Behavioral Economics:
An introduction
Edward Cartwright
Outline of the session
1.
2.
3.
4.
5.
A brief introduction to behavioral economics.
Fairness and reciprocity.
Learning from new information.
Coordination problems.
A very brief look at other areas in behavioral
economics.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
2
Why behavioral economics?
The workhouse of economic modeling is
homo-economicus, an agent who:
1.
2.
3.
Optimally maximizes his expected utility.
Optimally updates his beliefs according to Bayes
rule.
Is selfish and without emotion, or, more formally,
does not care about the consumption and utility of
others.
This approach has yielded fantastic insight, but…
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
3
The motivation behind behavioral economics

Do people behave like homo-economicus?

If not, how do they behave?

What are the implications for economic
theory and policy?
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
4
What behavioral economics does?
It adds to the standard model of economics
some reality about how humans behave. In
particular, it adds,
 bounded rationality,
 biases in interpreting information,
 interdependent preferences,
 emotions,
 Learning,
 ….
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
5
What behavioral economics is not? Part I



It is not about throwing away the economics
textbook to start from scratch.
Behavioral economists fully recognize the crucial
role played by models based on homoeconomicus. [Many of them have helped to
develop them.]
They want to work with and adapt these models
to take account of human behavior in those
instances where it seems important to do so.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
6
What behavioral economics is not? Part II



It is not about reinventing psychology.
Behavioral economics does, and should draw on
psychology but
 is focused on different questions to psychology,
 retains the methodology and mathematical rigor
familiar in economics and game theory.
The ‘mindless economic’ debates continue on
how much neuroscience and evolutionary
psychology, and the like, really add to
economics.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
7
The basic nature of behavioral economics
1.
We can find that people do behave as if homoeconomicus.
2.
We can find that people have inter-dependent
preferences, and emotions, but are behaving
‘rationally’ relative to these.
3.
We can find that people are biased in choices and
how they interpret information.
4.
We can say something about settings where
outcomes are ambiguous with homo-economicus.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
8
The methods of behavioral economics

Experiments




Theoretical




lab based,
in the field,
neuroscience
game theory
decision theory
evolutionary theory
Simulation

Agent based models
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
9
What about policy?



By its nature behavioral economics should be
relevant in all areas of economic policy.
If policy is about influencing individuals (even if they
are within a corporate or other structure) then
behavioral economics is crucial to get things right.
Policy makers should be worried
about a science built on
Friedman’s positive methodology.
Behavioral economics is
diametrically opposite to a
positive methodology.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
10
A not too serious example

A problem for primary schools and nurseries is
parents picking their children up late. The school
must play the role of baby sitter.

Suppose that we fine parents for picking their
children up late?

The result can be more parents leaving their
children late because the fine makes it ‘ok’ to put a
burden on the school.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
11
The results of an experiment
Gneezy and Rustichini (2000) report an experiment in
day care centres in Haifa, Israel in 1998. In week 4 a fine
was introduced and in week 17 it was removed.
0.7
0.6
0.5
Proportion late

0.4
0.3
0.2
0.1
Test group
Control group
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Weeks of trial
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
12
A sketch history

Behavioral economics naturally emerged with game
theory in the 50’s and 60’s. The likes of Vernon Smith,
Kahneman and Selten showed it’s power.

From the 80’s onwards behavioral economics has been
the fastest growing area of economics. Partly due to
dissatisfaction with the ‘standard model’. Partly due to
the breadth of talent that has worked in the area.

But note that behavioral economics is not new.
Historically, economists, including Adam Smith,
Keynes and Marshall talked a lot about behavioral
tendencies.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
13
Topic I: Fairness and reciprocity

Have at look at the hypothetical scenarios
that you have been given.




