Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-22T14:27:42.320Z Has data issue: false hasContentIssue false

Agent-based computational economics: a short introduction

Published online by Cambridge University Press:  26 April 2012

Matteo G. Richiardi*
Affiliation:
Department of Economics, University of Turin, via Po 53, 10124 Italy Collegio Carlo Alberto - LABORatorio Revelli, via Real Collegio 30, 10024 Moncalieri, Italy; e-mail: [email protected]

Abstract

In this paper we provide a brief overview of the main characteristics of agent-based computational economics. We discuss its points of strength, with respect to analytical models, and its weaknesses. The latter are mainly related to how the results of a simulation model can be interpreted, and how the structural parameters of the model can be estimated. We then show how these problems can be dealt with.

Type
Articles
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.)

References

Anderson, P. W. 1972. More is different. Science 1770(4047), 393396.Google Scholar
Anderson, P. W., Arrow, K. J., Pines, D. (eds) 1988. The Economy as an Evolving Complex System. SFI Studies in the Sciences of Complexity. Addison-Wesley Longman.Google Scholar
Arthur, W. B. 1990. Emergent Structures. A Newsletter of the Economic Research Program. The Santa Fe Institute.Google Scholar
Arthur, W. B. 2006. Out-of-equilibrium economics and agent-based modeling. In Handbook of Computational Economics, 2: Agent-Based Computational Economics of Handbook in Economics 13, Tesfatsion L. & Judd K. L. (eds). North-Holland, Ch. 32, 1551–1564.Google Scholar
Arthur, W. B., Durlauf, S. N., Lane, D. A. (eds) 1997. The Economy as an Evolving Complex System II. Addison-Wesley Longman.Google Scholar
Askenazi, M., Burkhart, R., Langton, C., Minar, N. 1996. The Swarm Simulation System: A Toolkit for Building Multi-agent Simulations. Santa Fe Institute Working Paper no. 96-06-042.Google Scholar
Axtell, R. L. 2000. Why agents? on the varied motivations for agent computing in the social sciences. In Proceedings of the Workshop on Agent Simulation: Applications, Models and Tools, Argonne National Laboratory, Argonne, IL.Google Scholar
Blume, L. E., Durlauf, S. N. (eds) 2006. The Economy as an Evolving Complex System, III. Current Perspectives and Future Directions. Santa Fe Institute in the Science of Complexity. Santa Fe Institute in the Science of Complexity, Oxford University Press.Google Scholar
Chu, D., Strand, R., Fjelland, R. 2003. Theories of complexity. Complexity 8(3), 1930. URL http://dx.doi.org/10.1002/cplx.10059.Google Scholar
Conlisk, J. 1996. Why bounded rationality. Journal of Economic Literature 34(2), 669700.Google Scholar
Edmonds, B. 1999. The evolution of complexity. In What is Complexity?—The Philosophy of Complexity per se with Application to Some Examples in Evolution, Heylighen, F. & Aerts, D. (eds). Kluwer.Google Scholar
Epstein, J. M. 1999. Agent-based computational models and generative social science. Complexity 4(5), 4160.3.0.CO;2-F>CrossRefGoogle Scholar
Epstein, J. M. 2006. Remarks on the foundations of agent-based generative social science. In Handbook of Computational Economics, 2: Agent-Based Computational Economics of Handbook in Economics 13, Tesfatsion L. & Judd K. L. (eds). North-Holland.Google Scholar
Epstein, J. M., Axtell, R. L. 1996. Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press.Google Scholar
Fagiolo, G., Moneta, A., Windrum, P. 2007. A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Computational Economics 30(3), 195226.Google Scholar
Farmer, J. D., Foley, D. 2009. The economy needs agent-based modelling. Nature 460, 685686.Google Scholar
Feyerabend, P. 1975. Against Method. Verso.Google Scholar
Gigerenzer, G., Selten, R. (eds) 1997. Bounded Rationality: The Adaptive Toolbox. The MIT Press.Google Scholar
Gilbert, N., Troitzsch, K. G. 1999. Simulation for the Social Scientist. Open University Press.Google Scholar
Gleick, J. 1987. Chaos: Making a New Science. Penguin Books.Google Scholar
Gleick, J. 1992. Genius: The Life and Science of Richard Feynman. Pantheon.Google Scholar
Gourieroux, C., Monfort, A. 1997. Simulation-Based Econometric Methods. Oup/Core Lecture Series. Oxford University Press.Google Scholar
Grazzini, J. 2012. Analysis of the emergent properties: Stationarity and ergodicity Journal of Artificial Societies and Social Simulation, forthcoming.Google Scholar
Haken, H. 1983. “Synergetics”. Non-equilibrium Phase Transitions and Social Measurement, 3rd edn. Springer-Verlag.Google Scholar
Hodgson, G. M. 2007. Meanings of methodological individualism. Journal of Economic Methodology 14(2), 211226.CrossRefGoogle Scholar
