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Abstract

Agent-based models (ABMs) are computational models used to simulate the actions and interactions of agents within a system. Usually, each agent has a relatively simple set of rules for how he or she responds to his or her environment and to other agents. These models are used to gain insight into the emergent behavior of complex systems with many agents, in which the emergent behavior depends upon the micro-level behavior of the individuals. ABMs are widely used in many fields, and this article reviews some of those applications. However, as relatively little work has been done on statistical inference for such models, this article also points out some of those gaps and recent strategies to address them.

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/content/journals/10.1146/annurev-statistics-010814-020218
2015-04-10
2024-03-28
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Literature Cited

  1. Antle JM, Capalbo SM, Elliott ET, Hunt HW, Mooney S. et al. 2001. Research needs for understanding and predicting the behavior of managed ecosystems: lessons from the study of agroecosystems. Ecosystem 4:8723–35 [Google Scholar]
  2. Axelrod R. 1997. The Complexity of Cooperation: Agent-Based Models of Conflict and Cooperation Princeton, NJ: Princeton Univ. Press
  3. Berry BJL, Kiel LD, Elliott E. 2002. Adaptive agents, intelligence, and emergent human organization: capturing complexity through agent-based modeling. PNAS 99:7187–88 [Google Scholar]
  4. Bijak J, Hilton J, Silverman E, Cao VD. 2013. Reforging the wedding ring: exploring a semi-artificial model of population for the United Kingdom with Gaussian process emulators. Demogr. Res. 29:729–66 [Google Scholar]
  5. Blum MGB, Francois O. 2010. Non-linear regression models for Approximate Bayesian Computation. Stat. Comput. 20:163–73 [Google Scholar]
  6. Burke SD, Epstein JM, Cummings DAT, Parker JI, Cline KC. et al. 2006. Individual-based computational modeling of smallpox epidemic control strategies. Acad. Emerg. Med. 13:1142–49 [Google Scholar]
  7. Cangelosi R, Goriely A. 2007. Component retention in principal component analysis with application to cDNA microarray data. Biol. Direct 2:21–21 [Google Scholar]
  8. Chen S-H, Chang C-L, Du Y-R. 2009. Agent-based economic models and econometrics. Knowl. Eng. Rev. 27:1–46 [Google Scholar]
  9. Cisse PA, Dembele JM, Lo M, Cambier C. 2013. Assessing the spatial impact on an agent-based modeling of epidemic control: case of schistosomiasis. Complex Sci. 126:58–69 [Google Scholar]
  10. Crooks AT. 2010. Constructing and implementing an agent-based model of residential segregation through vector GIS. Int. J. Geogr. Inf. Sci. 24:5661–75 [Google Scholar]
  11. Dawid H, Reimann M, Bullnheimer B. 2001. To innovate or not to innovate?. IEEE Trans. Evol. Comput. 5:471–81 [Google Scholar]
  12. Del Moral P, Doucet A, Jasra A. 2012. An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat. Comput. 22:51009–20 [Google Scholar]
  13. Duffy J. 2001. Learning to speculate: experiments with artificial and real agents. J. Econ. Dyn. 25:295–319 [Google Scholar]
  14. Epstein JM. 2001. Learning to be thoughtless: Social norms and individual computation. Comput. Econ. 18:19–24 [Google Scholar]
  15. Epstein JM. 2002. Modeling civil violence: an agent-based computational approach. PNAS 99:Suppl. 37243–50 [Google Scholar]
  16. Epstein JM, Axtell R. 1996. Growing Artificial Societies: Social Science from the Bottom Up Washington, DC: Brooking Inst.
  17. Filatova T, Verburg PH, Parker DC, Stannard CA. 2013. Spatial agent-based models for socio-ecological systems: challenges and prospects. Environ. Model. Softw. 45:1–7 [Google Scholar]
  18. Gardner M. 1970. Mathematical games: the fantastic combinations of John Conway's new solitaire game “Life.”. Sci. Am. 222:120–23 [Google Scholar]
  19. Gintis H. 2000. Game Theory Evolving Princeton, NJ: Princeton Univ. Press
