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Agent Based Models in Social Science James Fowler University of California, San Diego.

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Presentation on theme: "Agent Based Models in Social Science James Fowler University of California, San Diego."— Presentation transcript:

1 Agent Based Models in Social Science James Fowler University of California, San Diego

2 The Big Picture: Collective Action Cooperation Alternative Models of Participation Social Networks

3 Cooperation Evolutionary models Altruistic Punishment and the Origin of Cooperation PNAS 2005 Second Order Defection Problem Solved? Nature 2005 On the Origin of Prospect Theory JOP, forthcoming The Evolution of Overconfidence Experiments Egalitarian Motive and Altruistic Punishment Nature 2005 Egalitarian Punishment in Humans Nature 2007 The Role of Egalitarian Motives in Altruistic Punishment The Neural Basis of Egalitarian Behavior

4 Alternative Models of Political Participation Computational Models of Adaptive Voters and Legislators Parties, Mandates, and Voters: How Elections Shape the Future 2007 Policy-Motivated Parties in Dynamic Political Competition JTP 2007 Habitual Voting and Behavioral Turnout JOP 2006 A Tournament of Party Decision Rules Empirical Models of Legislator Behavior Dynamic Responsiveness in the U.S. Senate AJPS 2005 Elections and Markets: The Effect of Partisan Orientation, Policy Risk, and Mandates on the Economy JOP 2006 Parties and Agenda-Setting in the Senate,

5 Alternative Models of Political Participation Experiments Altruism and Turnout JOP 2006 Patience as a Political Virtue: Delayed Gratification and Turnout Political Behavior 2006 Beyond the Self: Social Identity, Altruism, and Political Participation JOP 2007 Social Preferences and Political Participation When It's Not All About Me: Altruism, Participation, and Political Context Partisans and Punishment in Public Goods Games Genetics The Genetic Basis of Political Participation Southern California Twin Register at the University of Southern California: II Twin Research and Human Genetics 2006

6 Political Social Networks Voters Dynamic Parties and Social Turnout: an Agent-Based Model AJS 2005 Turnout in a Small World Social Logic of Politics 2005 Legislators Legislative Cosponsorship Networks in the U.S. House and Senate Social Networks 2006 Connecting the Congress: A Study of Cosponsorship Networks Political Analysis 2006 Community Structure in Congressional Networks Legislative Success in a Small World: Social Network Analysis and the Dynamics of Congressional Legislation Co-Sponsorship Networks of Minority-Supported Legislation in the House The Social Basis of Legislative Organization

7 Political Social Networks Court Precedents The Authority of Supreme Court Precedent Social Networks, forthcoming Network Analysis and the Law: Measuring the Legal Importance of Supreme Court Precedents Political Analysis, forthcoming

8 Other Social Networks Political Science PhDs Social Networks in Political Science: Hiring and Placement of PhDs, PS 2007 Academic Citations Does Self Citation Pay? Scientometrics 2007 Health Study Participants The Spread of Obesity in a Large Social Network Over 32 Years New England Journal of Medicine 2007 Friends and Participation Genetic Basis of Social Networks

9 What is an Agent Based Model? Computer simulation of the global consequences of local interactions of members of a population Types of agents plants and animals in ecosystems (Boids) vehicles in traffic people in crowds Political actors

10 What is an Agent Based Model? “Boids” are simulations of bird flocking behavior (Reynolds 1987) Three rules of individual behavior Separation avoid crowding other birds Alignment point towards the average heading of other birds Cohesion move toward the center of the flock Result is a very realistic portrayal of group motion in flocks of birds, schools of fish, etc.

11 What is an Agent Based Model? Comparison with formal models Same mathematical abstraction of a given problem, but uses simulation rather than mathematics to “solve” model and derive comparative statics Comparison with statistical models Same attempt to analyze data, but uses simulation data rather than real data

12 Advantages of Agent Based Modeling Formal Assumptions laid bare Flexible Cognitively: agents can be “rational” or “adaptive” Tractable Easier to cope with complexity (nonlinearities, discontinuities, heterogeneity) Generative Helps create new hypotheses Social Science from the Bottom Up “If you didn’t grow it you didn’t show it.”

13 Disadvantages of Agent Based Modeling Models too simple Could be solved in closed-form (Axelrod 1984) Closed-form solution always preferable Models too complicated Not possible to assess causality (Cederman 1997) What use is an existence proof? Coding mistakes Many more lines of code than lines in typical formal proof Data analysis What part of the parameter space to search?

