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Cold play: Learning across bimatrix games

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  • Author(s): Lensberg, Terje; Schenk-Hoppé, Klaus Reiner
  • Source:
    Lensberg , T & Schenk-Hoppé , K R 2021 , ' Cold play: Learning across bimatrix games ' , Journal of Economic Behavior & Organization . https://doi.org/10.1016/j.jebo.2021.02.027
  • Document Type:
    article in journal/newspaper
  • Language:
    English
  • Additional Information
    • Publication Date:
      2021
    • Collection:
      The University of Manchester: Research Explorer - Publications
    • 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 a stochastically stable solution concept. Our aim is to develop a theory predicting how experienced agents would play in one-shot games. To use the theory, visit https://gplab.nhh.no/gamesolver.php.
    • Accession Number:
      10.1016/j.jebo.2021.02.027
    • Online Access:
      https://research.manchester.ac.uk/en/publications/464cbbf2-0207-47f9-806d-b8940d27a41a
      https://doi.org/10.1016/j.jebo.2021.02.027
    • Rights:
      info:eu-repo/semantics/openAccess
    • Accession Number:
      edsbas.9F038397