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Individual Expectations and Aggregate Behavior in Learning to Forecast Experiments

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Author Info
Cars Hommes
Thomas Lux

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Abstract

Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations and individual forecasting behaviour in recent learning to forecast laboratory experiments with human subjects (Hommes et al. 2007), simultaneously and across different treatments

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Publisher Info
Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1466.

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Length: 40 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:kie:kieliw:1466

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Related research
Keywords: Learning; heterogeneous expectations; genetic algorithms; experimental economics;

Find related papers by JEL classification:
C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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References listed on IDEAS
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.:
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    Other versions:
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    Other versions:
  6. Hommes, C.H., 2001. "Modeling the stylized facts in finance through simple nonlinear adaptive systems," CeNDEF Working Papers 01-06, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. [Downloadable!]
  7. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier. [Downloadable!] (restricted)
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  8. Freeman, Richard B, 1975. "Legal "Cobwebs": A Recursive Model of the Market for New Lawyers," The Review of Economics and Statistics, MIT Press, vol. 57(2), pages 171-79, May. [Downloadable!] (restricted)
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  11. Lux, T. & M. Marchesi, . "Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market," Discussion Paper Serie B 438, University of Bonn, Germany, revised Jul 1998.
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  14. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February. [Downloadable!] (restricted)
    Other versions:
  15. Kirman, Alan, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, MIT Press, vol. 108(1), pages 137-56, February. [Downloadable!] (restricted)
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  17. Heemeijer, P. & Hommes, C.H. & Sonnemans, J. & Tuinstra, J., 2006. "Price Stability and Volatility in Markets with Positive and Negative Expectations Feedback: An Experimental Investigation," CeNDEF Working Papers 06-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. [Downloadable!]
    Other versions:
  18. Chavas, Jean-Paul, 2000. "On information and market dynamics: The case of the U.S. beef market," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 833-853, June. [Downloadable!] (restricted)
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    Other versions:
  20. Tesfatsion, Leigh S. & Judd, Kenneth L., 2003. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers 10368, Iowa State University, Department of Economics. [Downloadable!]
  21. Cars Hommes & Joep Sonnemans & Jan Tuinstra & Henk van de Velden, 2005. "Coordination of Expectations in Asset Pricing Experiments," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 18(3), pages 955-980. [Downloadable!] (restricted)
    Other versions:
  22. 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. [Downloadable!] (restricted)
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  28. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
  29. Alan Kirman, 2006. "Heterogeneity in Economics," Journal of Economic Interaction and Coordination, Springer, vol. 1(1), pages 89-117, May. [Downloadable!] (restricted)
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    Other versions:
  31. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer, vol. 24(3), pages 257-275, March. [Downloadable!] (restricted)
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