  20. Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V. et al. 2006. A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198:115–26 [Google Scholar]
  21. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. 2010. The ODD protocol: a review and first update. Ecol. Model. 221:232760–68 [Google Scholar]
  22. Heard D. 2014. Statistical inference utilizing agent-based models. PhD Thesis. Duke University, Durham, N.C.
  23. Heard D, Bobashev GV, Morris RJ. 2014. Reducing the complexity of an agent-based local heroin market model. PLOS ONE 9:e100263 [Google Scholar]
  24. Higdon D, Gattiker J, Williams B, Rightley M. 2008. Computer model calibration using high-dimensional output. J. Am. Stat. Assoc. 103:570–83 [Google Scholar]
  25. Hoffer LD. 2005. Junkie Business: The Evolution and Operation of a Heroin Dealing Network (Case Studies on Contemporary Social Issues) Beverly, MA: Wadsworth
  26. Hoffer LD, Bobashev GV, Morris RJ. 2009. Researching a local heroin market as a complex adaptive system. Am. J. Community Psychol. 44:273–86 [Google Scholar]
  27. Hommes CH. 2002. Modeling the stylized facts in finance through simple nonlinear adaptive systems. PNAS 99:37221–28 [Google Scholar]
  28. Hooten MB, Wikle CK. 2010. Statistical agent-based models for discrete spatio-temporal systems. J. Am. Stat. Assoc. 105:236–48 [Google Scholar]
  29. Izumi K, Ueda K. 2001. Phase transition in a foreign exchange market: analysis based on an artificial market approach. IEEE Trans. Evol. Comput. 5:456–70 [Google Scholar]
  30. Kennedy MC, O'Hagan A. 2001. Bayesian calibration of computer models. J. R. Stat. Soc. B 63:3425–64 [Google Scholar]
  31. LeBaron B. 2001. Empirical regularities from interacting long and short horizon investors in an agent-based stock market. IEEE Trans. Evol. Comput. 5:442–55 [Google Scholar]
  32. Lorek H, Sonnenschein M. 1999. Modelling and simulation software to support individual-oriented ecological modelling. Ecol. Model. 115:199–216 [Google Scholar]
  33. Luus KA, Robinson DT, Deadman PJ. 2013. Representing ecological processes in agent-based models of land use and cover change. J. Land Use Sci. 8:175–198 [Google Scholar]
  34. Lux T, Marchesi M. 2000. Volatility clustering in financial markets: a micro-simulation of interacting agents. Int. J. Theor. Appl. Finance 3:675–702 [Google Scholar]
  35. Marjoram P, Molitor J, Plagnol V, Tavare S. 2003. Markov chain Monte Carlo without likelihoods. PNAS 100:15324–28 [Google Scholar]
  36. O'Hagan A. 2006. Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91:10–111290–300 [Google Scholar]
  37. Parker DC, Hessl A, Davis SC. 2008. Complexity, land-use modeling, and the human dimension: fundamental challenges for mapping unknown outcome spaces. Geoforum 39:2789e804 [Google Scholar]
  38. Polhill JG, Parker D, Brown D, Grimm V. 2008. Using the ODD protocol for describing three agent-based social simulation models of land-use change. J. Artif. Soc. Soc. Simul. 11:23 [Google Scholar]
  39. Pritchard JK, Seielstad MT, Perez-Lezaun A, Feldman MW. 1999. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol. Biol. Evol. 16:1791–98 [Google Scholar]
  40. Tassier T, Menczer F. 2001. Emerging small-world referral networks in evolutionary labor markets. IEEE Trans. Evol. Comput. 5:482–92 [Google Scholar]
  41. Tesfatsion L. 2002. Agent-based computational economics: growing economies from the bottom up. Artif. Life 8:155–82 [Google Scholar]
  42. Wilhite A. 2001. Bilateral trade and small-world networks. Comput. Econ. 18:149–64 [Google Scholar]
  43. Wolfram S. 1983. Statistical mechanics of cellular automata. Rev. Mod. Phys. 55:3601–44 [Google Scholar]
  44. Zou Y, Fonoberov VA, Fonoberova M, Mezic I, Kevrekidis IG. 2012. Model reduction for agent-based social simulation: coarse-graining a civil violence model. Phys. Rev. E 85:6066106 [Google Scholar]
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