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Chris Starmer. Day 4 - Experimental Games. TSU Short course in Experimental and Behavioural economics, 5-9 November 2012. Route Map. Part 1: Modelling social/strategic interaction as ‘games’ Some basic ideas in game theory From theory to empirics What people do and how to do well Part 2:

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Chris starmer

Chris Starmer

Day 4 - Experimental Games

TSU Short course in Experimental and Behavioural economics, 5-9 November 2012

Route map

Route Map

Part 1:

  • Modelling social/strategic interaction as ‘games’

    • Some basic ideas in game theory

  • From theory to empirics

    • What people do and how to do well

      Part 2:

  • Identifying Social Preferences

  • The impact of Social Preferneces



Game theory

One of the main tools of modern econ analysis

Models strategic interaction through stylised ‘games’

e.g. prisoner’s dilemma……..

The background story

The Background Story

  • Two people arrested on suspicion of a crime

    • Police not enough evidence to convict

  • Idea

    – place in separate cells (no communication)

    • Each has two options (confess or silent)

    • Consequences…………

Consequences for each depend on actions of both

Consequences for each depend on actions of both

  • If both confess:

    • Each get Moderate/High sentence (6 years)

  • If neither confess:

    • Each get Moderate/Light sentence (3 Years)

  • If one confesses while other remains silent

    • Confessor – gets very light sentence (1 year)

    • Silent gets very harsh sentence (10 Year)

In game theory analysis just requires the ranking of payoffs

In game theory, analysis just requires the RANKING of payoffs

That is, we translate from the absolute material payoffs to ordinal rankings

Consequences for each depend on actions of both1

Consequences for each depend on actions of both

  • If both confess:

    • Each get Moderate/High sentence (6 years) [2]

  • If both remain silent:

    • Each get Moderate/Light sentence (3 Years) [3]

  • If one confesses while other remains silent

    • Confessor – gets very light sentence (1 year) [4]

    • Silent gets very harsh sentence (10 Year) [1]

Interpreting payoffs

Interpreting Payoffs

  • These red numbers are called ‘utilities’

  • They represent each player’s own ranking of the possible outcomes of the ‘game’

  • For the game to be correctly specified, these ranking should include consideration of everything that the players care about

    • E.g. if there is ‘honour among thieves’ so they hate to confess, this should be reflected in ranking (and might change the rankings on previous slide)

Normal form

Normal Form

  • Now represent the game in ‘Normal Form’

    • Matrix

    • One player selects rows

    • Other selects columns

Prisoner s dilemma

Prisoner’s dilemma

Player 2 - Marina






Player 1

- Rati




(utility) payoffs in each cell with Row (Rati) written first in each pair

What will be the outcome of this game

What will be the outcome of this game?

  • What does game theory predict rational players would do?

  • What do ordinary people do in situations like this?

  • Will look at both of these questions but first………..

What would you do

What would you do?

Player 2 - the other prisoner











To help you – I’ve highlighted your (row) payoffs in RED

Now the game theory prediction

Now the game theory prediction

Very simple for this game

Game theoretic prediction

Game Theoretic Prediction

Player 2 - Marina








Player 1

- Rati






(utility) payoffs in each cell with Row (Rati) written first in each pair



Player 2 - Marina






Player 1

- Rati




The predicted outcome is (Pareto) SUBOPTIMAL - at the predicted outcomes, both players are worseoff than they could have been

3 cheers for rational choice theory

3 cheers for rational choice theory

  • This simple game demonstrates the power of rational choice analysis…..

  • It captures a fundamental insight of social science.

    • It is a mistake to assume that individual pursuit of self-interest fosters the good of all

    • Pursuit of rational self interest can lead to outcomes which are worse for all than others available

  • Maybe models many important social problems (e.g. pollution control, arms races etc.)

Real play

Real play

When real people play experimental games with payoffs structured so that they would be prisoner’s dilemma’s IF all they cared about was money payoffs they often manage to reach the pareto-superior outcome…….

In games with money payoffs people often reach the better joint payoff

In games with Money payoffs, people often reach the better joint payoff

Player 2 - Marina






Player 1

- Rati




How do they escape the dilemma

How do they escape the dilemma?

  • Happy ignorance?

    • They don’t understand the game

  • Enlightened reasoning?

    • E.g. Schelling reasoning

  • They have “social preferences”?

    • They don’t care just about the money

    • They care about each other’s outcomes

  • Players think of the game as part of an ongoing social game

Part 2


2 repeated games

2. Repeated Games

  • Would things be different if people play a PD game repeatedly?

