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Evolutionary Switching between Forecasting Heuristics: An Explanation of an Asset-Pricing Experiment

In: Complexity and Artificial Markets

Author

Listed:
  • Mikhail Anufriev

    (University of Amsterdam)

  • Cars Hommes

    (University of Amsterdam)

Abstract

In this paper we propose an explanation of the findings of a recent laboratory market forecasting experiment. In the experiment the participants were asked to predict prices for 50 periods on the basis of past realizations. Three different aggregate outcomes were observed in an identical environment: slow monotonic price convergence, persistent price oscillations, and oscillatory dampened price fluctuations. Individual predictions exhibited a high degree of coordination, although the individual forecasts were not commonly known. To explain these findings we propose an evolutionary model of reinforcement learning over a set of simple forecasting heuristics. The key element of our model is the switching between heuristics on the basis of their past performance. Simulations show that such evolutionary learning can reproduce the qualitative patterns observed in the experiment.

Suggested Citation

  • Mikhail Anufriev & Cars Hommes, 2008. "Evolutionary Switching between Forecasting Heuristics: An Explanation of an Asset-Pricing Experiment," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 4, pages 41-53, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-70556-7_4
    DOI: 10.1007/978-3-540-70556-7_4
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    Cited by:

    1. Luigi Mittone & Gian Paolo Jesi, 2016. "Heuristic Driven Agents in Tax Evasion: an Agent-based Approach," CEEL Working Papers 1605, Cognitive and Experimental Economics Laboratory, Department of Economics, University of Trento, Italia.

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