update: reciprocity in groups and third party punishment
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Update: Reciprocity in Groups and Third Party Punishment. Robert Kurzban. University of Pennsylvania. Hokkaido University 8 Nov 2006. Roadmap. Public Goods Work Theories in the spotlight Third Party Punishment Directions. Remember this? Real Time Public Goods Game. 50. - PowerPoint PPT PresentationTRANSCRIPT
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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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Round
Ave
rage
Con
trib
utio
n (T
oken
s)
Low Info & Increase/Decrease
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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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85
Elapsed time (Seconds)C
ontr
ibut
ion
(Tok
ens)
U.S. Data
Japan Data(Ishii & Kurzban)
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Elapsed time (Seconds)
Con
trib
utio
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oken
s)
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2530
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1 2 3 4 5 6 7 8 9 10
Round
Ave
trag
e C
ontr
ibut
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(in
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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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2001 4 7 10 13 16 19 22 25 28 31 34
Round
Con
trib
uti
on
F irst Seven Games Hold-out sample
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Mean Contribution PathGroups with Score = 5
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2001 4 7 10 13 16 19 22 25 28 31
Round
Con
trib
uti
on
F irst seven games Hold-out sample
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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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0 5 10 15 20Game
Con
trib
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onFree Information
Costly Information
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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 $7 used to punish DM2
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Conditions
• Anonymous condition Measures punishment due to “tastes” Non-zero punishment implies some “taste” for
punishment.
• Non-anonymous condition Measures punishment due to “tastes” PLUS
punishment due to knowledge that punishment is observed.
Significantly greater punishment implies computation associated with others’ knowledge.
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Results, part I: Trust Games
Game Tree
Proportion of DM1’s
Moving Down (“trust”)
Proportion of DM2’s moving
Down(“trustworthy”)
1/39 1/7 0/1
3/37 1/7 0/1
6/34 1/7 0/1
9/31 4/7 1/4
12/28 6/7 3/6
N = 14. All remaining DM1 moves (22) were 10,10
One untrustworthy DM2 at 1/39
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Results, Part II: Punishment
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$'s used to Punish
Pro
po
rtio
n o
f S
's Anonymous
Observed
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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Avg
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ishm
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ANON EXPERIMENTER PARTICIPANTS
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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2001 4 7 10 13 16 19 22 25 28 31 34
Round
Con
trib
uti
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F irst seven games Hold-out sample
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Mean Contribution PathGroups with Score = 4
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50
100
150
2001 4 7 10 13 16 19 22 25 28 31
Round
Con
trib
uti
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F irst seven games Hold-out sample
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Results: Stage 2
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0 1 2 3 4 5 6 7 8 9 10Dollars Spent on Punishment
Num
ber o
f Par
ticip
ants
ANON EXPERIMENTER PARTICIPANTS
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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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