TY - JOUR
T1 - Cold play: Learning across bimatrix games
AU - Lensberg, Terje
AU - Schenk-Hoppé, Klaus Reiner
PY - 2021/3/22
Y1 - 2021/3/22
N2 - 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.
AB - 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.
U2 - 10.1016/j.jebo.2021.02.027
DO - 10.1016/j.jebo.2021.02.027
M3 - Article
SN - 0167-2681
JO - Journal of Economic Behavior & Organization
JF - Journal of Economic Behavior & Organization
ER -