IDEAS home Printed from https://ideas.repec.org/a/spr/joevec/v2y1992i1p17-38.html
   My bibliography  Save this article

Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists

Author

Listed:
  • Marks, R E

Abstract

Oligopolistic pricing decisions--in which the choice variable is not dichotomous, as in the simple prisoner's dilemma, but continuous--have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoport's Tit for Tat for the three-person repeated game. Holland's genetic algorithm and Axelrod's representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions. This paper discusses how findings from two-person tournaments can be extended to the GPD, in particular how the author's winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of "niche" strategies, successful against a particular environment of competitors.

Suggested Citation

  • Marks, R E, 1992. "Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists," Journal of Evolutionary Economics, Springer, vol. 2(1), pages 17-38, March.
  • Handle: RePEc:spr:joevec:v:2:y:1992:i:1:p:17-38
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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. 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.
    3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
    4. Tesfatsion, Leigh S., 1998. "Teaching Agent-Based Computational Economics to Graduate Students," Staff General Research Papers Archive 1199, Iowa State University, Department of Economics.
    5. Tomas Klos, "undated". "Decentralized Interaction and Co-adaptation in the Repeated Prisoner's Dilemma," Computing in Economics and Finance 1997 88, Society for Computational Economics.
    6. 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.
    7. Leigh TESFATSION, 1995. "How Economists Can Get Alife," Economic Report 37, Iowa State University Department of Economics.
    8. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    9. Cacho, Oscar J. & Simmons, Phil, 1999. "A genetic algorithm approach to farm investment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 43(3), September.
    10. Robert Hoffmann, 1999. "The Independent Localisations of Interaction and Learning in the Repeated Prisoner's Dilemma," Theory and Decision, Springer, vol. 47(1), pages 57-72, August.
    11. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 265-290, October.
    12. repec:dgr:rugsom:97b33 is not listed on IDEAS
    13. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    14. Tony Curson Price, 1997. "Using co-evolutionary programming to simulate strategic behaviour in markets," Levine's Working Paper Archive 588, David K. Levine.
    15. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-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.
    16. Clemens, Christiane & Riechmann, Thomas, 1996. "Evolutionäre Optimierungsverfahren und ihr Einsatz in der ökonomischen Forschung," Hannover Economic Papers (HEP) dp-195, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    17. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
    18. Christos Ioannou, 2014. "Coevolution of finite automata with errors," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 541-571, July.
    19. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    20. Cariola, Monica, 1999. "A high-potential sector: titanium metal: Oligopolistic policies and technological constraints as main limits to its development," Resources Policy, Elsevier, vol. 25(3), pages 151-159, September.

    More about this item

    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:2:y:1992:i:1:p:17-38. 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.