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Using co-evolutionary programming to simulate strategic behaviour in markets

Author

Listed:
  • Tony Curzon Price

    (Energy Policy Group, Imperial College, 48 Princes Gardens, London SW7 2PE, UK)

Abstract

This paper describes the use of a genetic algorithm (GA) to model several standard industrial organisation games: Bertrand and Cournot competition, a vertical chain of monopolies, and a simple model of an electricity pool. The intention is to demonstrate that the GA performs well as a modelling tool in these standard settings, and that evolutionary programming therefore has a potential role in applied work requiring detailed market simulation. The advantages of using a GA over scenario analysis for applied market simulation are outlined. Also explored are the way in which the equilibria discovered by the GA can be interpreted, and what the market analogue for the GA process might be.

Suggested Citation

  • Tony Curzon Price, 1997. "Using co-evolutionary programming to simulate strategic behaviour in markets," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 219-254.
    • Handle: RePEc:spr:joevec:v:7:y:1997:i:3:p:219-254
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    Citations

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    Cited by:

    1. Mattheos Protopapas & Francesco Battaglia & Elias Kosmatopoulo, 2008. "Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games," Working Papers 004, COMISEF.
    2. Graubner, Marten & Sexton, Richard J., 2021. "Spatial competition in agricultural procurement markets," 2021 Annual Meeting, August 1-3, Austin, Texas 313962, Agricultural and Applied Economics Association.
    3. Kellermann, Konrad & Balmann, Alfons, 2006. "How Smart Should Farms Be Modeled? Behavioral Foundation of Bidding Strategies in Agent-Based Land Market Models," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25446, International Association of Agricultural Economists.
    4. Graubner, Marten & Balmann, Alfons & Sexton, Richard J., 2011. "Spatial Pricing and the Location of Processors in Agricultural Markets," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114601, European Association of Agricultural Economists.
    5. Edmund Chattoe-Brown, 1998. "Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-2.
    6. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM) [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    7. repec:zbw:iamodp:109915 is not listed on IDEAS
    8. Ian McCarthy, 2008. "Simulating Sequential Search Models with Genetic Algorithms: Analysis of Price Ceilings, Taxes, Advertising and Welfare," CAEPR Working Papers 2008-010, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    9. Dawid, Herbert, 2000. "On the emergence of exchange and mediation in a production economy," Journal of Economic Behavior & Organization, Elsevier, vol. 41(1), pages 27-53, January.
    10. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    11. Shu-Heng Chen & Chia-Hsuan Yeh, 1999. "Evolving Traders and the Faculty of the Business School: A New Architecture of the Artificial Stock Market," Computing in Economics and Finance 1999 613, Society for Computational Economics.
    12. Bower, John & Bunn, Derek W. & Wattendrup, Claus, 2001. "A model-based analysis of strategic consolidation in the German electricity industry," Energy Policy, Elsevier, vol. 29(12), pages 987-1005, October.
    13. 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.
    14. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 6, Edward Elgar Publishing.
    15. Schnizler, Björn & Neumann, Dirk & Veit, Daniel & Napoletano, Mauro & Catalano, Michele & Gallegati, Mauro & Reinicke, Michael & Streitberger, Werner & Eymann, Torsten, 2005. "Environmental analysis for application layer networks," Bayreuth Reports on Information Systems Management 1, University of Bayreuth, Chair of Information Systems Management.
    16. Graubner, Marten & Sexton, Richard J., 2023. "More competitive than you think? Pricing and location of processing firms in agricultural markets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 105(3), pages 784-808.
    17. Ian McCarthy, 2008. "Simulating Sequential Search Models with Genetic Algorithms: Analysis of Price Ceilings, Taxes, Advertising and Welfare," Caepr Working Papers 2008-010, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    18. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    19. Vinícius Ferraz & Thomas Pitz, 2024. "Analyzing the Impact of Strategic Behavior in an Evolutionary Learning Model Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 437-475, February.
    20. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    21. Tomas B. Klos, 1999. "Decentralized Interaction and Co-Adaptation in the Repeated Prisoner&2018;s Dilemma," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 147-165, July.
    22. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    23. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    24. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.

    More about this item

    Keywords

    Industrial organisation ; Evolutionary programming ; Genetic algorithms ; Strategy selection ; Learning;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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