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Cold play: Learning across bimatrix games

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  • Lensberg, Terje
  • Schenk-Hoppé, Klaus R.

Abstract

We study one-shot play in the set of all bimatrix games by a large population of agents. The agents never see the same game twice, but they can learn ‘across games’ by developing solution concepts that tell them how to play new games. Each agent’s individual solution concept is represented by a computer program, and natural selection is applied to derive stochastically stable solution concepts. Our aim is to develop a theory predicting how experienced agents would play in one-shot games.

Suggested Citation

  • Lensberg, Terje & Schenk-Hoppé, Klaus R., 2020. "Cold play: Learning across bimatrix games," MPRA Paper 99095, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:99095
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    Cited by:

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    2. Daniele Condorelli & Massimiliano Furlan, 2024. "Deep Learning to Play Games," Papers 2409.15197, arXiv.org.
    3. 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.
    4. İzgi, Burhaneddin & Özkaya, Murat & Üre, Nazım Kemal & Perc, Matjaž, 2023. "Extended matrix norm method: Applications to bimatrix games and convergence results," Applied Mathematics and Computation, Elsevier, vol. 438(C).

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    More about this item

    Keywords

    One-shot games; solution concepts; genetic programming; evolutionary stability.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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