Horgan, J. 1995. From complexity to perplexity. Scientific American 272(6), 7479.Google Scholar
Horgan, J. 1997. The End of Science: Facing the Limits of Knowledge in the Twilight of the Scientific Age. Broadway Books.Google Scholar
Jones, R. H. 2000. Reductionism: Analysis and the Fullness of Reality. Associated University Press.Google Scholar
Kleijnen, J. P. C. 1998. Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation, Banks, J. (eds). Wiley, Ch. 6, 173223.CrossRefGoogle Scholar
Koppl, R. 2000. Policy implications of complexity: an Austrian perspective. In The Complexity Vision and the Teaching of Economics, Colander, D. (ed.). Edward Elgar, 97117.Google Scholar
Leombruni, R., Richiardi, M. G. 2005. Why are economists sceptical about agent-based simulations? Physica A 355(1), 103109.Google Scholar
Manson, S. M. 2006. Bounded rationality in agent-based models: experiments with evolutionary programs. International Journal of Geographical Information Science 9(20), 9911012.Google Scholar
Marks, R. E. 2007. Validating simulation models: a general framework and four applied examples. Computatational Economics 30(3), 265290.Google Scholar
Miller, J. H., Page, S. E. 2006. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.Google Scholar
Nicolis, G., Prigogine, I. 1989. Exploring Complexity: An Introduction. Springer-Verlag.Google Scholar
Ostrom, T. M. 1988. Computer simulation: the third symbol system. Journal of Experimental Social Psychology 24(5), 381392.Google Scholar
Phelan, S. E. 2001. What is complexity science, really? Emergence 3(1), 120136.Google Scholar
Prigogine, I., Stengers, I. 1984. Order out of Chaos: Man's New Dialogue with Nature. Bantam Books.Google Scholar
Pyka, A., Fagiolo, G. 2007. Agent-based modelling: a methodology for neo-schumpetarian economics. In Elgar Companion To Neo-Schumpeterian Economics, Hanusch, H. & Pyka, A. (eds). Ch. 29. Edward Elgar, 467487.Google Scholar
Resnick, M. 1994. Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds. The MIT Press.Google Scholar
Richiardi, M. G., Leombruni, R., Saam, N., Sonnessa, M. 2006. A common protocol for agent-based social simulation. Journal of Artificial Societies and Social Simulations 9(1), Article 15, http://jasss.soc.surrey.ac.uk/9/1/15.html.Google Scholar
Rosser, J. B. Jr. 1999. On the complexities of complex economic dynamics. The Journal of Economic Perspectives 13(4), 169192.Google Scholar
Rosser, J. B. Jr. 2010. How complex are the Austrians? In What is so Austrian about Austrian Economics?, Koppl, R., Howitz, S. & Desrochers, P. (eds). Advances in Austrian Economics 14, 165180, Emerald Books.Google Scholar
Schelling, T. 1971. Dynamic models of segregration. Journal of Mathematical Sociology 1, 143186.Google Scholar
Schelling, T. C. 2006. Some fun, thirty-five years ago. In Handbook of Computational Economics, 2: Agent-Based Computational Economics of Handbook in Economics 13, Tesfatsion L. & Judd K. L. (eds). North-Holland, Ch. 37, 1639–1644.Google Scholar
Schumpeter, J. 1909. On the concept of social value. The Quarterly Journal of Economics 23(2), 213232.Google Scholar
Smith, A. 2008. Indirect inference. In The New Palgrave Dictionary of Economics, 2nd edn, Durlauf, S. & Blume, L. (eds). Palgrave Macmillan.Google Scholar
Tesfatsion, L., Judd, K. L. (eds) 2006. Handbook of Computational Economics, 2: Agent-Based Computational Economics of Handbook in Economics 13. North-Holland.Google Scholar
Tesfatsion, L. 2006. Agent-based computational economics: a constructive approach to economic theory. In Handbook of Computational Economics, 2: Agent-Based Computational Economics of Handbook in Economics 13, Tesfatsion L. & Judd K. L. (eds). North-Holland, Ch. 16, 831–880.Google Scholar
Vaughn, K. 1999. Hayek's theory of the market order as an instance of the theory of complex adaptive systems. Journal des economists et des études humaines 9, 241256.Google Scholar
von Hayek, F. A. 1948. Individualism and Economic Order. University of Chicago Press.Google Scholar
von Hayek, F. A. 1967. The theory of complex phenomena. In Studies in Philosophy, Politics, and Economics, University of Chicago Press, 2242.Google Scholar
Vriend, N. J. 2002. Was Hayek an ACE? Southern Economic Journal 68(4), 811840.Google Scholar
Waldrop, M. M. 1992. Complexity: The Emerging Science at the Edge of Order and Chaos. Touchstone.Google Scholar
Weber, M. 1922 (1968). Economy and Society. University of California Press.Google Scholar
Winker, P., Gilli, M., Jeleskovic, V. 2007. An objective function for simulation based inference on exchange rate data. Journal of Economic Interaction and Coordination 2, 125145.Google Scholar