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A model of learning and emulation with artificial adaptive agents

  • James Bullard
  • John Duffy

We study adaptive learning behavior in a sequence of n-period endowment overlapping generations economies with fiat currency, where n refers to the number of periods in agents' lifetimes. Agents initially have heterogeneous beliefs and seek to form multi-step-ahead forecasts of future prices using a forecast rule chosen from a vast set of possible forecast rules. Agents take optimal actions given their forecasts of future prices. They learn in every period by creating new forecast rules and by emulating the forecast rules of other agents. Computational experiments with artificial adaptive agents are conducted. These experiments yield three qualitatively different types of outcomes. In one, the initially heterogeneous population of artificial agents learns to coordinate on a low inflation, stationary perfect foresight equilibrium. In another, we observe persistent currency collapse. The third outcome is a lack of coordination within the allotted time frame. One possible outcome, a stationary perfect foresight equilibrium with a relatively high inflation rate, is never observed.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1994-014.

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Date of creation: 1994
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Publication status: Published in Journal of Economic Dynamics and Control, February 1998
Handle: RePEc:fip:fedlwp:1994-014
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  1. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
  2. Ramon Marimon & Shyam Sunder, 1993. "Indeterminacy of equilibria in a hyperinflationary world: Experimental evidence," Economics Working Papers 25, Department of Economics and Business, Universitat Pompeu Fabra.
  3. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
  4. Binmore, Kenneth G. & Samuelson, Larry, 1992. "Evolutionary stability in repeated games played by finite automata," Journal of Economic Theory, Elsevier, vol. 57(2), pages 278-305, August.
  5. Ho, Teck-Hua, 1996. "Finite automata play repeated prisoner's dilemma with information processing costs," Journal of Economic Dynamics and Control, Elsevier, vol. 20(1-3), pages 173-207.
  6. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
  7. Binmore, K. & Samuelson, L., 1991. "Evolutionary Stability in Repeated games Played by Finite Automata," Papers 90-17, Michigan - Center for Research on Economic & Social Theory.
  8. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
  9. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May.
  10. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July.
  11. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
  12. James Bullard, 1992. "Samuelson's model of money with n-period lifetimes," Review, Federal Reserve Bank of St. Louis, issue May, pages 67-82.
  13. 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.
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