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Agent Based Models in Social Science James Fowler University of California, San Diego The Big Picture: Collective Action Cooperation Alternative Models of Participation Social Networks Cooperation Evolutionary models Altruistic Punishment and the Origin of Cooperation PNAS 2005

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agent based models in social science
Agent Based Models in Social Science

James Fowler

University of California, San Diego

the big picture collective action
The Big Picture: Collective Action
  • Cooperation
  • Alternative Models of Participation
  • Social Networks
  • Evolutionary models
    • Altruistic Punishment and the Origin of Cooperation PNAS 2005
    • Second Order Defection Problem Solved?Nature 2005
    • On the Origin of Prospect TheoryJOP, forthcoming
    • The Evolution of Overconfidence
  • Experiments
    • Egalitarian Motive and Altruistic PunishmentNature 2005
    • Egalitarian Punishment in HumansNature 2007
    • The Role of Egalitarian Motives in Altruistic Punishment
    • The Neural Basis of Egalitarian Behavior
alternative models of political participation
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 CompetitionJTP 2007
    • Habitual Voting and Behavioral TurnoutJOP 2006
    • A Tournament of Party Decision Rules
  • Empirical Models of Legislator Behavior
    • Dynamic Responsiveness in the U.S. SenateAJPS 2005
    • Elections and Markets: The Effect of Partisan Orientation, Policy Risk, and Mandates on the EconomyJOP 2006
    • Parties and Agenda-Setting in the Senate, 1973-1998
alternative models of political participation5
Alternative Models of Political Participation
  • Experiments
    • Altruism and TurnoutJOP 2006
    • Patience as a Political Virtue: Delayed Gratification and TurnoutPolitical Behavior 2006
    • Beyond the Self: Social Identity, Altruism, and Political ParticipationJOP 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
political social networks
Political Social Networks
  • Voters
    • Dynamic Parties and Social Turnout: an Agent-Based ModelAJS 2005
    • Turnout in a Small WorldSocial Logic of Politics 2005
  • Legislators
    • Legislative Cosponsorship Networks in the U.S. House and SenateSocial Networks 2006
    • Connecting the Congress: A Study of Cosponsorship NetworksPolitical 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
political social networks7
Political Social Networks
  • Court Precedents
    • The Authority of Supreme Court PrecedentSocial Networks, forthcoming
    • Network Analysis and the Law: Measuring the Legal Importance of Supreme Court PrecedentsPolitical Analysis, forthcoming
other social networks
Other Social Networks
  • Political Science PhDs
    • Social Networks in Political Science: Hiring and Placement of PhDs, 1960-2002PS 2007
  • Academic Citations
    • Does Self Citation Pay?Scientometrics 2007
  • Health Study Participants
    • The Spread of Obesity in a Large Social Network Over 32 YearsNew England Journal of Medicine 2007
    • Friends and Participation
    • Genetic Basis of Social Networks
what is an agent based model
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
what is an agent based model10
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.
what is an agent based model11
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
advantages of agent based modeling
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.”
disadvantages of agent based modeling
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?
my approach to agent based modeling
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
tournament overview
Tournament Overview
  • A dynamic spatial account of multi-party multi-dimensional political competition
    • is substantively plausible
    • generates a complex system that is analyticallyintractable
    • 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
tournament abm test bed
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-enteredbut 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
departures from laver 2005
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 factosurvival threshold (<10%, 2 consecutive elections)
  • Rule designers’ knowledge of pre-entered rules
  • Diverse and indeterminate rule set to be competed against
tournament structure
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)
tournament algorithm portfolio
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
tournament algorithm portfolio20
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
characteristics of successful rules
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
characteristics of successful rules29
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
  • 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)