Skip to main content

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

Abstract

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.

This paper is an abbreviated version of Chen and Yeh (1998). Research support from NSC grant NSC. 86-2415-H-004-022 is gratefully acknowledged. The authors are grateful to David Fogel and one anonymous referee for painstaking reviews and many helpful suggestions. This paper is devoted to the memory of Mr. Paul Lin with Sun Fast International Corp., who had been a great supporter for our research for many years. To many people's grief, he died at the age of 38 on September 30, 1997 of liver cancer.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allais, M. (1947), “Economie et Interet,” Imprimerie Nationale, Paris.

    Google Scholar 

  2. Arifovic, J. (1995), “Genetic Algorithms and Inflationary Economies,” Journal of Monetary Economies, 36, pp. 219–243.

    Google Scholar 

  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. 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. 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. 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. Koza, J. R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge: MIT Press.

    Google Scholar 

  8. Lucas, R. E., Jr., (1986), “Adaptive Behavior and Economic Theory,” Journal of Business, 59, pp. 401–426.

    Google Scholar 

  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. 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. 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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, SH., Yeh, CH. (1998). Genetic programming in the overlapping generations model: An illustration with the dynamics of the inflation rate. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040833

Download citation

  • DOI: https://doi.org/10.1007/BFb0040833

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics