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Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists

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  • 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
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    Cited by:

    1. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    2. Tesfatsion, Leigh, 1998. "Teaching Agent-Based Computational Economics to Graduate Students," ISU General Staff Papers 199807010700001043, Iowa State University, Department of Economics.
    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. 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), pages 1-18, September.
    5. 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.
    6. 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.
    7. 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.
    8. Tesfatsion, Leigh, 1995. "How Economists Can Get Alife," Economic Reports 18196, Iowa State University, Department of Economics.
    9. Christos Ioannou, 2014. "Coevolution of finite automata with errors," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 541-571, July.
    10. 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.
    11. 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.
    12. Claus Dierksmeier, 2020. "From Jensen to Jensen: Mechanistic Management Education or Humanistic Management Learning?," Journal of Business Ethics, Springer, vol. 166(1), pages 73-87, September.
    13. 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.
    14. 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.
    15. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
    16. 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.
    17. repec:dgr:rugsom:97b33 is not listed on IDEAS
    18. 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.
    19. Tony Curson Price, 1997. "Using co-evolutionary programming to simulate strategic behaviour in markets," Levine's Working Paper Archive 588, David K. Levine.
    20. 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.
    21. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    22. Robert E. Marks, 2013. "Validation and Functional Complexity," Discussion Papers 2013-30, School of Economics, The University of New South Wales.
    23. 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".

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