi-player version of the Prisoners’
Dilemma
• Game theory predicts the behavior of
experienced subjects reasonably well
• For two-stage voluntary contribution game,
predictions based on standard game theory
are far off
• Assumptions about players’ preferences
may be incorrect

96.
Importance of Social Motives:
The Dictator Game
• In the dictator game:
– The dictator divides a fixed prize
between himself and the recipient
– The recipient is a passive participant
– Usually no direct contact during the
game
– Strictly speaking, not really a game!
13-
101

97.
• Most studies find significant generosity, a
sizable fraction of subjects divides the prize
equally
• Illustrates the importance of social motives:
altruism, fairness, status

98.
Importance of Social Motives:
The Ultimatum Game
• In the ultimatum 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

99.
• Theory says the proposer will offer a tiny
fraction of the prize; the recipient will
accept
• Studies show that many subjects reject very
low offers; the threat of rejection produces
larger offers
• In social situations, emotions such as anger
and indignation influence economic
decisions

100.
Importance of Social Motives:
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

101.
• If players have no motives other than monetary
gain, theory says that trustees will be untrustworthy
and trustors will forgo potentially 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 justify
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

102.
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 retirement by investing?
– But money today money tomorrow is
confusing choice for people
– Critical to present values in purchasing
power or real terms
– Talk to people in terms of annuities
– http://youtu.be/-Cw4PiCB8X8

103.
Example
• Instead of saying one needs 350,000 in
savings?
– Present as annuity
– If you save XYZ you can have ABC in
retirement income
• PV 350,000, FV = 0, N = 20, I/Yr = 5
• PMT = $28K per year
• Want more income? Save more…

104.
Smart Couponing
• Are you familiar with prices?
• Comparison shop
• Shop with a list
• What is the goal?
– Try new products?
– Save money?

105.
Couponing
• Does buying more save you money?
• Coupons
– Usually for non-generic, non-staples

106.
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?

107.
Marketing to Your Personality
• Marketers study our habits
• Market to our perceived needs
• They also create needs and wants

108.
Advertising & Emotional
Appeals
• Peer Approval or Social Acceptance
• Status
• Excitement
• Fear
• Other types?

109.
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 getting & what will I be giving
up?

110.
Before Shopping
• Comparison shop
– Online
– big ticket items
• Keep track of what you spend
• Be aware of your surroundings & marketing
influences
– Brick & Mortar
• Design & Ambience
– Online

111.
Mental Checklist
• What should you consider before you go
shopping?

112.
Sources:
• Dan Ariely, Predictably Irrational, 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

113.
What is the problem with
free?
• When free is dangerous…
– http://youtu.be/TlXjdW0xQco

114.
Additional Issues
• Influence of Arousal
• http://youtu.be/MuTP1XJWKmA
• Cost of Social Norms
• http://youtu.be/AIqtbPKjf6Q

115.
Some Additional Cool Videos
• http://danariely.com/videos/#TOC24
• Are We In Control of Our Decisions
– http://youtu.be/9X68dm92HVI
• The IKEA Effect
– http://youtu.be/VQ_CncrR-uU
• Paying More For Less
– http://youtu.be/vIS-OLgA8p4

116.
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#.U4S6F
a1dXrU

117.
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