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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 on stationary 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 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 eventually to coordinate on steady state and low-order cycles, but not on the higher-order periodic equilibria that exist under the perfect-foresight assumption.

Suggested Citation

  • Bullard, James & Duffy, John, 1998. "Learning And The Stability Of Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 2(1), pages 22-48, March.
    • Handle: RePEc:cup:macdyn:v:2:y:1998:i:01:p:22-48_00
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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.
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    Cited by:

    1. Hommes, Cars & Huber, Stefanie J. & Minina, Daria & Salle, Isabelle, 2024. "Learning in a complex world: Insights from an OLG lab experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 220(C), pages 813-837.
    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. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    4. Eusepi, Stefano, 2007. "Learnability and monetary policy: A global perspective," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1115-1131, May.
    5. Richard C. Barnett & Joydeep Bhattacharya & Helle Bunzel, 2007. "Resurrecting Equilibria Through Cycles," Economics Working Papers 2007-12, Department of Economics and Business Economics, Aarhus University.
    6. Jasmina Arifovic & James B. Bullard & John Duffy, 1995. "Learning in a model of economic growth and development," Working Papers 1995-017, Federal Reserve Bank of St. Louis.
    7. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    8. Negroni, Giorgio, 2005. "Eductive expectations coordination on deterministic cycles in an economy with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 29(5), pages 931-952, May.
    9. Bunzel, Helle, 2006. "Habit persistence, money, and overlapping generations," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2425-2445, December.
    10. Arifovic, Jasmina, 2001. "Evolutionary dynamics of currency substitution," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 395-417, March.
    11. Barnett, Richard C. & Bhattacharya, Joydeep & Bunzel, Helle, 2010. "Resurrecting equilibria through cycles in an overlapping generations model of money," Journal of Macroeconomics, Elsevier, vol. 32(2), pages 515-526, June.
    12. Vinícius Ferraz & Thomas Pitz, 2024. "Analyzing the Impact of Strategic Behavior in an Evolutionary Learning Model Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 437-475, February.
    13. 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.
    14. Arifovic, Jasmina & Hommes, Cars & Salle, Isabelle, 2019. "Learning to believe in simple equilibria in a complex OLG economy - evidence from the lab," Journal of Economic Theory, Elsevier, vol. 183(C), pages 106-182.
    15. 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.
    16. 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.
    17. Barnett, Richard C. & Bhattacharya, Joydeep & Bunzel, Helle, 2007. "Minimum Consumption Requirements and Cycles in an Overlapping Generations Model of Money," Staff General Research Papers Archive 12834, Iowa State University, Department of Economics.
    18. 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.
    19. Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 625-655, November.
    20. 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.
    21. 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.

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