Agent-based modeling of cooperation in collective action situations & diffusion of information

1. Agent-based modeling of cooperation in collective action situations & diffusion of information Marco Janssen School of Human Evolution and Social Change & Department of Computer Science and Engineering Arizona State University

2. Games and Gossip Marco Janssen School of Human Evolution and Social Change & Department of Computer Science and Engineering Arizona State University

3. Games and Gossip • Games: Strategic interactions • Gossip: Diffusion of information

4. Agent-based modeling is a way to study the interactions of large numbers of agents and the macro-level consequences of these interactions. If <cond> then <action1> else <action2> ….. ….. Inanimate agents …. Observer Animate agents Data Organizations of agents Artificial world

6. Content • Games • Why do we cooperate with strangers? • Changing the rules of the game • Gossip • Diffusion of consumer products

7. Why do strangers cooperate?

8. The problem of cooperation in commons dilemmas • Dilemma between individual and group interests • Group interest: cooperation • Individual interest: free riding on efforts of others • Public goods and common pool resources • Expectation with rational selfish agents • No public goods • Overharvesting of common pool resources • Many empirical examples of self-governance

9. The puzzle of eBay • Net revenues $2.2 billion for 2003. • In eBay strangers cooperate in non-repeated interactions of traditional dilemma of buyer and seller. • Reputation system is found to be theoretically problematic (aggregation, unlimited memory, entry problem) • Monitoring is incomplete • About 55% of transactions include feedback. • About 1% of this feedback is negative. • 90% of fraud on internet occurs in auction markets. • Puzzle: Why does eBay work?

10. eBay reputation system • Buyer and Seller can provide “Feedback”: • Ratings translated into points: positive = 1 point, neutral = 0 points, and negative = -1 point. Aggregate is the reputation score. • If reputation score reaches -4 the participant is removed from the system.

11. Simple model on reputation and trustworthiness • Agents play one-shot prisoner dilemma games. • Reputation scores evaluates past behavior of the actors. • Are reputation scores alone sufficient to derive cooperation? • Especially, when not everybody provides feedback. • They may refuse to play and decide to cooperate or not, based on expected trustworthiness.

12. Monetary payoff table of the Prisoner’s Dilemma with the option to withdraw from the game.

13. Experiments have shown that the subjective evaluation of monetary payoffs lead to a different order of preferred situations than monetary rewards. • Thus, utility and monetary rewards may differ.

14. Utility table of the Prisoner’s Dilemma with the option to withdraw from the game. α and β are individual characteristics of agents

15. How to estimate trustiness? • The probability to trust the opponent: • Where • Adjusting weightings of symbols: Symbol i Feedback (0 or 1) Learning rate

16. When to Cooperate? • Estimate expected utilities: Make discrete choice decision:

17. Population dynamics • Agent remove from the system if they do not derive positive income, or when reputation score falls to -4. • Agent is replaced with a random new one. • Agents provide feedback with a certain probability.

18. Role of feedback(history 100 interactions)

19. Role of symbols

20. Finding • Reputation systems with voluntary feedback might not be sufficient to foster cooperation. • Observed high levels of cooperation might be explained by the use of multiple other sources of indicators of trustworthiness.

21. Changing the rules of the game • Earlier work has focused on behavior of individuals and groups given a particular rule set, and what happens when this rule set changes. • I am interested in how people change the rule of the game.

23. Questions on rule change • How do individuals and groups know the potential effect of a rule change? • What affect that persons invest in a rule change? • What is the role of experience in rule crafting?

24. Using different type of methods Dynamics of Rules project: http://www.public.asu.edu/~majansse/dor/nsfhsd.htm

25. Laboratory Experiment • Renewable resource • Collection of green tokens • 5 subjects: self is yellow dot; and other subjects are blue dots • move yellow dot around by arrow keys

26. For each treatment, a practice round and then 3 rounds of about 5 minutes. Treatments: no rules – vote for rule (cost 50 tokens) – no rule (22 groups, 2 groups discarded) No rules for three rounds (4 groups, need more done later) Rule imposed in 2nd round (9 groups) Totally 174 different subjects used (one person did an experiment twice) In communication experiment we asked 30 persons to do it a second time. Design

27. Information collected • Everytime a subject collect a token, the time, and place are recorded. • Every 2 seconds the location of all tokens is recorded. • When subjects break the rule and/or are caught (place and time) • Questionaire at end of experiment.

28. What happens?

29. Round 1

30. Effect of experience Small but significant high collection of tokens and length of time

31. Round 2

32. How much tokens collected? (including penalties)

33. How fast do they destroy the resource?

34. Average collected earnings of individuals

35. Where did they break the rules?

36. Individual collected tokens in round 2 and 3 Elected Not elected No rule Imposed

37. CommunicationExperiment for designing future experiments • Treatment 1: All three groups could communicate within one big group • Treatment 2: The three groups split up and could talk among themselves. • Experienced subjects!!

38. Global CommunicationAgreed Rule: 20 seconds wait, 10 seconds “go for it”

39. Group talk:Areas of harvest

40. Next steps • Analysis of data • Development of agent-based models • New experimental designs

41. Fun project • Why do recreational games have the rules they have? • Co-evolution of agents playing games and changing the rules such that certain objectives are derived (excitement of playing?). Rules of tournaments Evaluation Agents Play Games (Tournament) Adjustment of rules

42. Diffusion dynamics in various types of social networks with heterogeneous consumers with Alessio Delre & Wander Jager (University of Groningen, the Netherlands) • How do network structure affect diffusion of consumer products? • How do behavioral rules of consumer behavior affect diffusion processes? (Most models assume diffusion is a kind of epidemic spreading of a disease, we use cognitive theories)

43. Small-World Networks Regular network (randomness = 0) Random network (randomness = 1) Small-World network (0 < randomness < 1) Watts, D.J. and Strogatz S. H. (1998). Collective Dynamics of “Small-World” Networks, Nature, 393, 440-442.

44. Our innovation diffusion model Individual part: Social part: where Ai is the number of adopters in set of neighbors of agent i hi is a personal threshold which determines when agent i adopts. P.S. Notice that we included mass media effects. Independently on word-of mouth process, at each time step, agents adopt with probability e.

45. Results -the speed of diffusion- ßi =1; hi=0.3; D(t) = cumulative number of adopters; f(t) = adopters at time t

46. Results -the speed of diffusion in heterogeneous populations- Continuous line: <hi>=0.4; Dashed line: <hi>=0.3; Pointed line:<hi>=0.2.

47. Application: hits and flops of movies • What makes a movie a hit? Spread of information? • Most movies have their most successful week in the first week. • Only in rare cases there is an increase after the first week. • Same phenomena with best seller books (Harry Potter). • Expectations are formed by media campaign before the product is available. • Survey data from movie-goers (challenging fieldwork!!)