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How Economists Can Get Alife: Abbreviated Version


  • Leigh Tesfatsion

    (Iowa State University)


Artificial life (alife) is the bottom-up study of basic phenomena commonly associated with living agents, such as self- replication, evolution, adaptation, self-organization, exploitation, competition, cooperation, and social network formation. Alife complements the traditional biological and social sciences concerned with the analytical, laboratory, and field study of living organisms by attempting to simulate or synthesize these basic phenomena within computers, robots, and other man-made media. One goal is to enhance the understanding of actual and potential life processes. A second goal is to use nature as an inspiration for the development of solution algorithms for difficult optimization problems characterized by high- dimensional search domains, nonlinearities, and/or multiple local optima. This paper presents a brief overview of alife, stressing aspects especially relevant for the study of decentralized market economies. In particular, a recently developed trade network game (TNG) is used to illustrate how the basic alife paradigm might be specialized to economics. This type of research has recently come to be known as agent- based computational economics.

Suggested Citation

  • Leigh Tesfatsion, 1995. "How Economists Can Get Alife: Abbreviated Version," Computational Economics 9501001, EconWPA, revised 21 Jun 1998.
  • Handle: RePEc:wpa:wuwpco:9501001
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    References listed on IDEAS

    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-396, March.
    3. Anderson, Evan W. & McGrattan, Ellen R. & Hansen, Lars Peter & Sargent, Thomas J., 1996. "Mechanics of forming and estimating dynamic linear economies," Handbook of Computational Economics,in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 4, pages 171-252 Elsevier.
    4. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
    5. Ariel Pakes & Paul McGuire, 1997. "Stochastic Algorithms for Dynamic Models: Markov Perfect Equilibrium, and the 'Curse' of Dimensionality," Cowles Foundation Discussion Papers 1144, Cowles Foundation for Research in Economics, Yale University.
    6. Judd, Kenneth L., 1996. "Approximation, perturbation, and projection methods in economic analysis," Handbook of Computational Economics,in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 12, pages 509-585 Elsevier.
    7. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March.
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    More about this item


    Artificial life; agent-based computational economics; evolutionary match-and-play game; trade networks; iterated prisoner's dilemma.;

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D4 - Microeconomics - - Market Structure, Pricing, and Design


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