Estimation of a Simple Genetic Algorithm Applied to a Laboratory Experiment

  • Simone Alfarano
  • Eva Camacho
  • Josep Domenech
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 77)

Abstract

The aim of our contribution relies on studying the possibility of implementing a genetic algorithm in order to reproduce some characteristics of a simple laboratory experiment with human subjects. The novelty of our paper regards the estimation of the key-parameters of the algorithm, and the analysis of the characteristics of the estimator.

Keywords

Experiments Genetic algorithm Bounded rationality Estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arifovic, J.: Genetic algorithm learning and the cobweb model. J. Econom. Dynam. Control 18(1), 3–28 (1994)MATHCrossRefGoogle Scholar
  2. 2.
    Arifovic, J.: The behavior of the exchange rate in the genetic algorithm and experimental economies. J. Political Economy 104(3), 510–541 (1996)CrossRefGoogle Scholar
  3. 3.
    Aumann, R.J.: Rationality and Bounded Rationality. Games Econ. Behav. 21, 2–14 (1997)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Casari, M.: Can Genetic Algorithms Explain Experimental Anomalies? Comput. Econ. 24(3), 257–275 (2004)MATHCrossRefGoogle Scholar
  5. 5.
    Camacho, E., Requate, T.: The Regulation of Non-Point Source Pollution and Risk Preferences: An Experimental Approach. Mimeo (2010)Google Scholar
  6. 6.
    Dawid, H.: On the convergence of genetic learning in a double auction market. J. Econom. Dynam. Control 23(9-10), 1545–1569 (1999)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Duffy, J.: Agent-Based Models and Human Subject Experiments. In: Tesfatsion, L., Judd, K.L. (eds.) Handbooks of Computational Economics. Handbooks in Economic Series, vol. 2. Elsevier, Amsterdam (2006)Google Scholar
  8. 8.
    Hansen, L.G.: A Damage Based Tax Mechanism for Regulation of Non-Point Emissions. Environ. Resource Econom. 12(1), 99–112 (1998)CrossRefGoogle Scholar
  9. 9.
    Lux, T., Schornstein, S.: Genetic learning as an explanation of stylized facts of foreign exchange markets. J. Math. Econ. 41(1-2), 169–196 (2005)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Lux, T., Hommes, C.: Individual Expectations and Aggregate Behavior in Learning to Forecast Experiments. Kiel Working Papers, 1466. Kiel Institute for the World Economy (2008)Google Scholar
  11. 11.
    Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Comput. J. 7(4), 308–313 (1965)MATHGoogle Scholar
  12. 12.
    Segerson, K.: Uncertainty and incentives for nonpoint pollution control. J. Environ. Econ. Manage. 15(1), 87–98 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Simone Alfarano
    • 1
  • Eva Camacho
    • 1
  • Josep Domenech
    • 2
  1. 1.Departamento de EconomiaUniversitat Jaume ICastellónSpain
  2. 2.Dpto. de Economía y Ciencias SocialesUniversidad Politecnica de ValenciaValenciaSpain

Personalised recommendations