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Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs

  • Bullard, James
  • Duffy, John

We study a general equilibrium system where agents have heterogeneous beliefs concerning realizations of possible outcomes. The actual outcomes feed back into beliefs thus creating a complicated nonlinear system. Beliefs are updated via a genetic algorithm learning process which we interpret as representing communication among agents in the economy. We are able to illustrate a simple principle: genetic algorithms can be implemented so that they represent pure learning effects (i.e., beliefs updating based on realizations of endogenous variables in an environment with heterogeneous beliefs). Agents optimally solve their maximization problem at each date given their beliefs at each date. We report the results of a set of computational experiments in which we find that our population of artificial adaptive agents is usually able to coordinate their beliefs so as to achieve the Pareto superior rational expectations equilibrium of the model. Citation Copyright 1999 by Kluwer Academic Publishers.

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Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 13 (1999)
Issue (Month): 1 (February)
Pages: 41-60

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Handle: RePEc:kap:compec:v:13:y:1999:i:1:p:41-60
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  1. Thomas J. Sargent & Neil Wallace, 1981. "Some unpleasant monetarist arithmetic," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  2. Routledge, Bryan R, 1999. "Adaptive Learning in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1165-1202.
  3. Arifovic, Jasmina & Eaton, Curtis, 1995. "Coordination via Genetic Learning," Computational Economics, Society for Computational Economics, vol. 8(3), pages 181-203, August.
  4. James B. Bullard & John Duffy, 1994. "A model of learning and emulation with artificial adaptive agents," Working Papers 1994-014, Federal Reserve Bank of St. Louis.
  5. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
  6. Marimon, R. & Sunder, S., 1993. "Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach," Papers 189, Cambridge - Risk, Information & Quantity Signals.
  7. Arifovic, Jasmina & Bullard, James & Duffy, John, 1997. " The Transition from Stagnation to Growth: An Adaptive Learning Approach," Journal of Economic Growth, Springer, vol. 2(2), pages 185-209, July.
  8. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  9. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
  10. James B. Bullard, 1991. "Learning equilibria," Working Papers 1991-004, Federal Reserve Bank of St. Louis.
  11. James B. Bullard & John Duffy, 1995. "On learning and the stability of cycles," Working Papers 1995-006, Federal Reserve Bank of St. Louis.
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