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