Cold play: Learning across bimatrix games

Terje Lensberg and Klaus Schenk-Hoppé

MPRA Paper from University Library of Munich, Germany

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.

Keywords: One-shot games; solution concepts; genetic programming; evolutionary stability. (search for similar items in EconPapers)
JEL-codes: C63 C73 C90 (search for similar items in EconPapers)
Date: 2020-03-10
New Economics Papers: this item is included in nep-evo, nep-gth, nep-mic and nep-ore
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https://mpra.ub.uni-muenchen.de/99095/1/MPRA_paper_99095.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/104438/1/MPRA_paper_104438.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/107044/1/MPRA_paper_107044.pdf revised version (application/pdf)

Related works:
Journal Article: Cold play: Learning across bimatrix games (2021)
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