IDEAS home Printed from https://ideas.repec.org/a/spr/joevec/v7y1997i3p219-254.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.de/link/service/journals/00191/papers/7007003/70070219.pdf
    Download Restriction: Access to the full text of the articles in this series is restricted

    File URL: http://link.springer.de/link/service/journals/00191/papers/7007003/70070219.ps.gz
    Download Restriction: Access to the full text of the articles in this series is restricted

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters,in: Evolution, Organization and Economic Behavior, chapter 6 Edward Elgar Publishing.
    4. 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.
    5. 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.
    6. 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.
    7. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    8. 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.
    9. 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).
    10. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    11. repec:zbw:iamodp:109915 is not listed on IDEAS
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

    More about this item

    Keywords

    Industrial organisation ; Evolutionary programming ; Genetic algorithms ; Strategy selection ; Learning;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joevec:v:7:y:1997:i:3:p:219-254. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.