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Tutorial on agent-based modeling and simulation

Published:04 December 2005Publication History

ABSTRACT

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.

References

  1. Arthur, W. B. et al. Eds. 1997. The economy as an evolving complex system II, SFI Studies in the Sciences of Complexity, Addison Wesley: Reading, MA.Google ScholarGoogle Scholar
  2. Axelrod, R. 1997. The complexity of cooperation: agent-based models of competition and collaboration, Princeton, NJ: Princeton University Press.Google ScholarGoogle Scholar
  3. Axtell, R. 2000. Why agents? On the varied motivations for agent computing in the social sciences, Working Paper 17, Center on Social and Economic Dynamics, Brookings Institution, Washington, D.C.Google ScholarGoogle Scholar
  4. Bandura, A. 2001. Social cognitive theory: an agentic perspective, Annual Review of Psychology 52:1--26.Google ScholarGoogle ScholarCross RefCross Ref
  5. Barabási, A.-L. 2002. Linked: the new science of networks, Cambridge, MA: Perseus Pub.Google ScholarGoogle Scholar
  6. Bonabeau, E., M. Dorigo and G. Theraulaz. 1999. Swarm intelligence: from natural to artificial systems, Oxford: Oxford University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bonabeau, E. 2001. Agent-based modeling: methods and techniques for simulating human systems. In Proc. National Academy of Sciences 99(3): 7280--7287.Google ScholarGoogle Scholar
  8. Bradshaw, J. 1997. An introduction to software agents, Menlo Park, CA: AAAI Press:. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bryson, J. 2002. The behavior-oriented design of modular agent intelligence: a practical guide to behavior-oriented design (BOD), In Proc. of Agent Technology and Software Engineering (AgeS 02), Ed., Jörg P. Müller, Springer, Nov. 27.Google ScholarGoogle Scholar
  10. Callen, E. and Shapero, D. 1974. A theory of social imitation, Physics Today 27: 23--28.Google ScholarGoogle ScholarCross RefCross Ref
  11. Casti, J. 1994. Complexification, Harper Collins: New York.Google ScholarGoogle Scholar
  12. Casti, J. 1997. Would-be worlds: how simulation is changing the world of science, New York: Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Cederman, Lars-Erik. 2002. Endogenizing geopolitical boundaries with agent-based modeling, Proc. National Academy of Sciences 99 (suppl. 3):7796--7303.Google ScholarGoogle Scholar
  14. Christiansen, J. H. and M. Altaweel. 2004. Simulation of natural and social process interactions in Bronze Age Mesopotamian settlement systems, presented at Society for American Anthropology 69th Annual Meeting, Montreal, Canada.Google ScholarGoogle Scholar
  15. Collier, N., T. Howe, et al. 2003. Onward and upward: the transition to Repast 2.0. in Proc. First Annual North American Association for Computational Social and Organizational Science Conference, Pittsburgh, PA.Google ScholarGoogle Scholar
  16. Emonet, T., C. M. Macal, M. J. North, C. E. Wickersham and P. Cluzel. 2005. AgentCell: a digital single-cell assay for bacterial chemotaxis, Bioinformatics 21(11):2714--2721. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Epstein, Joshua M. 2005. Remarks on the foundations of agent-based generative social science, in Handbook on Computational Economics II, Eds., K. Judd and L. Tesfatsion, North Holland Press.Google ScholarGoogle Scholar
  18. Epstein, J. M. and R. Axtell. 1996. Growing artificial societies: social science from the bottom up, Cambridge, MA: MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. FIPA (Foundation for Intelligent Physical Agents). 2005. FIPA Home Page, <http://www.fipa.org/>.Google ScholarGoogle Scholar
  20. Gardner, M. 1970. The fantastic combinations of John Conway's new solitaire game "Life", Scientific American 223:120--123.Google ScholarGoogle ScholarCross RefCross Ref
  21. Gilbert, N. and A. Abbot. 2005. Introduction to special issue: social science computation, American Journal of Sociology 110(4):859--863.Google ScholarGoogle ScholarCross RefCross Ref
  22. Gilbert, N. and K. G. Troitzsch. 1999. Simulation for the Social Scientist, Buckingham UK: Open University Press:. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Gratch, Jonathan, and Stacy Marsella. 2001. Tears and fears: modeling emotions and emotional behaviors in synthetic agents, In Proc. 5th International Conference on Autonomous Agents, 278--285. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jennings, N. R. 2000. On agent-based software engineering, Artificial Intelligence, 117:277--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kohler, T. A., G. J. Gumerman and R. G. Reynolds. 2005. Simulating ancient societies, Scientific American, July.Google ScholarGoogle Scholar
  26. Krawczyk, K., W. Dzwinel, and D. Yuen. 2003. Nonlinear development of bacterial colony modeled with cellular automata and agent objects, Int'l. Journal of Modern Physics C, 14(10):1385--1404.Google ScholarGoogle ScholarCross RefCross Ref
  27. Law, A. M. and D. W. Kelton. 2000. Simulation modeling and analysis, 3rd ed. New York: McGraw-Hill. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Macal, C. 2003. Effects of global information availability in networks of supply chain agents, in Proc. Agent 2003: Conf. on Challenges in Social Simulation, Eds., C. Macal, D. Sallach and M. North, Chicago, IL, Oct. 2--4, 235--252, Argonne National Laboratory.Google ScholarGoogle Scholar
  29. MacKenzie, D. 2002. The science of surprise, Discover, 59--62.Google ScholarGoogle Scholar