How do you think people behave in these
scenarios (go with your instinct)?
How would homo-economicus behave?
Why are these scenarios different?
Why are they similar?
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
14
Dictator and ultimatum games

The Nash equilibrium in all of these games is
simple:


The receiver should accept any positive amount,
because something is better than nothing.
The proposer should propose that he will keep all
of the money, bar some minimal amount, because
the receiver will accept any offer.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
15
Ultimatum game results, part I

What we typically observe is that
 Median and modal offers are 40-50%.
 The mean offer is 30-40%.
 Offers below 20% are rejected about half
the time.
 High stakes, reputation and anonymity do
not change the results.
 Demographic variables have weak effects
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
16
Representative results from Eckel and
Grossman (2001)
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
17
Tipping, a real ultimatum game
How much would you tip?
 In a restaurant they visit frequently the mean
amount suggested was $1.28.
 In a restaurant in another city the mean
amount suggested was $1.27.
(Kahneman, Knetsch and
Thaler 1986)

Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
18
The dictator game, part I


What we typically observe is that
 Around 60% of people give money to the
other player.
 The mean amount given is around 20% of the
endowment.
The amount given is less than in the ultimatum
game but still positive. This is despite no threat
of rejection.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
19
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Different cultures
People all over the world have now been subjected to the
ultimatum game!
Cultural differences are significant and range from
competitive gift giving, to no sharing in sharing societies.
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
20
Story so far
Low offers create a negative response.
‘I would rather have nothing than accept such an
unfair offer.’
A fear of provoking a negative response can
increase offers.
‘I need to give him enough that he will not reject’
Generous offers are made even if no chance of
rejection.
‘It seems a bit unfair that I should get everything’
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
21
Context matters in the ultimatum game


With uncertainty offers are lower and less rejected
(but, in scenario 9, offers of $9 were rejected more
often than those of $8!)
With discrete choice low offers are rejected less
(Falk, Fehr and Fischbacher 2003):
Unchosen
offer
Interpretation
(5, 5)
How often (8,2) offer is
rejected
Proposed
Unfair
0.44
0.31
(2, 8)
Not sacrifical
0.27
0.73
(8, 2)
Neutral
0.18
-
(10, 0)
Fair
0.09
1.00
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
22
How about chimps?

Chimpanzees do behave according to Nash
equilibrium. They propose an unequal split and it is
not rejected (Jensen, Call, Tomasello 2007).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
23
How about children?

Murninghan and Saxon (1998), Harbaugh, Krause and
Vesterlund (2007) looked at behavior in the ultimatum game
with uncertainty with children and young adults.
Age
Partial info
Full info
5
9
12
32.54 47.58 48.7
42.25 37.3
44.3
15
33.3
42.7
Second graders
Twelfth graders
0.50
0.50
0.40
0.40
0.30
0.30
0.20
0.20
0.10
0.00
0
1
2
3
Dictator proposal
4
0
5
>=6
19
25.4
39.3
1
2
4
3
>=6
5
Ultimatum
proposal
0.10
0.00
2
0
1
1
2
3
Dictator proposal
0
4
5
>=6
3
4
5
>=6
Ultimatum
proposal
Context matters in the ultimatum game 2


When $1 given equals $3 received we see different
behavior (Andreoni and Miller 2002), 30-50% try to max
the min and 20-30% try to max the total.
Offers from a third party are rejected less (Blount 1995):
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
25
Context matters in the dictator game


The possibility to take money reduces positive
offers.
Earning the money reduces offers (List 2007):
Scenario
Positive
offers
median
Average
positive
Dictator
0.71
$1
.38
With take $1
0.35
$0
.31
With take $5
0.10
-$4.5
.42
With earnings
0.06
$0
.40
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
26
The data of List (2007).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
27
Groups propose less


When a group of individuals play the role of proposer
and responder in ultimatum and dictator game they
typically offer less, and reject less.
Further, interacting in a team lowers individual offers
(Luhan, Kocher, Sutter 2009).
Round 1
Round 2
Round 3
0.94
0.54
0.66
Individual 1.27
1.17
1.25
Team
1. Obvious discussion, don’t you think?
3. Sure, all for ourselves.
1. I am no good Samaritan. 3. Transfer 0. …
3. No.2, do you share our opinion? 2. I think we should be fair. ….
3. So, for heavens sake, make that 1!
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
28
What we observe



Reciprocity: many people seem to desire
reciprocity: ‘If someone does good (or bad) to
me then I want to do good (or bad) to them.
Fairness: people care about outcomes,
relative to others: ‘Why should I get less than
him’, ‘Why should I get more than him?’
Why is this different to the ‘standard model’.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
29
Fairness and reciprocity matter

In the standard model, utility is a function of
consumption, $10 is always as good as $10.

