Evolution, Trust, and Reciprocity

1. Evolution, Trust, and Reciprocity Robert Kurzban Trust in Groups from Cross-Societal Perspectives Hokkaido University, Sapporo, Japan, September 26-28, 2003

2. The Evolution of Cooperation: Constraints on Models • No model of the evolution of cooperation should predict promiscuous cooperation • Cue-based models (“green beard”) have profound difficulties, in particular the problem of mimics. • Plausible models of the evolution of cooperation must provide a basis by which exploitation is minimized, or otherwise eliminate the problem

3. The Evolution of Cooperation: Minimizing Exploitation • Exploitation can be minimized through: • Punishment (but note the 2nd order problem) • Boyd et al., 2003 • Withdrawing cooperation, changing groups… • Aktipis, in prep. • Indirect reciprocity models: limiting transactions? • Panchanathan & Boyd, in press (JTB) • Exploitation irrelevant in: • kin selection models, honest signaling models

4. The Evolution of Cooperation: Cognitive Mechanisms • A well-designed cognitive system should be fashioned to reap benefits of cooperation while minimizing exposure to exploitation: • Process cues that inform computations regarding cooperation (costs/benefits, features of others, etc) • Decide whether/how much to trust (endure cost with expectation of future benefit)

5. Models of the evolution of cooperation • Kin selection doesn’t generalize because • r falls off fast • independent assortment of alleles makes “similarity” irrelevant. • Reciprocal altruism doesn’t generalize easily because (e.g., Axelrod, 1986) • When common, “tolerant” reciprocators are replaced by defectors. • When rare, “intolerant” reciprocators find insufficient conspirators (unless there is a lot of assortment). • Punishment presents a 2nd order problem (e.g., Boyd & Richerson, 1988)

6. Models of the evolution of cooperation • Some models suggest that striving for uniqueness/irreplaceability will lead to mechanisms that cause individuals to deliver benefits to others. • Note: KS and RA are very different, and suggest that it’s not the number of interactants that matters, but the relevant selection pressures (pace Caporael et al.)

7. Reciprocity in Groups? • “Mean matching” strategies are too “tolerant” (Miller & Andreoni, 1991) because marginally less cooperative group members will out-replicate more cooperative group members. • But, empirically, contributions in public goods game correlate with expectations about others’ cooperation • Bornstein & Ben-Yossef, 1994 • Braver & Barnett, 1974 • Croson, 1998 • Dawes, McTavish, & Shaklee, 1977 • Komorita, Parks, & Hulbert, 1992 • Messick et al., 1983 • Yamagishi & Sato, 1986

8. Incremental Reciprocity • Strategies that cooperate as long as every other group member does so. • Intolerant • Enforces equity • Preliminary agent-based simulations show conditions broaden under which cooperation in groups can evolve. (Panchanathan et al., in prep., similar to Boyd & Richerson, 1988)

9. Assessing Cooperation: The Public Goods Game 1. Participants are given an allotment of Tokens 2. Task: Divide tokens between two accounts: • Private Account: Returns $1 for 1 Token • Public Account: Returns $2 per Token, divided equally among players (>2)

10. Creates a tension between individual and aggregate outcomes. Assessing Cooperation: The Public Goods Game Cooperation is indexed by contribution to Public Account.

11. Public Goods Game: Typical Results (Kurzban 1998)

12. Puzzling findings? In round 10, one’s best move is to contribute zero. So, by backward induction, one ought to contribute zero every time. Why are there any contributions at all? (Economics)

13. Communication If players are allowed to discuss the dilemma before the game, we observe much more cooperation. (Dawes, McTavish, and Shaklee, 1977)

14. Puzzling findings? Communication is cheap talk. Why does this have any effect on cooperation? (Economics) How does communication work its magic? (Psychology)

15. Hypotheses • People are willing to contribute to the extent others are, but have limited trust. • Communication influences beliefs about others’ contributions, especially when others are committed. • To be effective, commitment must not expose the player to “being free ridden.”

16. Distinguishing Accounts If cooperation is due to… Commitment should… Confusion or Altruism have no effect. Social identity have no effect. Reciprocity or increase cooperation. Fear of being free ridden

17. Testing IF people are willing to contribute to the extent others are, THEN • Observing others’ commitments will induce reciprocal cooperation • Incremental commitment minimizes the cost of committing by limiting exposure

18. Experiment 1 Claim Players in public goods games are trying to play a reciprocal strategy but this has been obscured by uncertainty about others’ contributions. Hypothesis Providing players the ability to publicly communicate their incrementalcommitment will elicit contributions.

19. Experiment 1: Implementing Incremental Commitment • Real time updating of contributions • Manipulation of commitment • INCREASE ONLY condition • Commitment • INCREASE/DECREASE condition • Cheap talk • Prediction: Higher contributions in INCREASE ONLY condition.

20. Experiment 1: Parameters • Five players per group • 50 Tokens per round • 1 Token = $.01 (MPCR = .33) • 10 Rounds (known) • 90 seconds per round • ALL players’ contributions displayed during round • Updated ~3 times/second.

23. Real Time Play

24. Experiment 1: Conclusions • Cooperation increase with incremental commitment mechanism. • Reciprocity? (R = .74) • Imperfect cooperation.

25. Experiment 2 • Claim. • Players are reciprocators, but concerned about being free ridden (by the least cooperative group member). • Claim 2. • Players know this about others.

26. Experiment 2: Design • Consider 2 independent variables • IO/ID, as in experiment 1 • Highest vs. Lowest current contributor

27. Experiment 2: Predictions

28. Experiment 2: Parameters • Five players per group • 50 Tokens per round • 1 Token = $.01 (MPCR = .33) • 10 Rounds (known) • 90 seconds per round

29. A look at the real time dynamics

30. 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

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

32. Experiment 2: Summary of findings • Lowest info IO can improve cooperation rates. • Individual differences seem to cause big changes in group dynamics. • There is a close (r =.9) relationship between low info and player i’s contribution

33. Conclusions • Aversion to being free ridden • Excessive trusting (in Exp 1) may inhibit reciprocal cooperation • Points to “inequity aversion” models • Role for individual differences in group dynamics

34. Experiment 3:Information Seeking • IF it is true that players care about the LEAST cooperative member of a group, • THEN when given the opportunity to learn one other group member’s contribution, • Participants can be predicted to choose the LOWEST current contributor…

35. Experiment 3: Method • “Circular” Public Goods game • Players make initial contribution • Players, in turn, can observe ONE contribution of other players, listed lowest to highest. • After observing this value, player may update their own contribution • Round ends with p = .04 each update

36. Results How often do players look at the: • Low? • 42% • Middle? • 25% • High? • 33% Take with grain of salt: Sessions run in last 2 weeks add noise to these findings.

37. Results (con’t) Does one’s contribution depend on the information one observes?

38. Results (con’t)

39. Experiment 3: Conclusions • There is a tendency to prefer observing the LOWEST current contributor. • The circular game, even under heavy information restriction conditions, affords cooperation • The circular game might be useful in understanding reciprocal play.

40. Experiment 4: Individual Differences • Circular public goods games, with parameters similar to previous experiment • Except: Players have access to only aggregate information of others’ contributions • This allows us to plot a “contribution profile” for each player (CP)

41. Individual Differences • 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

42. 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

43. 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…

49. Cross-Cultural Data • NOTE: • I did none (0) of this work

50. Public goods game results - Machiguenga Are the Machiguenga Homo economicus?