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This paper considers information sharing in a multi-player repeated game.Every round, each player observes a subset of components of a random vector andthen takes a control action. The utility earned by each player depends on thefull random vector and on the actions of others. An example is a game wheredifferent rewards are placed over multiple locations, each player only knowsthe rewards in a subset of the locations, and players compete to collect therewards. Sharing information can help others, but can also increase competitionfor desirable locations. Standard Nash equilibrium and correlated equilibriumconcepts are inadequate in this scenario. Instead, this paper develops analgorithm where, every round, all players pass their information and intendedactions to a game manager. The manager provides suggested actions for eachplayer that, if taken, maximize a concave function of average utilities subjectto the constraint that each player gets an average utility no worse than itwould get without sharing. The algorithm acts online using information given ateach round and does not require a specific model of random events or playeractions. Thus, the analytical results of this paper apply in non-ergodicsituations with any sequence of actions taken by human players.

Sharing Information Without Regret in Managed Stochastic Games