Hostname: page-component-5db58dd55d-htx7c Total loading time: 0 Render date: 2026-05-28T12:39:17.684Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis and synthesis: multi-agent systems in the social sciences

Published online by Cambridge University Press:  26 April 2012

Robert E. Marks
Robert E. Marks*
Affiliation:
Melbourne Business School, University of Melbourne, Carlton, Vic 3053, Australia; e-mail: robert.marks@mbs.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Although they flow from a common source, the uses of multi-agent systems (or ‘agent-based computational systems’––ACE) vary between the social sciences and computer science. The distinction can be broadly summarized as analysis versus synthesis, or explanation versus design. I compare and contrast these uses, and discuss sufficiency and necessity in simulations in general and in multi-agent systems in particular, with a computer science audience in mind.

Information

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 2: Agent-Based Computational Economics , 26 April 2012 , pp. 123 - 136
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Arifovic, J, Ledyard, J 2008. Individual Evolutionary Learning and the Voluntary Contributions Mechanism, Working Paper, California Insititute of Technology.Google Scholar
Arthur, W. B. 1991. Designing economic agents that act like human agents: a behavioral approach to bounded rationality. American Economic Review 81, 353–359.Google Scholar
Arthur, W. B. 1993. On designing economic agents that behave like human agents. Journal of Evolutionary Economics 3, 1–22.CrossRefGoogle Scholar
Axelrod, R. 1984. The Evolution of Cooperation. Basic Books.Google Scholar
Axelrod, R. 2006. Advancing the art of simulation in the social sciences. In Handbook of Research on Nature-Inspired Computing for Economy and Management, Rennard, J.-P. (ed.). Idea Group Inc., 90–100.Google Scholar
Axtell, R., Axelrod, R., Epstein, J., Cohen, M. D. 1996. Aligning simulation models: a case study and results. Computational and Mathematical Organization Theory 1, 123–141.CrossRefGoogle Scholar
Brenner, T. 2006. Agent learning representation: advice on modelling economic learning (Ch. 18). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 895–947.Google Scholar
Burton, R. M. 2003. Computational laboratories for organizational science: questions, validity and docking. Computational & Mathematical Organizational Theory 9, 91–108.CrossRefGoogle Scholar
Byde, A. 2006. Applying evolutionary search to a parametric family of auction mechanisms. Australian Journal of Management 31(1), 1–16. http://www.agsm.edu.au/bobm/eajm/0606/01-byde.htmlCrossRefGoogle Scholar
Cliff, D. 2001. Evolutionary Optimization of Parameter Sets for Adaptive Software-agent Traders in Continuous Double Auction Markets. Hewlett-Packard Technical Report HPL-2001-99, November.Google Scholar
Duffy, J. 2006. Agent-based models and human subject experiments (Ch. 19). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 949–1011.Google Scholar
Durlauf, S. 2005. Complexity and empirical economics. The Economic Journal 115 (June), F225–F243.CrossRefGoogle Scholar
Edmonds, B., Bryson, J. J. 2003. Beyond the Design Stance: the Intention of Agent-based Engineering. Centre for Policy Modelling, CPM Report no.: CPM-03-126. http://cfpm.org/papers/btds.pdfGoogle Scholar
Epstein, J. M. 1999. Agent-based computational models and generative social science. Complexity 4(5), 41–60.3.0.CO;2-F>CrossRefGoogle Scholar
Epstein, J. M. 2006. Remarks on the foundations of agent-based generative social science (Ch. 34). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 1585–1604.Google Scholar
Fader, P. S., Hauser, J. R. 1988. Implicit coalitions in a generalized prisoner's dilemma. Journal of Conflict Resolution 32, 553–582.CrossRefGoogle Scholar
Friedman, M. 1953. Essays in Positive Economics. University of Chicago Press.Google Scholar
Gilbert, N., Troitzsch, K. G. 2005. Simulation for the Social Scientist, 2nd edn. Open University Press.Google Scholar
Gjerstad, S., Dickhaut, J. 1998. Price formation in double auctions. Games and Economic Behavior 22, 1–29.CrossRefGoogle Scholar
Gode, D., Sunder, S. 1993. Allocation efficiency of markets with zero intelligence traders: market as a partial substitute for individual rationality. Journal of Political Economy 101, 119–137.CrossRefGoogle Scholar
Haefner, J. W. 2005. Modeling Biological Systems: Principles and Applications, 2nd edn. Springer.CrossRefGoogle Scholar
Holland, J. H. 1975. Adaptation in Natural and Artifical Systems. University of Michigan Press.Google Scholar
Jennings, N. R., Faratin, P., Lomuscio, A. R., Parsons, S., Sierra, C., Wooldridge, M. 2001. Automated negotiation: prospects, methods, and challenges. International Journal of Group Decision and Negotiation 10(2), 199–215.CrossRefGoogle Scholar
Judd, K. L. 2006. Computationally intensive analyses in economics (Ch. 17). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 881–893.Google Scholar
Kauffman, S. A. 1995. At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press.Google Scholar
Knuth, D. E. (1968–1973). The Art of Computer Programming. Addison-Wesley.Google Scholar