Behavioral economics emphasizes that it matters:
 where the $10 comes from; $10 stolen induces
guilt and shame while $10 earnt induces pride.
 How much others are getting; $10 when others
are getting $20 might be annoying but $10 when
others are earning $5 might induce guilt.

Note the important interaction between these two
effects.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
30
How to model fairness

There are now lots of models to model fairness and
reciprocity. We highlight and compare two:
 The Fehr-Schmidt model of inequality aversion.
This is simple and transparent but ignores the
importance of context.
 The Rabin model of fairness emphasizes the role
of motives but is cumbersome in applications.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
31
Fehr-Schmidt model of inequality aversion

Given an allocation (x1, x2, …, xn) a person’s
utility is
U i ( X )  xi 



i
max( x

n 1
k i
k
 xi ,0) 
i
max( x  x ,0)

n 1
k i
i
k
where 0 ≤ αi < 1 and βi ≤ α i.
So, players feel envy, as given by α i and guilt
as given by βi.
This is a very simple model that fits some of
the experimental data well.
But, it ignores motives and context.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
32
Rabin model of fairness

The kindness of player A towards player B is given by
 j (b j , ai )   jfair (b j )
f i (ai , b j )  max
 j (b j )   min
j (b j )

The utility of player A is then.
U i (ai , b j , ci )   i (ai , b j )  f 2 (b j , ai )[1  f1 (ai , b j )]



Utility thus depends on kindness given and perceptions
of kindness received.
This model does a very good fitting data that is hard to fit
(e.g. people are more likely to cooperate if they believe
others will cooperate).
But, it is a complicated model to apply.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
33
A neuroscience perspective


Unfair offers A
activate different
areas of the brain to
fair offers B (Sanfey
et. al. 2003).
After applying low
frequency rTMS to
disrupt dorsolateral
prefrontal cortex
(Knoch et. al. 2006).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
34
An evolutionary perspective




Much effort has been put in to explaining the
evolutionary origins of ‘altruism’.
Much ‘altruism’ can be explained as avoidance of
punishment. For example, offers in the ultimatum
game reflect the chance of being rejected.
This, however, raises a second order effect where
‘to punish is an altruistic act’.
Evolutionary models show that first order altruism is
unlikely to emerge, but second order altruism can,
primarily because punishment should be rare.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
35
Applications and policy


Fairness and reciprocity have wide ranging
applied and policy consequences.
One area with important consequences is
pricing and wage setting.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
36
Fairness and firm pricing

In Scenario 13: 68% would walk to save on the
calculator and 29% to save on the jacket (Kahneman
and Tversky 1984).

825 of respondents thought that the hardware store in
Scenario 15 was unfair (Kahneman, Knetsch and Thaler
1986)..
Ski resorts should charge high prices at christmas.
Tickets for the cup final should be more expensive. ….
Thaler (1985) compares some sports events:

1983 World Series
$25-30
1984 Super Bowl
$60
1984 Indianapolis 500
$125
1981 Holmes-Cooney fight
$600
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
37
Wage effort games

Similar to the ultimatum game (but more like
a game of trust) the Nash equilibrium is that:




The worker never puts in effort.
The employer, therefore, pays a low wage.
A bonus should never be paid so that makes
no difference.
A fine should provide a direct incentive for
worker effort.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
38
High wages are reciprocated


We observe that high wages induce more effort.
Fines do not work. Bonuses do.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
39
High wages are accepted by employers

If given the choice between hiring a low wage or
high wage worker employers may prefer the high
wage worker (see also Bewley 2000).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
40
Fairness in wages matters

In scenario 19, the change in real income is
similar but 62% thought a nominal reduction
unfair and only 22% a nominal increase unfair
(Tversky and Kahneman 1986).

