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On economic applications of the genetic algorithm: a model of the cobweb type

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  • Michael Kopel

    (Department of Managerial Economics and Industrial Organization, Vienna University of Technology, Wien, Austria)

  • Herbert Dawid

    ()
    (Department of Management Science, University of Vienna, Br, nnerstrasse 72, A-1210 Wien, Austria)

Abstract

This paper explores the idea of using artificial adaptive agents in economic theory. In particular, we use Genetic Algorithms (GAs) to model the learning behavior of a population of adaptive and boundedly rational agents interacting in an economic system. We analyze the behavior of a GA in two versions of a model of the cobweb-type, one in which firms make only quantity choices, and the other one in which firms first decide to exit or to stay in the market, and subsequently decide how much to produce. We present simulations with different coding schemes and interpret the rather surprising differences between the results for different setups by employing the mathematical theory for GAs with state-dependent fitness functions. In particular, we explain the relationship between coding and convergence properties of GAs.

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Bibliographic Info

Article provided by Springer in its journal Journal of Evolutionary Economics.

Volume (Year): 8 (1998)
Issue (Month): 3 ()
Pages: 297-315

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Handle: RePEc:spr:joevec:v:8:y:1998:i:3:p:297-315

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Related research

Keywords: Cobweb model ; Genetic algorithms ; Learning ; Artificial economic agents;

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Cited by:
  1. Neugart, Michael & Tuinstra, Jan, 2001. "Endogenous fluctuations in the demand for education," Discussion Papers, Research Unit: Labor Market Policy and Employment FS I 01-209, Social Science Research Center Berlin (WZB).
  2. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  3. Smith, Peter, 2004. "Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System," General Discussion Papers 30569, University of Manchester, Institute for Development Policy and Management (IDPM).
  4. Karolina SafarzyƄska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
  5. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO).
  6. Mattheos Protopapas & Francesco Battaglia & Elias Kosmatopoulo, 2008. "Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games," Working Papers 004, COMISEF.
  7. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
  8. Domenico Colucci & Vincenzo Valori, 2011. "Can Endogenous Participation Explain Price Volatility? Evidence from an Agent-Based Cobweb Model," Computational Economics, Society for Computational Economics, vol. 38(3), pages 425-437, October.
  9. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Beyond replicator dynamics: Innovation-selection dynamics and optimal diversity," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 229-245, May.

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