Cold play: Learning across bimatrix games

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Lensberg2021,

• author = "Terje Lensberg and Klaus Reiner Schenk-Hoppe",
• title = "Cold play: Learning across bimatrix games",
• journal = "Journal of Economic Behavior \& Organization",
• year = "2021",
• volume = "185",
• pages = "419--441",
• month = may,
• keywords = "genetic algorithms, genetic programming, One-shot games, Solution concepts, Evolutionary stability",
• ISSN = "0167-2681",
• URL = "https://www.sciencedirect.com/science/article/pii/S0167268121000949",
• URL = "https://gplab.nhh.no/gamesolver.php",
• DOI = "doi:10.1016/j.jebo.2021.02.027",
• 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 agents 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.",

}

Genetic Programming entries for Terje Lensberg Klaus Reiner Schenk-Hoppe

Citations