In scenario 20, 62%
thought it unfair to cut
the wage but 78%
acceptable to hire a
cheaper worker.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
41
Some conclusions

Fairness and reciprocity are an important factor
in price and wage setting.
Supply and demand are not context independent.

Some policy thoughts,:
 Care is needed measuring collusion,
 Wage setting in the public sector is even more
important than we might have thought,
 People really need to be explained ‘why’
things are as they are.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
42
Topic II: Interpreting new information

Here is a new set of hypothetical scenarios.


How do you think people behave in these
scenarios?
Would you expect any biases in judgements?
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
43
The Law of Small Numbers

People exaggerate how closely a small sample will
represent the population.


In scenario A, 22% said the large hospital, 56% said
no difference, and 22% the small hospital.
In scenarios B and C people are usually reluctant to
do a run of the same coin (Rabin 2002, Walker and
Wooders 2001)
Pr(H|T)
Pr(H|HT)
Pr(H|HHT)
Pr(H|HHH … )
58.5%
46.0%
38.0%
29.8%
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
44
A model inference with the law of small
numbers - Rabin 2002.




A person observes a sequence of binary
signals from some i.i.d. process.
The person believes that they are generated
without replacement from an urn with N
signals.
The urn is replaced every other period.
The smaller is N the larger the bias.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
45
Consequences of the law of small numbers

We overestimate the importance of small samples.






We underestimate the importance of large samples. (In
Scenario D the real likelihood is 1% but the average guess
was 10%).
There is regression to the mean.
We read too much into long streaks of success or failure.
(There are no hot hands in basketball).
The sequence matters beyond averages.
We tend to think there is more variation in different
things then there really is.
If signals are endogenous we may underestimate
the rate of success.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
46
Confirmatory Bias



People tend to be too inattentive to new information
contradicting their hypothesis:
 They can ignore contradictory evidence, and
 Misread it as supporting their hypothesis.
In Scenario E, proponents of capital punishment
became more in favor and those against less in
favor having read the reports.
In Scenario F, those who though she was from a
poor background and saw the second video
estimated a lower amount of 3.71. Those who
thought she was from a well-to-do background
estimated higher at 4.67!
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
47
A model of confirmatory bias – Rabin and
Schrag 1999




A person receives a series of signals a or b.
The person perceives each signal as α or β.
After each signal the person updates their belief
about the hypothesis. He currently believes in the
hypothesis he perceives to have received more
signals.
If he currently believes in hypothesis X then he:
 Correctly perceives a signal supporting the
hypothesis.
 With probability q > 0 wrongly perceives a signal
against the hypothesis.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
48
Consequences of confirmatory bias




Information contradicting a hypothesis can be
ignored or miss-interpreted. This is particularly the
case if the information is ambiguous.
Hypothesis based filtering. People can used filtered
evidence inappropriately.
A person who has recently
changed his mind can be
under-confident in a
hypothesis.
The confirmatory bias need
not be eliminated by
increasing information.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
49
Health Care



Both patient and practitioner must form
hypotheses based on constantly changing
information.
The law of small numbers and confirmatory
bias could cause biased decision making.
The evidence is that we do observe such
biases (Frank 2004).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
50
Medical Practitioners


Practitioners must choose drugs and referral
services based on information about the drugs and
services.
In treating conditions like Oitis Media, Diabetes,
Depression and Asthma physicians regularly depart
from evidenced based practice.


In 1999 medicare spending was $9,941 per enrolee in
Miami and Florida compared to $4,886 in Minneapolis and
Minnesota.
They have been observed to rely on drugs they
become familiar with and not use new drugs or
lower cost versions of older drugs.

