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A Partial Folk Theorem for Games with Unknown Payoff Distributions
Author(s) -
Wiseman Thomas
Publication year - 2005
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.1111/j.1468-0262.2005.00589.x
Subject(s) - stochastic game , mathematical economics , state (computer science) , repeated game , mathematics , normal form game , function (biology) , folk theorem , mathematical optimization , game theory , algorithm , evolutionary biology , biology
Repeated games with unknown payoff distributions are analogous to a single decision maker's “multi‐armed bandit” problem. Each state of the world corresponds to a different payoff matrix of a stage game. When monitoring is perfect, information about the state is public, and players are sufficiently patient, the following result holds: For any function that maps each state to a payoff vector that is feasible and individually rational in that state, there is a sequential equilibrium in which players experiment to learn the realized state and achieve a payoff close to the one specified for that state.