1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Political Science
  4. Volume 17, 2014
  5. Article

Annual Review of Political Science

Volume 17, 2014

Agent-Based Models

Abstract

Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world.

Keyword(s): complexity, computational, game theory, social science
Loading

Article metrics loading...

/content/journals/10.1146/annurev-polisci-080812-191558
2014-05-11
2024-04-16
Loading full text...

Full text loading...

/deliver/fulltext/polisci/17/1/annurev-polisci-080812-191558.html?itemId=/content/journals/10.1146/annurev-polisci-080812-191558&mimeType=html&fmt=ahah

Literature Cited

  1. Alchian AA. 1950. Uncertainty, evolution, and economic theory. J. Polit. Econ. 58.3:211–21 [Google Scholar]
  2. Anderson P. 1972. More is different. Science 177:393–96 [Google Scholar]
  3. Andreoni J, Miller JH. 1995. Auctions with artificial adaptive agents. Games Econ. Behav. 10:139–64 [Google Scholar]
  4. Ansolabehere S. et al. 2005. Voting weights and formateur advantages in the formation of coalition governments. Am. J. Polit. Sci. 49:3550–63 [Google Scholar]
  5. Axelrod R. 1997. The Complexity of Cooperation Princeton, NJ: Princeton Univ. Press
  6. Axelrod R, Axtell R, Epstein J, Cohen MD. 1996. Aligning simulation models: a case study and results. Comput. Math. Organ. Theory 1:123–41 [Google Scholar]
  7. Axelrod R, Cohen MD. 2000. Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: Free Press
  8. Axelrod R, Mitchell W, Thomas RE, Bennett DS, Bruderer E. 1995. Coalition formation in standard-setting alliances. Manag. Sci. 41:1493–508 [Google Scholar]
  9. Bak P. 1996. How Nature Works: The Science of Self-Organized Criticality New York: Springer-Verlag
  10. Bankes S. 2002. Agent-based modeling: a revolution?. Proc. Natl. Acad. Sci. USA 99:7199–200 [Google Scholar]
  11. Baron DP, Ferejohn JA. 1989. Bargaining in legislatures. Am. Polit. Sci. Rev. 89:1181–206 [Google Scholar]
  12. Bednar J. 2009. The Robust Federation: Principles of Design New York: Cambridge Univ. Press
  13. Bednar J, Bramson A, Jones-Rooy A, Page SE. 2010. Emergent cultural signatures and persistent diversity: a model of conformity and consistency. Ration. Soc. 22:4407–44 [Google Scholar]
  14. Bednar J, Chen Y, Liu X, Page SE. 2012. Behavioral spillovers and cognitive load in multiple games: an experimental study. Games Econ. Behav. 74:112–31 [Google Scholar]
  15. Bednar J, Page SE. 2007. Can game(s) theory explain culture? The emergence of cultural behavior within multiple games. Ration. Soc. 19:65–97 [Google Scholar]
  16. Bendor J, Diermeier D, Ting M. 2003. A behavioral model of turnout. Am. Polit. Sci. Rev. 97:2261–80 [Google Scholar]
  17. Billings D, Papp D, Schaeffer J, Szafron D. 1998. Poker as a testbed for machine intelligence research. Work. Pap., Dept. Comput. Sci., Univ. Alberta. http://poker.cs.ualberta.ca/publications.html
  18. Brown GW. 1951. Iterative solutions of games by fictitious play. Activity Analysis of Production and Allocation TC Koopmans 374–76 New York: Wiley [Google Scholar]
  19. Bruch E, Mare R. 2006. Neighborhood choice and neighborhood change. Am. J. Sociol. 112:667–709 [Google Scholar]
  20. Camerer. 2003. Behavioral Game Theory: Experiments in Strategic Interaction Princeton, NJ: Princeton Univ. Press
  21. Carpenter DP, Esterling KM, Lazer DMJ. 2004. Friends, brokers, and transitivity: Who informs whom in Washington politics?. J. Polit. 66:1224–46 [Google Scholar]