14 My Approach to Agent Based Modeling Write down model Solve as much as possible in closed-form Justify simulation with mathematical description of the complexity problem Use real world to “tune” model Make predictions Check predictions against reality Do comparative statics near real world parameters to assess causality

15 Tournament Overview A dynamic spatial account of multi-party multi-dimensional political competition is substantively plausible generates a complex system that is analytically intractable amenable to systematic and rigorous computational investigation using agent based models (ABMs) Existing ABMs use a fixed set of predefined strategies, typically in which all agents deploy the same rule. There as been little investigation of potential rules, or the performance of different rules in competition with each other The Axelrodian computer tournament is a good methodology for doing this … … while also offering great theoretical potential to be expanded into a more comprehensive evolutionary system

16 Tournament ABM test-bed We advertised a computer simulation tournament with a $1000 prize for the action selection rule winning most votes, in competition with all other submitted rules over the very long run. Tournament test-bed (in R) adapted from Laver (APSR 2005) The four rules investigated by Laver were declared pre-entered but ineligible to win: Sticker, Aggregator, Hunter and Predator Submitted rules constrained to use only published information about party positions and support levels during each past period and knowledge of own supporters’ mean/median location

17 Departures from Laver (2005) Distinction between inter-election (19/20) and election (1/20) periods Forced births (1/election) at random locations, as opposed to endogenous births at fertile locations, à la Laver and Schilperoord De facto survival threshold (<10%, 2 consecutive elections) Rule designers’ knowledge of pre-entered rules Diverse and indeterminate rule set to be competed against

18 Tournament structure There were 25 valid submissions – after several R&Rs for rule violations, elimination of a pair of identical submissions and of one in R code that would not run and we could not fix – making 29 distinctive rules in all. Five runs/rule (in which the rule in question was the first-born) 200,000 periods (10,000 elections)/run (after 20,000 period burn in) Thus 145 runs, 29,000,000 periods and 1,450,000 elections in all Brooks-Gelman tests used to infer convergence, in the sense that results from all chains are statistically indistinguishable. There was a completely unambiguous winner – not one of the pre-entered rules However only 9/25 submissions beat pre-announced Sticker (i.e. select random location and never move)

19 Tournament algorithm portfolio Center-seeking rules: use the vote-weighted centroid or median Previous work suggests these are unlikely to succeed, a problem exacerbated in a rule set with other species of the same rule Tweaks of pre-entered rules: eg with “stay-alive” or “secret handshake” mechanisms (see below) Sticker is the baseline “static” rule for any dynamic rule to beat Hunter was the previously most successful pre-entered rule “Parasites” (move near successful agent): have a complex effect Split successful “host” payoff so unlikely to win – especially in competition with other species of parasite But do systematically punish successful rules No submitted rule had any defense against parasites No submitted parasite anticipated other species of parasite

20 Tournament algorithm portfolio Satisficing (stay-alive) rules: stay above the survival threshold rather than maximize short-term support Substantively plausible but raise an important issue about agent time preference – which only becomes evident in a dynamic setting “Secret handshake” rules: agent signals its presence to other agents using the same rule (e.g. using a very distinctive step size), who recognize it and avoid attacking it Substantively implausible (?) but, given 29 rules and random rule selection, there was smallish a priori probability that an agent would be in competition with another using the same rule Inter-electoral explorers: use the 19 inter-election periods to search (costlessly) for a good location on election day Substantively plausible but raise an important issue about relative costs of inter-electoral moves

21 Results: votes/rule

22 Results: votes/agent-using-rule

23 Results: agent longevity

24 Results: Pairwise performance

25 Results: run-off

26 Results: No Secret Handshake

27 Results: Evolutionary Reproduction

28 Characteristics of successful rules KQ-strat focused on staying alive, protected itself against cannibalism with a very distinctive step size, and became a parasite when below the survival threshold Shuffle was a pure staying-alive algorithm, non-parasitic and without explicit cannibalism protection, though unlikely to attack itself since it tends to avoid other agents Genety had used prior simulations deploying the genetic algorithm to optimize its parameters against a set of pre-submitted and anticipated rules. It was not a parasite, had no protection against cannibalism and did not focus on staying alive. Fisher distinctively used the 19 inter-electoral periods to find the best position at election time. However, it also satisficed by taking much smaller steps when over the threshold

29 Characteristics of successful rules Of the three other rules doing significantly better than Hunter: Sticky-Hunter/Median-Finder conditioned heavily on the survival threshold Pragmatist simply tweaked Aggregator by dragging it somewhat towards the vote-weighted centroid Pick-and-Stick simply tweaked Sticker by picking the best of 19 random locations explored in the first 19 post-birth inter- election periods. Pure center-seeking and parasite rules did badly Set of successful rules was thus diverse – most systematic pattern being to condition on the survival threshold

30 Medium Eccentricity is Best

31 Less Motion is Better

32 Conclusion Agent Based Models can help us assess causality in social science Tournaments can help bring human element into an ABM However, agent-based modelers must Keep models simple Check for closed-form solutions Ground models in the real world Work closely with statisticians (EI) and formal modelers (TM)


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