Agent Based Models in Social Science
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Transcript of Agent Based Models in Social Science
Agent Based Models in Social Science
James FowlerUniversity 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
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 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 EconomyJOP 2006
Parties and Agenda-Setting in the Senate, 1973-1998
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 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 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
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 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? 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 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 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 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
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 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 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
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
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
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
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 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
Results: votes/rule
Results: votes/agent-using-rule
Results: agent longevity
Results: Pairwise performance
KQSTRATSHUFFLEGENETYFISHERPRAGMATISTSTICKY-HUNTERPICK-AND-STICKRAPTORHUNTERHALF-AGGREGATORSTICKERNICHE-HUNTERPATCHWORKNICHE-PREDATORAGGREGATORCENTER-MASSAVERAGEFOOL-PROOFPREDATORFOLLOW-THE-LEADERINSATIABLE-PREDATORMEDIAN-VOTER-SEEKERPARASITEAVOIDERZENOOVER-UNDERMOVE-NEAR-SUCCESSFULJUMPERBIGTENT
B I G T E N T
J U M P E R
M O V E - N E A R - S U C C E S S F U L
O V E R - U N D E R
Z E N O
A V O I D E R
P A R A S I T E
M E D I A N - V O T E R - S E E K E R
I N S A T I A B L E - P R E D A T O R
F O L L O W - T H E - L E A D E R
P R E D A T O R
F O O L - P R O O F
A V E R A G E
C E N T E R - M A S S
A G G R E G A T O R
N I C H E - P R E D A T O R
P A T C H W O R K
N I C H E - H U N T E R
S T I C K E R
H A L F - A G G R E G A T O R
H U N T E R
R A P T O R
P I C K - A N D - S T I C K
S T I C K Y - H U N T E R
P R A G M A T I S T
F I S H E R
G E N E T Y
S H U F F L E
K Q S T R A T
Vote Share By Type
N u m b e r o f P a r t i e s B y T y p e
Results: run-off
Rule Runoff Tournament Mean
vote Median
rank Mean vote
Median rank
KQ-Strat
19.6
1
11.2
1
Pick-and-Stick 15.4 2 6.8 6 Sticky hunter 15.0 3 7.3 5 Genety 14.0 4 8.4 4 Pragmatist 13.6 5 7.4 5 Shuffle 11.6 6 9.7 2 Fisher 10.9 7 7.9 4
Results: No Secret Handshake
B I G T E N T
J U M P E R
M O V E . N E A R . S U C C E S S F U L
O V E R . U N D E R
Z E N O
A V O I D E R
M E D I A N . V O T E R . S E E K E R
P A R A S I T E
F O L L O W . T H E . L E A D E R
I N S A T I A B L E . P R E D A T O R
P R E D A T O R
A V E R A G E
F O O L . P R O O F
C E N T E R . M A S S
A G G R E G A T O R
N I C H E . P R E D A T O R
P A T C H W O R K
N I C H E . H U N T E R
S T I C K E R
H U N T E R
H A L F . A G G R E G A T O R
R A P T O R
P I C K . A N D . S T I C K
S T I C K Y . H U N T E R . M E D I A N . F I N D E R
P R A G M A T I S T
F I S H E R
G E N E T Y
S H U F F L E
K Q S T R A T
0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0
T o t a l V o t e s R e c e i v e d P e r S i m u l a t i o n ( T h o u s a n d s )
Results: Evolutionary ReproductionRule Success-updated
fitness = 0.9; = 0.9
Original tournament = 0.0; = 1
KQ-strat
17.0
11.2
Genety 13.9 8.4 Sticky-hunter 11.7 7.3 Shuffle 11.2 9.7 Pick-and-stick 10.4 6.8 Pragmatist 9.6 7.4 Fisher 8.5 7.9 Raptor 4.1 4.9 Hunter 3.7 4.7 Half-aggregator 2.6 4.7 Sticker 1.2 3.9
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 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
Medium Eccentricity is Best
0 . 0 0 . 5 1 . 0 1 . 5 2 . 0
6
8
10
12
14
E c c e n t r i c i t y
( D i s t a n c e F r o m C e n t e r )
Average Vote Share (%)
S T I C K E R
A G G R E G A T O R
H U N T E R
P R E D A T O RA V E R A G E
A V O I D E R
F I S H E R
G E N E T Y
H A L F - A G G R E G A T O R
J U M P E R
K Q S T R A T
M E D I A N - V O T E R - S E E K E R
M O V E - N E A R - S U C C E S S F U L
Z E N O
N I C H E - H U N T E R
P A R A S I T E
Less Motion is Better
0 . 0 0 . 5 1 . 0 1 . 5
6
8
10
12
14
M o t i o n
( A v e r a g e D i s t a n c e M o v e d F r o m P r e v i o u s E l e c t i o n )
Average Vote Share (%)
S T I C K E R
A G G R E G A T O R
H U N T E R
P R E D A T O R
A V O I D E R
G E N E T Y
J U M P E R
K Q S T R A T
M E D I A N - V O T E R - S E E K E R
M O V E - N E A R - S U C C E S S F U L
Z E N O
P A R A S I T E
P A T C H W O R K
P I C K - A N D - S T I C K
R A P T O R
S H U F F L E
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)