  22. Cederman LE. 1997. Emergent Actors in World Politics Princeton, NJ: Princeton Univ. Press
  23. Cederman LE. 2005. Computational models of social forms: advancing generative process theory. Am. J. Sociol. 110:4864–93 [Google Scholar]
  24. Conitzer V, Sandholm T. 2006. Computing the optimal strategy to commit to. Proc. Seventh ACM Conf. Electron. Commer. New York: Assoc. Comp. Mach. [Google Scholar]
  25. Cutler J, de Marchi S, Gallop M, Hollenbach F, Laver M, Orlowski M. 2013. Cabinet formation and portfolio distribution in European multi-party systems Work. Pap., Dept. Polit. Sci., Duke Univ.
  26. de Marchi S. 1999. Adaptive models and electoral instability. J. Theor. Polit. 11:3393–419 [Google Scholar]
  27. de Marchi S. 2005. Computational and Mathematical Modelling in the Social Sciences New York: Cambridge Univ. Press
  28. de Marchi S, Page S. 2009. Agent-based modeling. Oxford Handbook of Political Science Methodology JM Box-Steffensmeier, HE Brady, D Collier 71–96 New York: Cambridge Univ. Press [Google Scholar]
  29. Diermeier D, Krehbiel K. 2003. Institutionalism as methodology. J. Theor. Polit. 15:2123–44 [Google Scholar]
  30. Ensley MJ, Tofias MW, de Marchi S. 2009. District complexity as an advantage in congressional elections. Am. J. Polit. Sci. 53:4990–1005 [Google Scholar]
  31. Epstein JM. 2007. Generative Social Science: Studies in Agent-Based Computational Modeling Princeton, NJ: Princeton Univ. Press
  32. Epstein JM. 2014. Toward Neurocognitive Foundations for Generative Social Science. Princeton, NJ: Princeton Univ. Press
  33. Epstein JM, Axtell R. 1996. Growing Artificial Societies: Social Science from the Bottom Up Cambridge, MA: MIT Press
  34. Epstein JM, Goedecke DM, Yu F, Morris RJ, Wagener DK, Bobashev GV. 2006. Pandemic influenza and the value of international travel restrictions CSED Work. Pap. 46, Brookings Inst., Washington, DC
  35. Fowler J. 2006a. Connecting the Congress: a study of cosponsorship networks. Polit. Anal. 14:4456–87 [Google Scholar]
  36. Fowler J. 2006b. Habitual voting and voter turnout. J. Polit. 68:2335–44 [Google Scholar]
  37. Fowler J. 2006c. Legislative cosponsorship networks in the U.S. House and Senate. Soc. Netw. 28:4454–65 [Google Scholar]
  38. Fowler J, Laver M. 2008. A tournament of party decision rules. J. Conf. Resolut. 52:168–92 [Google Scholar]
  39. Fowler J, Smirnov O. 2005. Dynamic parties and social turnout: an agent-based model. Am. J. Sociol. 110:4 [Google Scholar]
  40. Gartzke E, Weisiger A. 2013. Fading friendships: alliances, affinities and the activation of international identities. Br. J. Polit. Sci. 43:25–52 [Google Scholar]
  41. Gaylord R, D'Andria LJ. 1998. Simulating Society: A Mathematical Toolkit for Modeling Socioeconomic Behavior London: Springer
  42. Geanakoplos J, Axtell R, Farmer JD, Howitt P, Conlee B. et al. 2012. Getting at systemic risk via an agent-based model of the housing market. Am. Econ. Rev. 102:353–58 [Google Scholar]
  43. Gell-Mann M. 1994. The Quark and the Jaguar: Adventures in the Simple and the Complex New York: Freeman
  44. Glaeser E, Sacerdote B, Scheinkman J. 1996. Crime and social interactions. Q. J. Econ. 111:507–48 [Google Scholar]
  45. Golder M, Golder S, Siegel D. 2012. Modeling the institutional foundation of parliamentary government formation. J. Polit. 74:2427 [Google Scholar]
  46. Golman R, Page SE. 2009. Basins of attraction and equilibrium selection under different learning rules. Evol. Econ. 20:49–72 [Google Scholar]