Koesrindartoto, D., Tesfatsion, L. 2004. Testing the reliability of FERC's Wholesale Power Market Platform: an agent-based computational economics approach. In Energy, Environment and Economics in a New Era, Proceedings of the 24th Annual North American Conference of the United States Association for Energy Economics and the International Association for Energy Economics, 8–10 July 2004, Washington, DC. IAEE/USAEE.Google Scholar
LeBaron, B. 2006. Agent-based computational finance (Ch. 24). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 1187–1233.Google Scholar
MacKie-Mason, J. K., Wellman, M. P. 2006. Automated markets and trading agents (Ch. 28). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 1381–1431.Google Scholar
Mankin, J. B., O'Neill, R. V., Shugart, H. H., Rust, B. W. 1977. The importance of validation in ecosystem analysis. In New Directions in the Analysis of Ecological Systems, Part 1, Simulation Council Proceedings Series, 5, Innis, G. S. (ed.). Simulation Councils, 63–71. Reprinted in Shugart H. H. and O'Neill, R. V. (eds), Systems Ecology, (Dowden, Hutchinson and Ross), 1979, 309–317.Google Scholar
Marks, R.E. (1989). Breeding optimal strategies: optimal behavior for oligopolists. In Proceedings of the 3rd International Conference on Genetic Algorithms, George Mason University, June 4–7, 1989, Schaffer J.Ď. (ed.). Morgan Kaufmann Publishers, 198–207.Google Scholar
Marks, R. E. 2003. Models rule. Australian Journal of Management 28(1), i–v. http://www.agsm.edu.au/bobm/eajm/0306/edit.htmlGoogle Scholar
Marks, R. E. 2006. Market design using agent-based models (Ch. 27). In Handbook of Computational Economics, 2: Agent-Based Modeling, Tesfatsion, L. & Judd, K. L. (eds). Elsevier Science, 1339–1380.Google Scholar
Marks, R. E. 2007. Validating simulation models: a general framework and four applied examples. Computational Economics 30(3), 265–290.CrossRefGoogle Scholar
Marks, R. E. 2010. Learning to be Risk-Neutral: CRRA Agents Evolve to be Risk-Neutral, even with the Possibility of Bankruptcy. Mimeo. http://www.agsm.edu.au/bobm/papers/ralet.pdfGoogle Scholar
Marks, R. E., Midgley, D. F., Cooper, L. G. 2006. Co-evolving better strategies in oligopolistic price wars. In Handbook of Research on Nature-Inspired Computing for Economy and Management, Rennard (ed.). Idea Group Inc., 806–821.Google Scholar
McMillan, J. 1994. Selling spectrum rights. Journal of Economic Perspectives 8(3), 145–162.CrossRefGoogle Scholar
McMillan, J. 2002. Reinventing the Bazaar: A Natural History of Markets. Norton.Google Scholar
Midgley, D. F., Marks, R. E., Cooper, L. G. 1997. Breeding competitive strategies. Management Science 43(3), 257–275.CrossRefGoogle Scholar
Midgley, D. F., Marks, R. E., Kunchamwar, D. 2007. The building and assurance of agent-based models: an example and challenge to the field. Journal of Business Research (Special Issue: Complexities in Markets), 60, 884–893.CrossRefGoogle Scholar
Milgrom, P. 2004. Putting Auction Theory to Work. Cambridge University Press.CrossRefGoogle Scholar
Mirowski, P. 2007. Markets come to bits: evolution, computation and markomata in economic science. Journal of Economic Behavior & Organization 63(2), 209–242.CrossRefGoogle Scholar
Newton, I. 1687. Philosophiæ naturalis principia mathematica. London: Jussu Societatis Regiae ac Typis J. Streater.CrossRefGoogle Scholar
Phelps, S., McBurney, P., Parsons, S., Sklar, E. 2002. Co-evolutionary auction mechanism design: a preliminary report. In Lecture Notes In Computer Science: Revised Papers from the Workshop on Agent-Mediated Electronic Commerce IV: Designing Mechanisms and Systems. Padget, J. A., Shehory, O., Parkes, D. C., Sadeh, N. M. & Walsh, W. E. (eds). Springer123–142.Google Scholar
Roth, A. E. 1991. Game theory as a part of empirical economics. Economic Journal 101(401), 107–114.CrossRefGoogle Scholar
Roth, A. E. 2000. Game theory as a tool for market design. In Game Practice: Contributions from Applied Game Theory, Patrone, F., Garcia-Jurado, I. & Tijs, S. (eds). Kluwer7–18.CrossRefGoogle Scholar
Roth, A. E. 2002. The economist as engineer: game theory, experimentation, and computation as tools for design economics. Econometrica 70(4), 1341–1378.CrossRefGoogle Scholar
Rubinstein, A. 1998. Modeling Bounded Rationality. M.I.T. Press.CrossRefGoogle Scholar
Simon, H. A. 1982. Models of Bounded Rationality. M.I.T. Press.Google Scholar
Simon, H. A. 1996. The Sciences of the Artificial, 3rd edn. M.I.T. Press.Google Scholar
Streete, T. 1661. Astronomia Carolina: A New Theorie of the Coelestial Motions. Lodowick Lloyd.Google Scholar
Vriend, N. J. 2000. An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. Journal of Economic Dynamics & Control 24, 1–19.CrossRefGoogle Scholar
Watson, J. D., Crick, F. H. C. 1953. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature, no. 4357 (April 25) 737–738.CrossRefGoogle ScholarPubMed
Wellman, M. P., Greenwald, A., Stone, P. 2007. Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition. M.I.T. Press.CrossRefGoogle Scholar
Wooldridge, M., Jennings, N. R. 1995. Intelligent agents: theory and practice. Knowledge Engineering Review 10, 115–152.CrossRefGoogle Scholar