Genetic programming in the overlapping generations model: An illustration with the dynamics of the inflation rate

  • Shu-Heng Chen
  • Chia-Hsuan Yeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1447)


In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto-inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, and GP control parameters.


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  1. 1.
    Allais, M. (1947), “Economie et Interet,” Imprimerie Nationale, Paris.Google Scholar
  2. 2.
    Arifovic, J. (1995), “Genetic Algorithms and Inflationary Economies,” Journal of Monetary Economies, 36, pp. 219–243.Google Scholar
  3. 3.
    Arifovic, J. (1996), “The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies,” Journal of Political Economy, Vol. 104, No. 3, pp. 510–541.Google Scholar
  4. 4.
    Bullard, J. and J. Duffy (1994), “Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs,” mimeo, Federal Reserve Bank of St. Louis and University of Pittsburgh.Google Scholar
  5. 5.
    Chen, S.-H. and C.-H. Yeh (1996), “Genetic Programming Learning in the Cobweb Model,” in P. J. Angeline and K. E. Kinnear (eds.), Advances in Genetic Programming, Vol. II, MIT Press. pp. 443–466.Google Scholar
  6. 6.
    Chen, S.-H. and C.-H. Yeh (1998), “Modeling the Expectations of Inflation in the OLG model with Genetic Programming,” AI-ECON Research Group Working Paper Series No. 9801, Department of Economics, National Chengchi University.Google Scholar
  7. 7.
    Koza, J. R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge: MIT Press.Google Scholar
  8. 8.
    Lucas, R. E., Jr., (1986), “Adaptive Behavior and Economic Theory,” Journal of Business, 59, pp. 401–426.Google Scholar
  9. 9.
    Marimon, R. and S. Sunder (1994), “Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach,” Economic Theory, 4, pp. 131–162.Google Scholar
  10. 10.
    Samuelson, P. A. (1958), “An Exact Consumption-Loan Model of Interest with or without the Social Contrivance of Money,” Journal of Political Economy, Vol. 66, No. 6, pp. 467–482.Google Scholar
  11. 11.
    Tesfatsion, L. (1996), “How Economists Can Get Alife,” in B. Arthur, S. Durlauf and D. Lane (eds.), The Economy as an Evolving Complex Systems, II, Santa Fe Institute in the Science of Complexity, Vol. XXVII, Addison-Wesley, Reading, MA. pp. 533–564.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Shu-Heng Chen
    • 1
  • Chia-Hsuan Yeh
    • 1
  1. 1.AI-ECON Research Group Department of EconomicsNational Chengchi UniversityTaipeiTaiwan

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