  47. Golman R, Page SE. 2010. Individual and cultural learning in stag hunt games with multiple actions. J. Econ. Behav. Organ. 73:3359–76 [Google Scholar]
  48. Hoff PD, Ward MD. 2004. Modeling dependencies in international relations networks. Polit. Anal. 12:160–75 [Google Scholar]
  49. Holland J, Miller J. 1991. Artificial agents in economic theory. Am. Econ. Rev. Pap. Proc. 81:365–70 [Google Scholar]
  50. Hong L, Page S. 2009. Interpreted and generated signals. J. Econ. Theory 144:2174–96 [Google Scholar]
  51. Huckfeldt RR, Johnson PE, Sprague JD. 2004. Political Disagreement: The Survival of Diverse Opinions Within Communication Networks Cambridge, UK: Cambridge Univ. Press
  52. Jackson M. 2008. Social and Economic Networks Princeton, NJ: Princeton Univ. Press
  53. Jen E. 2005. Robust Design: A Repertoire of Biological, Ecological, and Engineering Case Studies Oxford, UK: Oxford Univ. Press
  54. Jervis R. 1997. System Effects: Complexity in Political and Social Life Princeton, NJ: Princeton Univ. Press
  55. Jones-Rooy A, Page SE. 2012. The Complexity of Systems Effects. Crit. Rev. 24:3313–42 [Google Scholar]
  56. Judd K. 1997. Computational economics and economic theory: complements or substitutes?. J. Econ. Dyn. Control 21:6907–42 [Google Scholar]
  57. Kauffman SA. 1993. The Origins of Order: Self-Organization and Selection in Evolution New York: Oxford Univ. Press
  58. Kim S-Y, Taber CS, Lodge M. 2010. A computational model of the citizen as motivated reasoner: modeling the dynamics of the 2000 presidential election. Polit. Behav. 32:1–28 [Google Scholar]
  59. Kirman A. 1997. The economy as an interactive system. The Economy as a Complex Evolving System II WB Arthur, S Durlauf, D Lane 491–533 Reading, MA: Addison Wesley [Google Scholar]
  60. Kollman K. 2012. The potential value of computational models in social science research. Oxford Handbook in Philosophy in the Social Sciences H Kincaid New York: Oxford Univ. Press [Google Scholar]
  61. Kollman K, Miller J, Page S. 1992. Adaptive parties in spatial elections. Am. Polit. Sci. Rev. 86:929–37 [Google Scholar]
  62. Kollman K, Miller J, Page S. 1997. Political institutions and sorting in a Tiebout model. Am. Econ. Rev. 87:977–92 [Google Scholar]
  63. Kollman K, Miller J, Page S. 2003. Computational Models of Political Economy Cambridge, MA: MIT Press
  64. Kollman K, Page S. 2005. Computational methods and models of politics. The Handbook of Computational Economics L Tesfatsion, K Judd 1433–63 Amsterdam: Elsevier/North Holland [Google Scholar]
  65. Lamberson PJ, Page SE. 2012. Tipping points. Q. J. Polit. Sci. 7:2175–208 [Google Scholar]
  66. Laver M. 2005. Policy and the dynamics of political competition. Am. Polit. Sci. Rev. 99:2263–81 [Google Scholar]
  67. Laver M, de Marchi S, Mutlu H. 2011. Negotiation in legislatures over government formation. Public Choice 147:285–304 [Google Scholar]
  68. Laver M, Sergenti E. 2011. Party Competition: An Agent-Based Model Princeton, NJ: Princeton Univ. Press
  69. Lazer D. 2001. The co-evolution of individual and network. J. Math. Sociol. 25:69–108 [Google Scholar]
  70. LeBaron B. 2001. Stochastic volatility as a simple generator of apparent financial power laws and long memory. Q. Financ. 1:621–31 [Google Scholar]
  71. Leombruni R, Richiardi M. 2005. Why are economists sceptical about agent-based simulations?. Phys. A 355:1103–9 [Google Scholar]
