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Using genetic algorithms to model the evolution of heterogeneous beliefs

  • James Bullard
  • John Duffy

Genetic algorithms have been used by economists to model the process by which a population of heterogeneous agents learn how to optimize a given objective. However, most general equilibrium models in use today presume that agents already know how to optimize. If agents face any uncertainty, it is typically with regard to their expectations about the future. In this paper, we show how a genetic algorithm can be used to model the process by which a population of agents with heterogeneous beliefs learns how to form rational expectation forecasts. We retain the assumption that agents optimally solve their maximization problem at each date given their beliefs at each date. Agents initially lack the ability to form rational expectations forecasts and have, instead, heterogeneous beliefs about the future. Using a genetic algorithm to model the evolution of these beliefs, we find that our population of artificial adaptive agents eventually coordinates their beliefs so as to achieve a rational expectations equilibrium of the model. We also report the results of a number of computational experiments that were performed using our genetic algorithm model.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1994-028.

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Date of creation: 1994
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Publication status: Published in Computational Economics, February 1999, 13(1), pp. 41-60
Handle: RePEc:fip:fedlwp:1994-028
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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. Marimon, R. & Sunder, S., 1993. "Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach," Papers 189, Cambridge - Risk, Information & Quantity Signals.
  3. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
  4. Arifovic, Jasmina & Eaton, Curtis, 1995. "Coordination via Genetic Learning," Computational Economics, Society for Computational Economics, vol. 8(3), pages 181-203, August.
  5. Routledge, Bryan R, 1999. "Adaptive Learning in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1165-1202.
  6. 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.
  7. James Bullard, 1991. "Learning equilibria," Working Papers 1991-004, Federal Reserve Bank of St. Louis.
  8. James Bullard & John Duffy, 1994. "A model of learning and emulation with artificial adaptive agents," Working Papers 1994-014, Federal Reserve Bank of St. Louis.
  9. 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.
  10. James Bullard & John Duffy, 1995. "On learning and the stability of cycles," Working Papers 1995-006, Federal Reserve Bank of St. Louis.
  11. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
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