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Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies

Author

Listed:
  • Waltman, L.
  • van Eck, N.J.P.
  • Dekker, R.
  • Kaymak, U.
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.

Suggested Citation

  • Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:16014
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    Cited by:

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    2. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1795-1825, October.
    3. 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.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    5. Chernomaz, K. & Goertz, J.M.M., 2023. "(A)symmetric equilibria and adaptive learning dynamics in small-committee voting," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    6. Zeng, Weijun & Ai, Hongfeng & Zhao, Man, 2019. "Asymmetrical expectations of future interaction and cooperation in the iterated prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 148-164.
    7. Michael K. Maschek, 2015. "Particle Swarm Optimization in Agent‐Based Economic Simulations of the Cournot Market Model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 133-152, April.

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    More about this item

    Keywords

    agent-based computational economics; binary encoding; evolutionary algorithm; genetic algorithm; premature convergence;
    All these keywords.

    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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