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 Experiments
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 Academic Citations
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)