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Genetic Algorithms and Economic Evolution


  • Thomas Riechmann

    () (University of Hannover)


This paper tries to connect the theory of genetic-algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria, which, during the learning process, build up finally to reach a neighborhood of an evolutionarily stable state. In order to clarify this point, a concept of evolutionary stability of genetic populations is developed. It then becomes possible to explain the reasons for the dynamics of standard GA learning models as well as those of extensions to this standard.

Suggested Citation

  • Thomas Riechmann, 1999. "Genetic Algorithms and Economic Evolution," Computing in Economics and Finance 1999 1011, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:1011

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    References listed on IDEAS

    1. Evans, Martin D D & Lewis, Karen K, 1995. " Do Expected Shifts in Inflation Affect Estimates of the Long-Run Fisher Relation?," Journal of Finance, American Finance Association, vol. 50(1), pages 225-253, March.
    2. Hauser, Michael A & Kunst, Robert M, 1998. "Fractionally Integrated Models with ARCH Errors: With an Application to the Swiss One-Month Euromarket Interest Rate," Review of Quantitative Finance and Accounting, Springer, vol. 10(1), pages 95-113, January.
    3. Peter C.B. Phillips, 1999. "Discrete Fourier Transforms of Fractional Processes," Cowles Foundation Discussion Papers 1243, Cowles Foundation for Research in Economics, Yale University.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Crato, Nuno & Rothman, Philip, 1994. "Fractional integration analysis of long-run behavior for US macroeconomic time series," Economics Letters, Elsevier, vol. 45(3), pages 287-291.
    6. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
    7. Perron, Pierre & Vogelsang, Timothy J, 1992. "Nonstationarity and Level Shifts with an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 301-320, July.
    8. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    9. Clemente, Jesus & Montanes, Antonio & Reyes, Marcelo, 1998. "Testing for a unit root in variables with a double change in the mean," Economics Letters, Elsevier, vol. 59(2), pages 175-182, May.
    10. Christopher F. Baum & John T. Barkoulas & Mustafa Caglayan, 1999. "Persistence in International Inflation Rates," Southern Economic Journal, Southern Economic Association, vol. 65(4), pages 900-913, April.
    11. Chang Sik Kim & Peter C.B. Phillips, 2006. "Log Periodogram Regression: The Nonstationary Case," Cowles Foundation Discussion Papers 1587, Cowles Foundation for Research in Economics, Yale University.
    12. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    13. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    14. Hassler, Uwe & Wolters, Jurgen, 1994. "On the power of unit root tests against fractional alternatives," Economics Letters, Elsevier, vol. 45(1), pages 1-5, May.
    15. Sowell, Fallaw, 1990. "The Fractional Unit Root Distribution," Econometrica, Econometric Society, vol. 58(2), pages 495-505, March.
    16. Perron, Pierre, 1990. "Testing for a Unit Root in a Time Series with a Changing Mean," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 153-162, April.
    17. Phillips, Peter C.B., 2007. "Unit root log periodogram regression," Journal of Econometrics, Elsevier, vol. 138(1), pages 104-124, May.
    18. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
    19. Diebold, Francis X. & Rudebusch, Glenn D., 1991. "On the power of Dickey-Fuller tests against fractional alternatives," Economics Letters, Elsevier, vol. 35(2), pages 155-160, February.
    20. Michael Dueker & Richard Startz, 1998. "Maximum-Likelihood Estimation Of Fractional Cointegration With An Application To U.S. And Canadian Bond Rates," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 420-426, August.
    21. Crowder, William J & Hoffman, Dennis L, 1996. "The Long-Run Relationship between Nominal Interest Rates and Inflation: The Fisher Equation Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(1), pages 102-118, February.
    22. Fama, Eugene F, 1975. "Short-Term Interest Rates as Predictors of Inflation," American Economic Review, American Economic Association, vol. 65(3), pages 269-282, June.
    23. Mishkin, Frederic S., 1992. "Is the Fisher effect for real? : A reexamination of the relationship between inflation and interest rates," Journal of Monetary Economics, Elsevier, vol. 30(2), pages 195-215, November.
    24. Cochrane, John H., 1991. "A critique of the application of unit root tests," Journal of Economic Dynamics and Control, Elsevier, vol. 15(2), pages 275-284, April.
    25. Ng, S. & Perron, P., 1994. "Unit Root Tests ARMA Models with Data Dependent Methods for the Selection of the Truncation Lag," Cahiers de recherche 9423, Universite de Montreal, Departement de sciences economiques.
    26. Gonzalo, Jesus & Lee, Tae-Hwy, 1998. "Pitfalls in testing for long run relationships," Journal of Econometrics, Elsevier, vol. 86(1), pages 129-154, June.
    27. Christopher F. Baum & Vince Wiggins, 2001. "Tests for long memory in a time series," Stata Technical Bulletin, StataCorp LP, vol. 10(57).
    28. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    29. Peter C.B. Phillips, 1998. "Econometric Analysis of Fisher's Equation," Cowles Foundation Discussion Papers 1180, Cowles Foundation for Research in Economics, Yale University.
    30. Wu, Yangru & Zhang, Hua, 1996. "Mean Reversion in Interest Rates: New Evidence from a Panel of OECD Countries," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(4), pages 604-621, November.
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    Cited by:

    1. Boldea Bogdan Ion & Boldea Costin-Radu & Stanculescu Mircea, 2009. "An Adaptative Evolutionary Model Of Financial Investors," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 4(1), pages 897-901, May.
    2. Jie-Shin Lin & Chris Birchenhall, 2000. "Learning And Adaptive Artificial Agents: An Analysis Of Evolutionary Economic Models," Computing in Economics and Finance 2000 327, Society for Computational Economics.

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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