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Update: Reciprocity in Groups and Third Party Punishment

Robert Kurzban

University of Pennsylvania

Hokkaido University

8 Nov 2006

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Roadmap

• Public Goods Work

• Theories in the spotlight

• Third Party Punishment

• Directions

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Remember this? Real Time Public Goods Game

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Low Info & Increase Only

High Info & Increase/Decrease

High Info & Increase Only

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Well, it should look familiar…

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US

Japan (all)

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Replication in Japan:Dynamics

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Elapsed time (Seconds)C

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Japan Data(Ishii & Kurzban)

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Elapsed time (Seconds)

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2530

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Contributions by Round in the Increase Only/Low Information Condition

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New Questions

• Are there types? [Can this explain both the upward and downward spirals?]

• Can we get more specific about reciprocal players? Median matching? Minimum reciprocity?

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Circular Game: Method(Kurzban & Houser, PNAS, 2005)

• “Circular” Public Goods gamePlayers make initial contributionPlayers, in turn, observe aggregate contribution of other

playersAfter observing this value, player may update their own

contributionRound ends with p = .04 each update

• This allows us to plot a “contribution profile” for each player (CP)

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Individual Differences

• This method allows us to plot a “contribution profile” for each player (CP)

• Regress contribution on information observed.• This gives an intercept and slope.• Intercept ~ how much player i contributes when

others aren’t contributing much• Slope ~ player i’s responsiveness to others’

contributions

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Individual Differences

• Free Rider = CP everywhere below 25 (1/2)20% of sample (N = 84)

• Cooperator = CP everywhere above 2513%

• Recriprocator = positive slope, and CP is both above and below the 50% line.63%

• Small percentage unclassifiable

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Individual Differences

• We use some rounds to see if typing scheme captures something stable.

• If so, we should be able to predict (in a hold-out sample) the dynamics of play given the makeup of the constituted groups.Groups are assigned a “Cooperativeness

Score,” 2 for a Cooperator, 1 for a Reciprocator, 0 for a Free Rider…

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Mean Contribution PathGroups with Score = 2

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Round

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F irst Seven Games Hold-out sample

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Mean Contribution PathGroups with Score = 5

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• Types fit reasonably into a 3-part system

• Payoffs did not vary as a function of type.Suggest individual differences in strategies?

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Information Seeking

Method:• Circular game, but allow players to observe one

piece of information (low, median, high) before making their own contribution decision.

• Other parameters as in Experiment 2• One Independent Variable: This information is

either free, or costly (2 tokens)

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Information SeekingHypotheses:

1. IF players know others respond to own contribution, costly information should decrease contributions.

2. IF players have reciprocal (type) preferences, they will have systematic preferences for information and will pay to observe it.

3. Type (reciprocator, free rider) will predict information-seeking preferences

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Results

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Results: Information-Seeking

Free Info

(%)

Costly Info (%) (conditional on paying)

Low 35 11 (23)

Median 35 21 (46)

High 30 14 (30)

None na 54 (n/a)

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Results: Information-SeekingIndividual Differences

Regress subjects' contribution amounts on contributions seen. • Reciprocity Index (RI) slope, how much i is “influenced” by others’ contributions. • Altruism Index (AI) is the y-intercept: i’s contribution when other’s contribution = 0 • Free-riding Index (FI) i’s contribution when contribution seen equals 50 (subtracted from 50 -- high values identify free-riding.)

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Results: Information-SeekingIndividual Differences

(non-randomly chosen) examples of typing regression for 3 s’s,

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Results: Information-SeekingIndividual Differences More reciprocal

players like median information

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Results: Information-SeekingIndividual Differences Free Riders like

high information

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Results: Information-SeekingIndividual Differences

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Information Seeking: Results

• Types: [max (AI, FI, RI*50)]. In the “Free Information” condition, payoffs did not vary as a function of type.

• In the “Costly Information” condition, Free Riders did better than Reciprocators or Altruists.

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Experiment 3: Conclusions

• There is a tendency to prefer observing the MEDIAN current contributor. (oops)

• People will endure costs to observe others’ decisions.

• Reciprocators tend to look at the median (Croson 1998)

• In contrastFree Riding types tend to look at the high information. Altruistic types don’t have clear preferences

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Part II: Third Party Punishment

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Third Party Punishment

• If A violates a norm, for example, [A cheats B], people (C) seem to express a preference for punishing A.

• There is, however, substantial debate about the scope of the phenomenon, as well as its evolutionary explanation.

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Third Party Punishment ≠ Second Party Punishment

• If A cheats B, B has a preference for inflicting costs on A. Substantial evidence from field and labTrivers (1971) theory of reciprocal altruism

provides one possible explanation for this phenomenon.

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Third Party Punishment

• A puzzle from either the standpoint of evolution or the canonical economic view. Letting others endure costs of punishment

would seem to be a good strategy. Why pay costs of punishing is the underlying

question.

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Punishment ( “negative reciprocity”) & Cooperation in Groups

Cultural group selection (Boyd et al., 2003, PNAS)

• Groups with those with such a taste do better because they give incentives to others in the group to be pro-social.

“Strong Reciprocity” (Gintis, 2000, JTB)• Groups with punishers to better than those without.

Inequity aversion driven by reduction of fitness differentials; (Price et al., 2003, EHB).

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3rd Party Punishment

• On some (recent) models, signaling that one punishes norm violators or, more narrowly, those who defect, leads to benefits through reputational processes. e.g., “Indirect reciprocity” (Panchanathan and

Boyd, 2004, Nature). Signaling models (E A Smith, etc.)

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Comparing models

• So. Some models don’t specifically predict

sensitivity to audience effects (though such effects don’t rule out MLS)

To the extent that 3rd party punishment is sensitive to cues to the presence of an audience,

this implies a history of selection associated with reputation effects.

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Experiments showing effects of “blinding” and “social distance”

• Dictator Games Dictator game – as “social distance” decreases,

altruism increases. (Hoffman et al., 1996)

• Public goods games Buchan et al. – Personal communication…

• Ultimatum games Bolton & Zwick. Anonymity has VERY

LIMITED effects on rejecting unequal offers.

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Method (con’t)• Part I

Trust game – each DM1 plays 5 games, paired with a different DM2

• Part II New S’s can punish (bad) DM2’s

• Part IIIParticipants from Part I return to collect their

money.

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Current Study: Method• Part I

Trust game – each DM1 plays 5 games, paired with a different DM2

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Values:

1 / 39

3 / 37

6 / 34

9 / 31

12 / 28

Part I: Stimuli

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Method (part II)

• Players given $3 show-up payment

• Players given $7 to punish DM2’s in the game in which result was 1/39

• Two conditions Anonymous – elaborate envelope technique Non-anonymous: one experimenter knows how

much of $7 used to punish DM2

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Conditions

• Anonymous condition Measures punishment due to “tastes” Non-zero punishment implies some “taste” for

punishment.

• Non-anonymous condition Measures punishment due to “tastes” PLUS

punishment due to knowledge that punishment is observed.

Significantly greater punishment implies computation associated with others’ knowledge.

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Results, part I: Trust Games

Game Tree

Proportion of DM1’s

Moving Down (“trust”)

Proportion of DM2’s moving

Down(“trustworthy”)

1/39 1/7 0/1

3/37 1/7 0/1

6/34 1/7 0/1

9/31 4/7 1/4

12/28 6/7 3/6

N = 14. All remaining DM1 moves (22) were 10,10

One untrustworthy DM2 at 1/39

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Results, Part II: Punishment

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t(41) = 2.87, p < .01. means: anonymous = $.58, observed = $2.42.

Better test: Kolmogorov-Smirnov, J*= 1.37, p < .05

Subject changed Treatment

Subjects do the funniest things #1

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Results

• People in the observed condition punished (four times) more than those in the anonymous condition.

• Punishment in the anonymous condition was small, $0.58/$7.00.

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Experiment 3b

Like Experiment 3a, only PD with labeled extensive form game.

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Method (con’t)

• Participants received a game piece from Stage 1 in which DM1 had played C and DM2 played D

• Participants could pay $0-10 to deduct a tripled amount from that DM2.

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Method: IV’s3 conditions

Anonymous – elaborate envelope technique

Non-anonymous – one experimenter knows how much of $7 used to punish DM2

Participants –punishment decisions were revealed to both the experimenters and all other participants.

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Results: Stage 1

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Results: Stage 2

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ns*Subjects do the funniest things #2

Some subjects announced “Cooperate, Cooperate.”

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Research Agenda

Cross-cultural Replications

“Vectors” Strategy Method

in a PGG

Developmental

3PP to 4PO

Emotions

Consensus on Punishment

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Acknowledgements

Collaborators Alex ChavezPeter DeScioliDan HouserKeiko IshiiKevin McCabeErin O’BrienVernon SmithBart Wilson

Funding University of Pennsylvania

Research Foundation University of Pennsylvania

University Scholars MacArthur Foundation Japan Society for the Promotion of

Science

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Thank You

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NOTE: The issue is the design of the mechanism, the cues that people respond to.

• In a game in which people have a decision to cooperate (or not): (e.g., Burnham & Hare)

“The test treatment adds a pair of human eyes to the control environment…the evolutionary legacy hypothesis suggests that the test treatment, although actually still private with regard to other subjects, will be perceived as public …” (by the modular system in question)

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Mean Contribution PathGroups with Score = 3

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Mean Contribution PathGroups with Score = 4

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Results: Stage 2

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Fessler & Haley

Only the desktop background on participants’ computers varied

In the Eyespots condition, players used computers displaying two stylized eye-like shapes along with familiar desktop icons

In the Control condition, the word “CASSEL” was displayed across the same portion of the screen

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