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Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies

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  • Ludo Waltman

    ()

  • Nees Eck

    ()

  • Rommert Dekker

    ()

  • Uzay Kaymak

    ()

Abstract

We are concerned with evolutionary algorithms that are employed for economic modeling purposes. We focus in particular on evolutionary algorithms that use a binary encoding of strategies. These algorithms, commonly referred to as genetic algorithms, are popular in agent-based computational economics research. In many studies, however, there is no clear reason for the use of a binary encoding of strategies. We therefore examine to what extent the use of such an encoding may influence the results produced by an evolutionary algorithm. It turns out that the use of a binary encoding can have quite significant effects. Since these effects do not have a meaningful economic interpretation, they should be regarded as artifacts. Our findings indicate that in general the use of a binary encoding is undesirable. They also highlight the importance of employing evolutionary algorithms with a sensible economic interpretation.
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Suggested Citation

  • Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
  • Handle: RePEc:spr:joevec:v:21:y:2011:i:5:p:737-756
    DOI: 10.1007/s00191-010-0177-1
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Michael Maschek, 2016. "Economic Modeling Using Evolutionary Algorithms: The Influence of Mutation on the Premature Convergence Effect," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 297-319, February.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    3. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(07), pages 1795-1825, October.
    4. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.

    More about this item

    Keywords

    Agent-based computational economics; Evolutionary algorithm; Genetic algorithm; Binary encoding; Premature convergence; C63; C73; D43; D83;

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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