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Learning And The Stability Of Cycles

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  • Bullard, James
  • Duffy, John

Abstract

We investigate the extent to which agents can learn to coordinate onstationary perfect-foresight cycles in a general-equilibrium environment.Depending on the value of a preference parameter, the limiting backward(direction of time reversed) perfect-foresight dynamics are characterized bysteady-state, periodic, or chaotic trajectories for real money balances. We relax the perfect-foresight assumption and examine how a population ofartificial, heterogeneous adaptive agents might learn in such anenvironment. These artificial agents optimize given their forecasts offuture prices, and they use forecast rules that are consistent with steady-state or periodic trajectories for prices. The agents forecast rules areupdated by a genetic algorithm. We find that the population of artificialadaptive agents is able eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that existunder the perfect-foresight assumption.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Macroeconomic Dynamics.

Volume (Year): 2 (1998)
Issue (Month): 01 (March)
Pages: 22-48

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Handle: RePEc:cup:macdyn:v:2:y:1998:i:01:p:22-48_00

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Cited by:
  1. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2006. "More hedging instruments may destabilize markets," CeNDEF Working Papers 06-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  2. Stefano Eusepi, 2005. "Comparing forecast-based and backward-looking Taylor rules: a "global" analysis," Staff Reports 198, Federal Reserve Bank of New York.
  3. Barnett, Richard C. & Bhattacharya, Joydeep & Bunzel, Helle, 2010. "Resurrecting Equilibria Through Cycles in an Overlapping Generations Model of Money," Staff General Research Papers 32099, Iowa State University, Department of Economics.
  4. Bunzel, Helle, 2006. "Habit Persistence, Money, and Overlapping Generations," Staff General Research Papers 12405, Iowa State University, Department of Economics.
  5. Richard C. Barnett & Joydeep Bhattacharya & Helle Bunzel, 2007. "Resurrecting Equilibria Through Cycles," Economics Working Papers 2007-12, School of Economics and Management, University of Aarhus.
  6. Eusepi, Stefano, 2007. "Learnability and monetary policy: A global perspective," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1115-1131, May.
  7. Arifovic, Jasmina & Gencay, Ramazan, 2000. "Statistical properties of genetic learning in a model of exchange rate," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 981-1005, June.
  8. 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.
  9. Arifovic, Jasmina, 2001. "Evolutionary dynamics of currency substitution," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 395-417, March.
  10. Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(05), pages 625-655, November.
  11. Goeree, Jacob K. & Hommes, Cars H., 2000. "Heterogeneous beliefs and the non-linear cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 761-798, June.
  12. Barnett, Richard C. & Bhattacharya, Joydeep & Bunzel, Helle, 2007. "Minimum Consumption Requirements and Cycles in an Overlapping Generations Model of Money," Staff General Research Papers 12834, Iowa State University, Department of Economics.
  13. Negroni, Giorgio, 2005. "Eductive expectations coordination on deterministic cycles in an economy with identical fundamentals," Journal of Economic Behavior & Organization, Elsevier, vol. 58(3), pages 420-443, November.
  14. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.

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