    • Reputation building

  • GT tells us:

    • If PD games is one shot no opportunity for rational players to develop cooperative reputations

  • Repeated Game: can be Much More Complex and support cooperative strategies

Robert axelrod s book the evolution of cooperation

Robert Axelrod’s (book)The Evolution of Cooperation

  • Cooperation emerges spontaneously in the world in surprising places

    • e.g. trench warfare

  • Axelrod’s question

    • What’s the best strategy for playing the repeated prisoner’s dilemma?

Axelrod s tournament

Axelrod’s Tournament

  • Invited game theorists to participate in tournament

    • Repeated prisoner’s dilemma

  • Each participant submitted a strategy

    • Strategy, specifies ‘Coop’ or ‘Not’ for each round

    • can use history of moves to determine choice in any round

Round robin

Round Robin

  • 14 entries submitted, all by ‘professionals’

  • Strategies coded as computer programmes

  • Every strategy played repeated PD against:

    • every other strategy

    • itself

    • random (plays coop/not with p=0.5)

  • Each pairing played

    • 200 rounds

    • repeated 5 times (average performance)

The winner

The winner

  • Anatol Rapoport (Univ. Toronto)

  • Tit-for-Tat

    • first round: coop

    • subsequent rounds: copies opponents play in previous round

Why was tft successful

Why was TFT successful?

  • A single characteristic distinguished high from low-scoring strategies….

  • Being ‘NICE’?

  • NICE means;

    • don’t be the first to defect

    • cooperating in first round

  • Large gap between average scores of nice and not-nice strategies

Why did nice rules do well

Why did nice rules do well?

  • Because of the environment

  • Nice rules score highly when they meet

    • (they cooperate all the way through)

  • And,

    • there were enough nice rules around for them to raise each other’s scores

Which nice rules did best

Which Nice Rules Did Best?

  • Most successful nice rules tended to be ‘FORGIVING’

  • Forgivingness

    • is willingness to resume cooperation after the other player has failed to cooperate.

  • Notice: TFT is forgives rapidly

    • TFT resumes cooperation as soon as it observes the other player doing so

Why is it good to be forgiving

Why is it good to be forgiving?

  • Compare with another nice but non-forgiving strategy….


    • Cooperates until it observes non-cooperation

    • then will never cooperate again

  • This strategy does:

    • well with other nice rules

    • But with non-nice rules, once a non-cooperative move happens, there is never any future cooperation

Axelrod s advice for playing repeated pds

Axelrod’s advice for playing repeated PDs

  • Don’t be envious

    • don’t try to beat the other player

    • can only do this by actions which will undermine cooperation and joint payoff max

  • Don’ t be first to ‘cheat’

  • Reciprocate cooperation and cheating

  • Don’t be too clever!

    • Cooperation is helped by people understanding your behaviour

Concluding observations

Concluding Observations

  • GT predicts the behaviour of real people (worryingly) well in some settings

    • e.g. one shot, high payoff.

      • Cooperation may be more viable when there is repetition

    • what it is optimal to do depends on the environment

Session 4 part 2

Session 4 – Part 2

Social Preferences

People care about each others outcomes

People care about each others outcomes

“participants in experiments frequently choose actions that do not maximise their own monetary payoffs when those actions affect others’ payoffs. They sacrifice money in simple bargaining environments to punish those who mistreat them and share money with other parties who have no say in allocations” (Charness and Rabin, QJE 2002, p817)



  • Some of the early and most famous experimental evidence supporting this claim comes from studies using:

    • The Ultimatum game

Ultimatum game

Ultimatum Game

  • Two players have to divide a fixed pie p

  • Player 1 (the proposer) proposes a division

    • (x, p-x) x = proposer’s share

  • Player 2 (the responder) observes the offer and either accepts or rejects.

  • If she accepts the agreed upon division is implemented

  • If she rejects both players get nothing

  • Game theoretic prediction1

    Game theoretic prediction

    Standard assumptions:

    • Both players are rational, i.e. everyone maximizes only his/her monetary income

      Standard analysis (‘Backward induction’)

    • Player 2 will accept any positive offer

    • So, Player 1 will offer the smallest possible positive amount to player 2

    Stylised facts from ug expmts

    Stylised Facts from UG Expmts

    • Play deviates (systematically) from GT predictions:

      • close-to-even splits are often proposed

      • ‘low’ offers often rejected

    Specific study g th schmidtberger schwarze 1982 jebo

    Specific StudyGüth, Schmidtberger, Schwarze (1982, JEBO)

    • Pie size varied from 4 to 10 DM

    • Subjects were in one room, but no subject knew the person with whom he/she was paired

    • Real monetary stakes

      Two treatments

    • “Naive” (inexperienced) subjects

    • Experienced subjects: Same experiment one week later with same subjects

    Ultimatum game experiments g th schmidtberger schwarze 1982 jebo

    Ultimatum game experiments Güth, Schmidtberger, Schwarze (1982, JEBO)

    • Results

    • “Naive” subjects

    • Modal offer: 50% of the pie (7 of 21)

    • Mean offer: 37% of the pie

    • Experienced subjects

    • Mean offer: 32% of the pie

    • 2/21 offer 50%

    • Systematic deviation from game theoretic prediction

      GSS conclude: Game theory is “of little help in explaining ultimatum game behaviour”

    What happens when stakes rise

    What happens when stakes rise?

