Behavioral Economics: An introduction

Behavioral Economics:An introduction

Edward Cartwright

Edward Cartwright, Behavioral economics, GES Summer School,

University of Kent 2

Outline of the session

1. A brief introduction to behavioral economics.

2. Fairness and reciprocity.

3. Learning from new information.

4. Coordination problems.

5. A very brief look at other areas in behavioral economics.



Why behavioral economics?The workhouse of economic modeling is

homo-economicus, an agent who:

1. Optimally maximizes his expected utility.

2. Optimally updates his beliefs according to Bayes rule.

3. 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…



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?



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, ….



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 homo-economicus. [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.



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.



The basic nature of behavioral economics

1. We can find that people do behave as if homo-economicus.

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.



The methods of behavioral economics Experiments

lab based, in the field, neuroscience

Theoretical game theory decision theory evolutionary theory

Simulation Agent based models



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.



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.



The results of an experiment

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Weeks of trial

Pro

po

rtio

n l

ate

Test group

Control group

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.

A sketch history

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.



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.

But note that behavioral economics is not new. Historically, economists, including Adam Smith, Keynes and Marshall talked a lot about behavioral tendencies.



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?



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.



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

Representative results from Eckel and Grossman (2001)



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)



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.



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

0.1

0.2

0.3

0.4

0.5

0.6

0.7



Story so farLow 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’



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 How often (8,2) offer is

rejected Proposed

(5, 5) 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

How about chimps? Chimpanzees do behave according to Nash

equilibrium. They propose an unequal split and it is not rejected (Jensen, Call, Tomasello 2007).



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 5 9 12 15 19

Partial info 32.54 47.58 48.7 33.3 25.4

Full info 42.25 37.3 44.3 42.7 39.3

0 1 2 3 4 5 >=6

01

2345>=6

0.00

0.10

0.20

0.30

0.40

0.50

Dictator proposal

Ultimatum proposal

Second graders

0 1 2 3 4 5 >=6

01

23

45

>=6

0.00

0.10

0.20

0.30

0.40

0.50

Dictator proposal

Ultimatum proposal

Twelfth graders



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):



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



The data of List (2007).



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

Team 0.94 0.54 0.66

Individual 1.27 1.17 1.25

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!



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’.



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.



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.



Fehr-Schmidt model of inequality aversion Given an allocation (x1, x2, …, xn) a person’s

utility is

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.

ik

kii

ikik

iii xx

nxx

nxXU )0,max(

1)0,max(

1)(



Rabin model of fairness The kindness of player A towards player B is given by

The utility of player A is then.

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.

)()(

)(),(),(

minmaxjjjj

jfairjijj

jii bb

babbaf

)],(1)[,(),(),,( 12 jiijjiiijii bafabfbacbaU



A neuroscience perspective

After applying low frequency rTMS to disrupt dorsolateral prefrontal cortex

(Knoch et. al. 2006).

Unfair offers A activate different areas of the brain to fair offers B (Sanfey

et. al. 2003).



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.



Applications and policy Fairness and reciprocity have wide ranging

applied and policy consequences. One area with important consequences is

pricing and wage setting.



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



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.



High wages are reciprocated We observe that high wages induce more effort. Fines do not work. Bonuses do.



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).

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.





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.



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?



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) 58.5%

Pr(H|HT) 46.0%

Pr(H|HHT) 38.0%

Pr(H|HHH … ) 29.8%



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.



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.



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!



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.



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.



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).



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.



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. 70% do not according to one survey.

Instead, they are more comfortable with doctors who they are familiar with, they trust. 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.



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.

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?

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.



Minimum effort games The evidence is of a slippery slope towards the worst

outcome of choosing low numbers (van Huyck et. al. 1990).



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).



The difficulties of coordinating This graph clearly shows how groups cannot

coordinate well (Van Huyck et. al. 1990).



Pairs do better With groups of two there is coordination.



Full coordination is not achieved (Van Huyck et. al. (1991)

Average effort games



Scenario VI

Salient to choose 4

Scenario IV 27 players

Scenario IV 9 players

Group size and order statistic matter Results from Van Huyck et. al. (1997).



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.

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).



International coordination problems



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.



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.





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.

Behavioral Economics: An introduction

Documents

Transcript of Behavioral Economics: An introduction