  30. Macy, Michael W., and Robert Willer. 2002. From factors to actors: computational sociology and agent-based modeling, Annual Review of Sociology 28:143--166.Google ScholarGoogle ScholarCross RefCross Ref
  31. MathWorks. 2005. MATLAB home page, <http://www.mathworks.com>.Google ScholarGoogle Scholar
  32. Mellouli, S., G. Mineau, et al. 2003. Laying the foundations for an agent modelling methodology for faulttolerant multi-agent systems, in Fourth International Workshop Engineering Societies in the Agents World, Imperial College London, UK.Google ScholarGoogle Scholar
  33. Minar, N., R. Burkhart, et al. 1996. The Swarm simulation system, a toolkit for building multi-agent simulations, <http://www.santafe.edu/projects/swarm/overview/overview.html>.Google ScholarGoogle Scholar
  34. NetLogo. 2005. NetLogo home page, <http://http://ccl.northwestern.edu/netlogo>.Google ScholarGoogle Scholar
  35. North, M., G. Conzelmann, V. Koritarov, C. Macal, P. Thimmapuram and T. Veselka. 2002. E-laboratories: agent-based modeling of electricity markets, 2002 American Power Conference, Chicago, IL, Apr. 15--17.Google ScholarGoogle Scholar
  36. North, M. J., and C. M. Macal. In press. Managing business complexity: discovering strategic solutions with agent-based modeling and simulation, Oxford: Oxford University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. North, M. J. and C. M. Macal. 2005. Escaping the accidents of history: an overview of artificial life modeling with Repast, in Artificial Life Models in Software, Eds., A. Adamatzky and M. Komosinski, Springer-Verlag: Dordrecht, Netherlands.Google ScholarGoogle Scholar
  38. NRC (National Research Council). 2003. Dynamic social network modeling and analysis: workshop summary and papers, R. Brieger, K. Carley, and P. Pattison, Committee on Human Factors, Washington, DC: National Academies Press.Google ScholarGoogle Scholar
  39. OMG (Object Management Group). 2005. Object Management Group home page, <http://www.omg.org>.Google ScholarGoogle Scholar
  40. Rao, A. S. and M. P. Georgeff. 1999. Modeling agents within a BDI-architecture, In Proc. International Conference on Principles of Knowledge Representation and Reasoning (KR), Eds., R. Fikes and E. Sandewall, Cambridge, MA: Morgan Kaufmann.Google ScholarGoogle Scholar
  41. Repast. 2005. Repast home page, <http://repast.sourceforge.nett/>.Google ScholarGoogle Scholar
  42. Reynolds, Craig. 2005. Boids, <http://www.red3d.com/cwr/boidss/>.Google ScholarGoogle Scholar
  43. Sallach, D. 2003. Social theory and agent architectures: prospective issues in rapid-discovery social science, Social Science Computer Review 21:179--195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Sallach, D. and C. Macal. 2001. The simulation of social agents: an introduction, Special Issue of Social Science Computer Review 19(3):245--248.Google ScholarGoogle ScholarCross RefCross Ref
  45. Schelling, T. C. 1971. Dynamic models of segregation, Journal of Mathematical Sociology 1: 143--186.Google ScholarGoogle ScholarCross RefCross Ref
  46. Schelling, T. C. 1978. Micromotives and macrobehavior, New York: Norton.Google ScholarGoogle Scholar
  47. Simon, H. 2001. The sciences of the artificial, Cambridge, MA: MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Smith, V. 1989. Theory, experiments and economics, Journal of Economic Perspectives, 3(1):151--169.Google ScholarGoogle ScholarCross RefCross Ref
  49. SDG (Swarm Development Group). 2005. Swarm Development Group home page, <http://www.swarm.org>.Google ScholarGoogle Scholar
  50. Sterman, John. 1989. Testing behavioral simulation models by direct experiment, Management Science 33(12):1572--1592. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Tesfatsion, L. 2002. Agent-based computational economics: growing economies from the bottom up, Artificial Life, 8(1): 55--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Tesfatsion, L. 2005. Agent-based Computational Economics (ACE) home page. <http://www.econ.iastate.edu/tesfatsi/ace.htm>.Google ScholarGoogle Scholar
  53. Tobias, Robert and Carole Hofmann. 2004. Evaluation of free Java-libraries for social-scientific agent based simulation, Journal of Artificial Societies and Social Simulation, 7(1), Jan. 31.Google ScholarGoogle Scholar
  54. Troisi, A., V. Wong, and M. Ratner. 2005. An agent-based approach for modeling molecular self-organization, Proc. National Academy of Sciences, 102(2):255--260.Google ScholarGoogle ScholarCross RefCross Ref
  55. Wasserman, S. and K. Faust. 1994. Social network analysis: methods and applications, Cambridge, UK: Cambridge University Press.Google ScholarGoogle Scholar
  56. Wolfram, S. 2002. A new kind of science, Wolfram Media. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Wolfram Inc. 2005. Mathematica home page, <http://www.wolfram.com>.Google ScholarGoogle Scholar
  58. Young, H. P. 1998. Individual strategy and social structure: an evolutionary theory of institutions, Princeton, NJ: Princeton University Press.Google ScholarGoogle Scholar
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      • Published in

        cover image ACM Conferences
        WSC '05: Proceedings of the 37th conference on Winter simulation
        December 2005
        2769 pages
        ISBN:0780395190

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        Winter Simulation Conference

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        • Published: 4 December 2005

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        WSC '05 Paper Acceptance Rate209of316submissions,66%Overall Acceptance Rate3,413of5,075submissions,67%

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