2. Models of Cooperation in Social Systems

3. Example 1: Prisoner’s Dilemma Prisoner’s Dilemma: Player 2 Player 1 cooperate defect cooperatedefect 3, 30, 5 5, 01, 1

4. Dilemma: It’s better for any particular individual to defect. But in the long run, everyone is better off if everyone cooperates. So what are the conditions under which cooperation can be sustained? –This has been investigated by many people using agent- based models: Iterated games Norms and metanorms Spatial distribution of players

5. “Bob Axelrod’s work on the evolution of cooperation has done a great deal to rebuild the thinking world’s faith in the power of cooperation.”

9. Iterated games Axelrod’s tournament Axelrod’s genetic algorithm How to represent strategies?

10. Example 2: Spatial games and chaos M. Nowak and R. May (1992), “Evolutionary games and spatial chaos”, Nature. Experimented with “spatial” Prisoner’s Dilemma to investigate how cooperation can be sustained.

11. Spatial Prisoner’s dilemma model Players arrayed on a two-dimensional lattice, one player per site. Each player either always cooperates or always defects. Players have no memory. Each player plays with eight nearest neighbors. Score is sum of payoffs. Each site is then occupied by highest scoring player in its neighborhood. Updates are done synchronously.

12. NetLogo PDCA What are assumptions?

13. Interpretation Motivation for this work is “primarily biological”. “We believe that deterministically generated spatial structure within populations may often be crucial for the evolution of cooperation, whether it be among molecules, cells, or organisms.“ "That territoriality favours cooperation...is likely to remain valid for real-life communities“ (Karl Sigmund, Nature, 1992, in commentary on Nowak and May paper)

14. Some prospects for minimal models:

15. –Often no other way to make progress! –“All models are wrong. Some are useful.” –All the ones I talked about here were useful and led to new insights, new ways of thinking about complex systems, better models, and better understanding of how to build useful models.

16. Some perils for minimal models:

17. –Hidden unrealistic assumptions –Sensitivity to parameters –Unreplicability due to missing information in publications –Misinterpretation and over-reliance on results –Lack of theoretical insight

18. Norms “Norm”: Behavior prescribed by social setting. Individuals are punished for not acting according to norm. Examples: –Norm against cutting people off in traffic. –Norm for covering your nose and mouth when you sneeze –Norm for governments not to raise import taxes without sufficient cause

19. Axelrod’s (1986) Norms model Norms game: N-player game. 1.Individual i decides whether to cooperate (C) or Defect (D). If D: –Individual gets "temptation" payoff T=3 –Inflicts on the N  1 others a "hurt" payoff (H=-1). If C, no payoff to self or others.

20. 2.If D, there is a (randomly chosen) probability S of being seen by each of the other agents. Each other agent decides whether to punish the defector or not. Punishers incur “enforcement" cost E=  2 every time they punish a defector with punishment P=  9.

21. Strategy of an agent given by: –Boldness (propensity to defect) –Vengefulness (propensity to punish defectors). B, V  [0,1]. S is the probability that a defector will be observed defecting. An agent defects if B > S (probability of being observed). An agent punishes observed defectors with probability V.

22. A population of 20 agents play the Norms game repeatedly. One round: Every agent has exactly one opportunity to defect, and also the opportunity to observe (and possibly punish) any defection by other players that has taken place in the round. One generation: Four rounds At beginning of each generation, agents’ payoffs set to zero. At end of generation, new generation is created via selection based on payoff and mutation (i.e., genetic algorithm).

23. Axelrod’s results (Norms) Axelrod performed five independent runs of 100 generations each. Intuitive expectation for results: Vengefulness will evolve to counter boldness.

24. NetLogo Norms Model

25. Actual results (Norms model) “This raises the question of just what it takes to get a norm established.” -- Axelrod, 1986 What might help get norm established? Experiment

26. Metanorms model On each round, if there is defection and non- punishment when it is seen, each agent gets opportunity to meta-punish non-punishers (excluding self and defector). Meta-punishment payoff to non-punisher:  9 Meta-enforcement payoff to enforcer:  2 every time it meta-punishes

27. Results (Metanorms model) (From Axelrod, 1986)

28. Results (Metanorms model) “[T]he metanorms game can prevent defections if the initial conditions are favorable enough.“ (i.e., sufficient level of vengefulness) (From Axelrod, 1986)

29. Conclusion: “Metanorms can promote and sustain cooperation in a population.”

30. Re-implementation by Galan & Izquierdo (2005) Galan & Izquierdo showed: 1.Long term behavior of model significantly different from short term. 2.Behavior dependent on specific values of parameters (e.g., payoff values, mutation rate) 3.Behavior dependent on details of genetic algorithm (e.g., selection method)

31. Re-Implementation results (From Galan & Izquierdo, 2005) Norms model: Norm collapse occurs in long-term on all runs. (Norm collapse: average B  6/7, average V  1/7 Norm establishment: average B  2/7, average V  5/7) Proportion of runs out of 1,000

32. (From Galan & Izquierdo, 2005) Metanorms model: Norm is established early, but collapses in long term on most runs.