  72. Lodge M, Sung-Youn K, Charles T. 2004. A computational model of political cognition—the dynamics of candidate evaluation in the 2000 presidential election Presented at Annu. Meet. Midwest Polit. Sci. Assoc., Chicago
  73. Lustick I, Miodownik D. 2000. Deliberative democracy and public discourse: the agent-based argument repertoire model. Complexity 5:413–30 [Google Scholar]
  74. Lustick I, Miodownik D, Eidelson RJ. 2004. Secessionism in multicultural states: Does sharing power prevent or encourage it?. Am. Polit. Sci. Rev. 98:2209–29 [Google Scholar]
  75. Miller J. 1998. Active nonlinear tests (ANTs) of complex simulations models. Manag. Sci. 44:820–30 [Google Scholar]
  76. Miller J, Page S. 2007. Complex Adaptive Systems: Computational Models of Social Life Princeton, NJ: Princeton Univ. Press
  77. Mitchell M. 2009. Complexity: A Guided Tour Oxford, UK: Oxford Univ. Press
  78. Newman MJ. 2003. On the logic and structure of complex networks. SIAM Rev. 45:167–256 [Google Scholar]
  79. Nowak M, Bonhoeffer S, May R. 1994. Spatial games and the maintenance of cooperation. Proc. Natl. Acad. Sci. USA 91:4877–81 [Google Scholar]
  80. Nowak M, May RM. 1992. Evolutionary games and spatial chaos. Nature 359:826–29 [Google Scholar]
  81. Ostrom E. 1990. Governing the Commons: The Evolution of Institutions for Collective Action Cambridge, UK: Cambridge Univ. Press
  82. Page SE. 1997. On incentives and updating in agent based models. Comput. Econ. 10:67–87 [Google Scholar]
  83. Page SE. 2006. Essay: path dependence. Q. J. Polit. Sci. 1:187–115 [Google Scholar]
  84. Page SE. 2007. The Difference: How the Power of Diversity Creates Better Groups, Teams, Schools, and Societies Princeton, NJ: Princeton Univ. Press
  85. Page SE. 2008. Uncertainty, difficulty, and complexity. J. Theor. Polit. 20:115–49 [Google Scholar]
  86. Page SE. 2011. Diversity and Complexity Princeton, NJ: Princeton Univ. Press
  87. Palmer R, Arthur W, Holland J, Lebaron B, Tayler P. 1994. Artificial economic life: a simple model of a stock market. Phys. D 75:264–74 [Google Scholar]
  88. Richards D. 2000. Nonlinear dynamics in games: convergence and stability in international environmental agreements. Political Complexity: Nonlinear Models of Politics D Richards 173–206 Ann Arbor: Univ. Michigan Press [Google Scholar]
  89. Schelling T. 1978. Micromotives and Macrobehavior New York: Norton
  90. Smirnov O, Fowler JH. 2007. Policy-motivated parties in dynamic political competition. J. Theor. Polit. 19:19–31 [Google Scholar]
  91. Tesfatsion L. 1997. How economists can get alife. The Economy as a Complex Evolving System II W Arthur, S Durlauf, D Lane 533–65 Reading, MA: Addison Wesley [Google Scholar]
  92. Vriend N. 2000. An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. J. Econ. Dyn. Control 24:1–19 [Google Scholar]
  93. Weidmann N, Salehyan I. 2013. Violence and ethnic segregation: a computational model applied to Baghdad. Int. Stud. Q. 57:52–64 [Google Scholar]
  94. Wichowsky A. 2012. District complexity and the personal vote. Legis. Stud. Q. 37:4437–63 [Google Scholar]
  95. Zinkevich M, Bowling M, Johanson M, Piccione C. 2007. Regret minimization in games with incomplete information Presented at Neural Inf. Process. Syst., Dec. 3–6, British Columbia, Can. http://books.nips.cc/nips20.html
/content/journals/10.1146/annurev-polisci-080812-191558
Loading
  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error