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On learning and the stability of cycles

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Author Info
James Bullard
John Duffy

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Abstract

We study a general equilibrium model where the multiplicity of stationary periodic perfect foresight equilibria is pervasive. We investigate the extent of which agents can learn to coordinate on stationary perfect foresight cycles. The example economy, taken from Grandmont (1985), is an endowment overlapping generations model with fiat money, where consumption in the first and second periods of life are not necessarily gross substitutes. Depending on the value of a preference parameter, the limiting backward (direction of time reversed) perfect foresight dynamics are characterized by steady state, periodic, or chaotic trajectories for real money balances. We relax the perfect foresight assumption and examine how a population of artificial, heterogeneous adaptive agents might learn in such an environment. These artificial agents optimize given their forecasts of future prices, and they use forecast rules that are consistent with steady state or periodic trajectories for prices. The agents' forecast rules are updated by a genetic algorithm. We find that the population of artificial adaptive agents is able to eventually coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect foresight assumption.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1995-006.

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Date of creation: 1995
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Publication status: Published in Macroeconomic Dynamics, v. 2, no. 1 (March 1998) pp. 22-48
Handle: RePEc:fip:fedlwp:1995-006

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Keywords: Business cycles;

References listed on IDEAS
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  1. 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. [Downloadable!] (restricted)
  2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January. [Downloadable!] (restricted)
  3. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July. [Downloadable!] (restricted)
  4. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Shu-Heng Chen, John Duffy, Chia-Hsuan Yeh, . "Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game," The Electronic Journal of Evolutionary Modeling and Economic Dynamics, IFReDE - Université Montesquieu Bordeaux IV. [Downloadable!]
  2. James Bullard & Jasmina Arifovic & John Duffy, 1995. "Learning in a model of economic growth and development," Working Papers 1995-017, Federal Reserve Bank of St. Louis. [Downloadable!]
  3. Stefano Eusepi, 2005. "Comparing forecast-based and backward-looking Taylor rules: a "global" analysis," Staff Reports 198, Federal Reserve Bank of New York. [Downloadable!]
  4. William Brock & Cars Hommes & Florian Wagener, 2006. "More Hedging Instruments may destablize Markets," Tinbergen Institute Discussion Papers 06-080/1, Tinbergen Institute, revised 30 Apr 2008. [Downloadable!]
    Other versions:
  5. Barnett, Richard & Bhattacharya, Joydeep & Bunzel, Helle, 2007. "Resurrecting Equilibria Through Cycles," Staff General Research Papers 12834, Iowa State University, Department of Economics. [Downloadable!]
    Other versions:
  6. Shu-Heng Chen & Chia-Hsuan Yeh, 1999. "Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market," Computing in Economics and Finance 1999 613, Society for Computational Economics. [Downloadable!]
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