33. (From Galan & Izquierdo, 2005)

35. Conclusions from Galan & Izquierdo Not intended to be a specific critique of Axelrod’s work. Analysis in G&I paper required a huge amount of computational power, which was not available in 1986. Instead, re-implementation exercise shows that we need to: –Replicate models (Axelrod: "Replication is one of the hallmarks of cumulative science.“) –Explore parameter space

36. Conclusions from Galan & Izquierdo –Run stochastic simulations for long times, many replications –Complement simulation with analytical work whenever possible –Be aware of the scope of our computer models and of the conclusions obtained with them.

37. El Farol Problem

38. Brian Arthur’s “El Farol Problem” http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Brian Arthur

39. Self-Organization and Cooperation in Economics

40. Traditional economics Assumptions:

41. Self-Organization and Cooperation in Economics Traditional economics Assumptions: Perfectly rational self-interested agents

42. Self-Organization and Cooperation in Economics Traditional economics Assumptions: Perfectly rational self-interested agents Each has complete knowledge of others’ strategies

43. Self-Organization and Cooperation in Economics Traditional economics Assumptions: Perfectly rational self-interested agents Each has complete knowledge of others’ strategies Each can do deductive reasoning

44. Self-Organization and Cooperation in Economics Traditional economics Assumptions: Perfectly rational self-interested agents Each has complete knowledge of others’ strategies Each can do deductive reasoning Result: “Efficiency”: Best possible situation for all

45. Self-Organization and Cooperation in Economics Traditional economics Assumptions: Perfectly rational self-interested agents Each has complete knowledge of others’ strategies Each can do deductive reasoning Result: “Efficiency”: Best possible situation for all Adam Smith’s “Invisible Hand”

46. "Every individual necessarily labours to render the annual revenue of the society as great as he can. He generally neither intends to promote the public interest, nor knows how much he is promoting it... He intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end which was no part of his intention. Nor is it always the worse for society that it was no part of his intention. By pursuing his own interest he frequently promotes that of the society more effectually than when he really intends to promote it. I have never known much good done by those who affected to trade for the public good.” — Adam Smith. An Inquiry into the Nature and Causes of the Wealth of Nations, 1776. Adam Smith, 1723−1790

47. “Complexity” economics:

48. Self-interested agents with bounded rationality

49. “Complexity” economics: Self-interested agents with bounded rationality Limited knowledge of others’ strategies

50. “Complexity” economics: Self-interested agents with bounded rationality Limited knowledge of others’ strategies Each does primarily inductive reasoning

51. “Complexity” economics: Self-interested agents with bounded rationality Limited knowledge of others’ strategies Each does primarily inductive reasoning Agents adapt over time to ever-changing environment

52. “Complexity” economics: Self-interested agents with bounded rationality Limited knowledge of others’ strategies Each does primarily inductive reasoning Agents adapt over time to ever-changing environment Traditional economics: Can make predictions with analytic (mathematical) models, assuming “equilibrium” dynamics

53. “Complexity” economics: Self-interested agents with bounded rationality Limited knowledge of others’ strategies Each does primarily inductive reasoning Agents adapt over time to ever-changing environment Traditional economics: Can make predictions with analytic (mathematical) models, assuming “equilibrium” dynamics Complexity economics: Analytic models often not possible; equilibria are never reached; often need agent-based models with ability to adapt

54. Brian Arthur’s “El Farol Problem” http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Brian Arthur

55. Brian Arthur’s “El Farol Problem” http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Brian Arthur

56. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0

57. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights

58. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably

59. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go.

60. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go. No prior communication among people.

61. El Farol Model Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go. No prior communication among people. Only information each person has is the number of people that attended on each of the last M Thursdays (everyone uses same value of M). http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0

62. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go. No prior communication among people. Only information each person has is the number of people that attended on each of the last M Thursdays (everyone uses same value of M). E.g., if M = 3, you might have this information: three weeks ago: 35, two weeks ago: 76, one week ago: 20

63. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go. No prior communication among people. Only information each person has is the number of people that attended on each of the last M Thursdays (everyone uses same value of M). E.g., if M = 3, you might have this information: three weeks ago: 35, two weeks ago: 76, one week ago: 20 Decision for each person: Should I go this Thursday?

64. El Farol Model http://picklemedia1.scrippsnetworks.com/pickle_media1/media/Great_American_Cou ntry/120921/Photo_Video_4447707903273446703788_medium.jpg?0 Live Irish music on Thursday nights 60 people will fit comfortably 100 people want to go, but only if 60 or less go. No prior communication among people. Only information each person has is the number of people that attended on each of the last M Thursdays (everyone uses same value of M). E.g., if M = 3, you might have this information: three weeks ago: 35, two weeks ago: 76, one week ago: 20 Decision for each person: Should I go this Thursday? How can all the people “cooperate” without communicating and without rational deductive reasoning?

