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Learning To Play Best Response In Duopoly Games

Author

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  • JAN WENZELBURGER

    (Fakultät für Wirtschaftswissenschaften, Universität Bielefeld, Postfach 100 131, D-33501 Bielefeld, Germany)

Abstract

We consider a quantity-setting duopoly market where firms lack perfect knowledge of the market demand function. They use estimated and therefore misspecified demand functions instead and determine their optimal strategies from the corresponding subjective payoff functions. The central issue of this paper is the question under which conditions a firm can learn the true demand function as well as the response behavior of its competitor from repeated estimations of historical market data. As soon as estimation errors are negligible, a firm is able to play best response in the usual game theoretic sense.

Suggested Citation

  • Jan Wenzelburger, 2004. "Learning To Play Best Response In Duopoly Games," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 443-459.
    • Handle: RePEc:wsi:igtrxx:v:06:y:2004:i:03:n:s0219198904000290
    DOI: 10.1142/S0219198904000290
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589819, November.
    3. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    4. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589833, November.
    5. Szidarovszky, F. & Krawczyk, J.B., 2004. "On stable learning in dynamic oligopolies," Pure Mathematics and Applications, Department of Mathematics, Corvinus University of Budapest, vol. 15(4), pages 453-468.
    6. G.I. Bischi, & C. Chiarella & M. Kopel, 2002. "On Market Games with Misspecified Demand Functions : Long Run Outcomes and Global Dynamics," Computing in Economics and Finance 2002 27, Society for Computational Economics.
    7. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589826, November.
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    Cited by:

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    2. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.

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    More about this item

    Keywords

    Learning; bounded rationality; heterogeneity; duopoly games; JEL Classification: C61; JEL Classification: C62; JEL Classification: D83; JEL Classification: D84;
    All these keywords.

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C0 - Mathematical and Quantitative Methods - - General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D5 - Microeconomics - - General Equilibrium and Disequilibrium
    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

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