Risk adjusted mortality rates for CABG fell from 4.17% in
1989 to 2.45% in 1992 but there was no increase in
demand.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
51
Medical patients


Patients must choose which doctor to see and, in
some instances, which treatment to have.
The evidence suggests that patients do not use
information available about doctors or treatments.


Instead, they are more comfortable with doctors who
they are familiar with, they trust.


70% do not according to one survey.
76% would choose a doctor they are familiar with over one
more highly rated by experts.
Choices are typically made on the basis of factors
not really relevant to health care.

70% rely on the advice of family and friends.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
52
Learning to get rid of bias


Many have suggested that biases should
disappear with greater experience.
This seems, however, to not be the case:



In many instances there is not time to learn from
experience.
When there is time, the person may not realize
that they are biased, so cannot correct.
Even if a person does learn their bias, and its
costs, they typically do not apply this learning to a
new context.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
53
Policy Thoughts



Greater information and choice is not in itself
good enough.
It is necessary to counteract biases and
inertia.
Maybe people can be ‘forced’ to become
better informed:


Medical practitioners mixed and matched to learn
from each other,
Patients exposed to different practitioners.
Topic III: Coordination games

Here is a final set of hypothetical scenarios.


How do you think people behave in these
scenarios (go with your instinct)?
How would should they?
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
55
Weak link games



In an order-statistic coordination game it is a
Nash equilibrium for everyone to choose the
same number.
The Pareto superior Nash equilibrium is for
everyone to choose the highest number.
This, however, is risky, because if you
choose high and someone else chooses low
you loose out.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
56
Minimum effort games

The evidence is of a slippery slope towards the worst
outcome of choosing low numbers (van Huyck et. al.
1990).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
57
Without an effort cost

If effort is not costly groups do typically converge to the
Pareto optimum of high numbers (van Huyck et. al.
1990).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
58
The difficulties of coordinating

This graph clearly shows how groups cannot
coordinate well (Van Huyck et. al. 1990).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
59
Pairs do better

With groups of two there is coordination.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
60
Average effort games

Full coordination is not achieved (Van Huyck
et. al. (1991)
Scenario IV
9 players
Scenario IV
27 players
Scenario VI
Salient to
choose 4
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
61
Group size and order statistic matter

Results from Van Huyck et. al. (1997).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
62
Summary so far




People do seem to realise that there are gains to
coordinating.
In groups of size 4 or above play typically converges
to the bad equilibrium.
In average effort games
things are better but there is
still not full coordination.
This is consistent with
general evidence that people
like to avoid risk.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
63
Modelling choices in the weak link game –
Crawford (1991)

The model assumes that in each period





People update their beliefs about what others will
do according to a linear adjustment rule.
They best reply to this belief assuming they have
negligible influence
Eventually play will converge to an
equilibrium.
This equilibrium will depend on the intitial
beliefs of people.
The model does fit the data well.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
64
What about leadership

Some experiments we did recently show that
leadership works, but only just (Cartwright, Gillet
and van Vugt 2009).
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
65
International coordination problems
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
66
How to coordinate?



It has become fashionable to think of many
international issues as a global public good
game in which nations can free ride. For
example, the environment.
I would suggest that many of these issues
are actual weak link games.
That matters, because it means we do not
need to solve a free riding problem we just
need to get people coordinating.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
67
The problem is?



As we have seen, the evidence is not good
for coordination in weak link games.
There are, therefore, no simple solutions. At
least there are none we have found yet.
We need to somehow improve
communication and remove the risks of
bidding higher.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
68
Things I wish I had time for







Public economics: giving time and money to
public goods and charity.
Law and economics: tax evasion, corruption
and discrimination.
Economic development: culture matters.
Financial economics: bubbles and busts.
Mechanism design: designing auctions and
markets that ‘work’.
Inter-temporal choice: savings and pensions.
Health economics: addiction.
Edward Cartwright, Behavioral economics,
GES Summer School, University of Kent
69