65. How a person decides whether to go or not (NetLogo Model)

66. Each person has some number N of strategies, each of which use the information from past Thursdays to predict attendance this Thursday. (Each person has a possibly different set of strategies.)

67. How a person decides whether to go or not (NetLogo Model) Each person has some number N of strategies, each of which use the information from past Thursdays to predict attendance this Thursday. (Each person has a possibly different set of strategies.) For example, if N = 3, your strategies might be: –Strategy 1: Predict attendance will be the same as last week –Strategy 2: Predict attendance will be 100 – last week –Strategy 3: Predict attendance will be 0.2 * last week + 0.1 * two weeks ago

68. How a person decides whether to go or not (NetLogo Model) Each person has some number N of strategies, each of which use the information from past Thursdays to predict attendance this Thursday. (Each person has a possibly different set of strategies.) For example, if N = 3, your strategies might be: –Strategy 1: Predict attendance will be the same as last week –Strategy 2: Predict attendance will be 100 – last week –Strategy 3: Predict attendance will be 0.2 * last week + 0.1 * two weeks ago One of these strategies is the “current best”. This is the one that did the best job of predicting attendance on the previous Thursday.

69. How a person decides whether to go or not (NetLogo Model) Each person has some number N of strategies, each of which use the information from past Thursdays to predict attendance this Thursday. (Each person has a possibly different set of strategies.) For example, if N = 3, your strategies might be: –Strategy 1: Predict attendance will be the same as last week –Strategy 2: Predict attendance will be 100 – last week –Strategy 3: Predict attendance will be 0.2 * last week + 0.1 * two weeks ago One of these strategies is the “current best”. This is the one that did the best job of predicting attendance on the previous Thursday. You use your “current best” strategy to predict attendance for this Thursday. If it predicts more than 60 people will show up (60 = “overcrowding threshold”), you decide not to go; otherwise you go.

70. How a person decides whether to go or not (NetLogo Model) Each person has some number N of strategies, each of which use the information from past Thursdays to predict attendance this Thursday. (Each person has a possibly different set of strategies.) For example, if N = 3, your strategies might be: –Strategy 1: Predict attendance will be the same as last week –Strategy 2: Predict attendance will be 100 – last week –Strategy 3: Predict attendance will be 0.2 * last week + 0.1 * two weeks ago One of these strategies is the “current best”. This is the one that did the best job of predicting attendance on the previous Thursday. You use your “current best” strategy to predict attendance for this Thursday. If it predicts more than 60 people will show up (60 = “overcrowding threshold”), you decide not to go; otherwise you go. All other people do the same thing simultaneously and independently, with no communication.

71. The next video gives the “nitty gritty details” of this model; if you don’t want that much detail, or don’t feel comfortable with math, you can skip it and go to the video after the next one.

72. The nitty gritty details (optional)

73. Let N be the number of strategies each person has and let M be the number of weeks for which the attendance number is known. Let t be the current time (week). The previous weeks are thus t −1, t − 2, etc. Let A(t) be the attendance at time t.

74. The nitty gritty details (optional) Let N be the number of strategies each person has and let M be the number of weeks for which the attendance number is known. Let t be the current time (week). The previous weeks are thus t −1, t − 2, etc. Let A(t) be the attendance at time t. Each strategy has the following form:

75. The nitty gritty details (optional) Let N be the number of strategies each person has and let M be the number of weeks for which the attendance number is known. Let t be the current time (week). The previous weeks are thus t −1, t − 2, etc. Let A(t) be the attendance at time t. Each strategy has the following form: Each person has N such strategies (where the set of strategies can be different from person to person). One of these strategies is determined to be the “current best”, and is denoted Prediction*(t). Each person makes a decision as follows: If Prediction*(t) > overcrowding-threshold, don’t go; otherwise, go.

76. The nitty gritty details, continued

77. Initialization: Each person’s N strategies are initialized with random

78. The nitty gritty details, continued Initialization: Each person’s N strategies are initialized with random Initial history: The attendance history (previous M time steps) is initialized at random, with values between 0 and 99. (This is so predictions can be made on the first M time steps.)

79. The nitty gritty details, continued Initialization: Each person’s N strategies are initialized with random Initial history: The attendance history (previous M time steps) is initialized at random, with values between 0 and 99. (This is so predictions can be made on the first M time steps.) Best current strategy: At each time step t, after each person makes their decision and learns the current attendance A(t), each person determines their best current strategy as the one that would have been the best predictor if it had been used on this round. This is the strategy that will be used by the person on the next round.

80. “El Farol” Model

81. Assumes “bounded rationality”, limited knowledge

82. “El Farol” Model Assumes “bounded rationality”, limited knowledge Includes adaptation (inductive learning from experience)

83. “El Farol” Model Assumes “bounded rationality”, limited knowledge Includes adaptation (inductive learning from experience) Question: Does self-organized “efficiency” (best situation for all) emerge under these conditions?

84. notes Show NetLogo model

85. Conclusion The El Farol model demonstrates that self-organized cooperation and “efficiency” are possible without perfect rationality, complete knowledge, and deductive reasoning!