1. Incentives and Reputation

2. Darwin on reputation
Man‘s] motive to give aid […] no longer consists of a blind instinctive impulse, but is largely influenced by the praise and blame of his fellow men.

3. Indirect Reciprocity

4. Direct vs indirect reciprocity
‚to help‘ means: confer benefit b at own cost c

5. Binary model Each player has a binary reputation G good or B bad
Individuals meet randomly, as Donor and Recipient
Donor can give benefit b to Recipient at cost c
If Donor gives, Donor´s reputation G
if not, Donor‘s reputation B
Discrimination: give only to G-player (SCORING)
Undiscriminate stategies AllC and AllD

6. SCORING vs. AllC and AllD

7. The paradox of SCORING Why should one discriminate?
(it reduces chances of being helped later)
Discrimination is costly
AllC can invade

8. Assessment What is ‚bad‘? (rudimentary moral systems)
SCORING: bad is to refuse help
SUGDEN: bad is to refuse help to good player
KANDORI: bad is (in addition) to help bad player

9. Assessment rules First order: is help given or not?
Second order: is recipient good or bad?
Third order: is donor good or bad?
256 assessment rules (value systems)
(Ohtsuki, Iwasa; Brandt et al;2004)

10. Assessment rules First order: is help given or not?
Second order: is recipient good or bad?
Third order: is donor good or bad?
Only eight strategies lead to cooperation
and cannot be invaded by other action rules,
e.g. by AllC or AllD
(Ohtsuki, Iwasa 2004)

11. Assessment What is ‚bad‘? (rudimentary moral systems)
SCORING: bad is to refuse help
SUGDEN: bad is to refuse help to good player
KANDORI: bad is (in addition) to help bad player

12. The leading eight
L3 (SUGDEN) and L6 (KANDORI) are second order assessment rules, the others third order
(L1 considered in Panchanathan-Boyd and Leimar-Hammerstein)

13. SUGDEN (or KANDORI) vs. AllC and AllD

14. The competition of SUGDEN and KANDORI
Must assume private image (Brandt and Sigmund, Pacheco et al)
rather than public image (Ohtsuki and Iwasa, Panchanathan and Boyd)

15. AllC
AllD
Sugden
Stable fixed points
(Mixture of K and S)
Kandori

16. Incentives

17. Ultimatum game Two players can share 10 euros
Toss of coin decides who is proposer, who is responder
Proposer offers share to Responder
Responder accepts, or declines.

18. What does homo oeconomicus?
If each player maximises payoff:
Proposer offers minimal share,
Responder accepts

19. What do we do? In real life:
60 to 80 percent of all offers between 40 et 50 percent
Less than 5 percent of all offers below 20 percent

22. Economic anthropology
Henrich et al, Amer. Econ. Review 2001

24. Variants of Ultimatum Against computer Against five responders
Against five proposers

25. Ultimatum for mathematicians
strategy (p,q)
p size of offer, if Proposer
q aspiration level, if Responder
(percentage of total)

26. Mini-Ultimatum Only two possible offers High offer H (40 %)
Low offer L (20 %)

27. Mini-Ultimatum

28. Asymmetric Games

29. Conditional Strategies

30. Conditional Strategies

31. Conditional Strategies

32. Conditional Strategies

33. Conditional Strategies

34. Mini-Ultimatum Population of players Types (H,H) (social)
(L,L) (asocial)
(H,L) (mild)
(L,H) (paradoxical)
Players copy whoever is successful

35. Mini-Ultimatum

36. Mini-Ultimatum

37. Reputation and temptation
Suppose that with a small probability
Players have information about type of co-player
(reputation)
and makes reduced offer L if co-player has low aspiration level
(temptation)

38. Mini-Ultimatum with reputation and temptation

39. Mini-Ultimatum with reputation-temptation
Bistability
Attractors HH (social) and LL (asocial)

40. Mini-Ultimatum with reputation-temptation
Bistability
Attractors HH (social) and LL (asocial)
Social stronger if H<1/2

41. Bifurcation

42. Back to full ultimatum Evolution leads to minimal offers
(as with rational players)
With reputation-temptation to values between 40 and 50 percent

43. Individual-based simulations

44. Individual-based simulations

45. An economic experiment
Ultimatum with or without reputation
(Fehr and Fischbacher, Nature 2004)

46. What if someone is watching?
Experiments by Haley, Fessler
By Bateson et al (honesty box)

47. Trust Game
Investor can send amount c to Trustee, knowing it will be multiplied by factor r>1 on arrival
Trustee, on receiving b=rc, can send
part of it back to Investor

48. Mini-Trust

49. Mini-Trust

50. Mini-Trust with Reputation

51. Incentives for cooperation
First, play a donation game (or a more complex game, involving cooperation), then punish the defector or reward the cooperator (same structure as ultimatum or trust)

52. PD with Reward

53. PD with Reward with reputation

54. PD with Reward with reputation

57. Payoff

58. Results:

59. low information
high information