    Cameron (1999, Econ Inquiry) Experiments in Indonesia

    pie = from Rupiah 5000 (≈ $ 2.5) to R200 000 (≈ $ 100)

    R 200 000 ≈ 3 x average monthly expenditure

    • Results:

    • Offers approach 50/50 with increasing stakes

    • Responders more willing to accept a given percentage in higher stakes games

    What accounts for this behaviour

    What accounts for this behaviour?

    Two possible explanations:

    • Fairness, Altruism

    • Strategic concerns, fear of rejections

    • This has been investigated using ‘Dictator Games’………

    Dictator versus ultimatum games

    Dictator versus Ultimatum Games

    Forsythe, Horowitz, Savin and Sefton (Games and Economic Behavior, 1994)

    Dictator Game:

    Proposer decides on division (x, p-x), responder has no choice but to accept.

    • If concerns with fairness fully explain behaviour, the distribution of offers should be the same in both games

    Dictator versus ultimatum game results source fig 4 4 forsythe et al 1994

    Dictator versus Ultimatum Game Results:Source Fig 4.4. Forsythe, et al. 1994.

    Results suggest UG behaviour reflects a mix of altruistic and strategic concerns

    An application of social preferences

    An application of Social Preferences

    Voluntary Contributions to Public Goods

    Public goods

    Public Goods

    • (Pure) Public good

      • Once provided everybody benefits

      • Can’t exclude people from the benefit

    • Examples:Street lights, National defense

    • Standard economic analysis

      • PGs may not be provided by market because, self-interested individuals will not pay

      • (free ride)

    Public goods experiments

    Public Goods Experiments

    • Voluntary Contribution Mechanism

      • N Individuals; each allocated T tokens

      • divide between ‘private’ vs ‘public’ account

    • Public contributions raised by factor m

    • Each individual (i) receives payoff:

      πi = T – ci + (m/N).(∑contributions)

    • with 1 < m < N

      • full contribution (social optimum)

      • zero contribution (individual optimum)

    Public goods experiments findings

    Public Goods Experiments findings

    • Marwell & Ames (J. Pub Econ, 1981)

      • On average 40-60% contribution

      • except economists 

    • Most people are willing to contribute than theory (as usually interpreted) predicts

    Subsequent work on public goods

    Subsequent work on Public Goods

    Lots of experimental research on PGs using VCM.

    Two significant dimensions include experiments exploring:

    • Repetition

    • Role of social sanctions

      • Opportunity to punish ‘free riders’

    Repeated play in vcm

    Repeated Play in VCM

    • When groups play the PG game repeatedly

    • Contributions go down toward the game theory prediction (based on private money maximisation)

    The role of social sanctions

    The role of social sanctions

    Fehr and Gächter, (AER 2000)

    • a very influential experimental finding.

      Used a repeated VCM but;

    • modified to allow group members to sanction (i.e. punish) ‘free riders’.

    Design fehr and g chter aer 2000

    Design (Fehr and Gächter, AER 2000)

    2-stage game: 1st stage: standard Public good game

    2nd stage: punishment stage

    Each member learns contribution of others

    Can then assign punishment points

    A punishment point costs the punisher 1 point and reduces the punished member’s payoff by 3 points.

    • (Perfect stranger design)

    • Punishment is second order public good

    • GT Prediction: no punishment, no contributions

    Results people do punish even though it s a costly public good

    Results: people do punish (even though it’s a costly public good)

    Cooperation and punishment fehr and g chter aer 2000

    Cooperation and punishment(Fehr and Gächter, AER 2000)

    Punishment opportunities INCREASE contributions towards

    the socially optimal level



    • Pattern replicated in MANY studies

    • Many regard this finding (sanctions promote cooperation) as identifying an important mechanism that supports human cooperation.

      • May reflect psychological propensity to punish

      • That propensity supports the common good

    • It has been influential in many disciplines:

      • Including sociology, politicital science, anthropology and biology



    • Large literature exploring social preferences

    • These studies point to the need for economic models to take account of ‘other regarding’ motives

      • May be more important than economists have traditionally thought

    • Experimental research in social preferences may be revealing insights into fundamental mechanisms that support human cooperation

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