Update: Reciprocity in Groups and Third Party Punishment

1. Update: Reciprocity in Groups and Third Party Punishment Robert Kurzban University of Pennsylvania Hokkaido University 8 Nov 2006

2. Roadmap • Public Goods Work • Theories in the spotlight • Third Party Punishment • Directions

3. Remember this? Real Time Public Goods Game

4. 50 Low Info & Increase/Decrease Low Info & Increase Only 40 High Info & Increase/Decrease High Info & Increase Only 30 Average Contribution (Tokens) 20 10 0 1 2 3 4 5 6 7 8 9 10 Round

5. Well, it should look familiar…

6. Replication in Japan:Dynamics U.S. Data Japan Data (Ishii & Kurzban)

7. Contributions by Round in the Increase Only/Low Information Condition

8. 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?

9. Circular Game: Method(Kurzban & Houser, PNAS, 2005) • “Circular” Public Goods game • Players make initial contribution • Players, in turn, observe aggregate contribution of other players • After observing this value, player may update their own contribution • Round ends with p = .04 each update • This allows us to plot a “contribution profile” for each player (CP)

10. 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

11. Individual Differences • Free Rider = CP everywhere below 25 (1/2) • 20% of sample (N = 84) • Cooperator = CP everywhere above 25 • 13% • Recriprocator = positive slope, and CP is both above and below the 50% line. • 63% • Small percentage unclassifiable

12. 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…

15. Types fit reasonably into a 3-part system • Payoffs did not vary as a function of type. • Suggest individual differences in strategies?

16. 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)

17. Information Seeking Hypotheses: • IF players know others respond to own contribution, costly information should decrease contributions. • IF players have reciprocal (type) preferences, they will have systematic preferences for information and will pay to observe it. • Type (reciprocator, free rider) will predict information-seeking preferences

18. Results

19. Results: Information-Seeking

20. 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.)

21. Results: Information-SeekingIndividual Differences (non-randomly chosen) examples of typing regression for 3 s’s,

22. Results: Information-SeekingIndividual Differences More reciprocal players like median information

23. Results: Information-SeekingIndividual Differences Free Riders like high information

24. Results: Information-SeekingIndividual Differences

25. 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.

26. 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 contrast • Free Riding types tend to look at the high information. • Altruistic types don’t have clear preferences

27. Part II: Third Party Punishment

28. 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.

29. Third Party Punishment ≠ Second Party Punishment • If A cheats B, B has a preference for inflicting costs on A. • Substantial evidence from field and lab • Trivers (1971) theory of reciprocal altruism provides one possible explanation for this phenomenon.

30. 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.

31. 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).

32. 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.)

33. 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.

34. 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.

35. 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 III • Participants from Part I return to collect their money.

36. Current Study: Method • Part I • Trust game – each DM1 plays 5 games, paired with a different DM2

37. Part I: Stimuli Values: 1 / 39 3 / 37 6 / 34 9 / 31 12 / 28

38. 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

39. 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.

40. Results, part I: Trust Games One untrustworthy DM2 at 1/39 N = 14. All remaining DM1 moves (22) were 10,10

41. Results, Part II: Punishment Subjects do the funniest things #1 Subject changed Treatment t(41) = 2.87, p < .01. means: anonymous = $.58, observed = $2.42. Better test: Kolmogorov-Smirnov, J*= 1.37, p < .05

42. 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.

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

44. 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.

45. Method: IV’s 3 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.

46. Results: Stage 1

47. Results: Stage 2 Subjects do the funniest things #2 Some subjects announced “Cooperate, Cooperate.” * ns

48. Research Agenda 3PP to 4PO Emotions Cross-cultural Replications Developmental “Vectors” Strategy Method in a PGG Consensus on Punishment

49. Collaborators Alex Chavez Peter DeScioli Dan Houser Keiko Ishii Kevin McCabe Erin O’Brien Vernon Smith Bart Wilson Funding University of Pennsylvania Research Foundation University of Pennsylvania University Scholars MacArthur Foundation Japan Society for the Promotion of Science Acknowledgements

50. Thank You