Signal extraction for simulated games with a large number of players

A signal extraction problem in simulated games is studied. A modelling technique is proposed for deriving beliefs for players in simulated games. Since standard Bayesian games provide conditions for beliefs on the basis of the common prior assumption, they do not allow for non-uniform beliefs unless the game has some dynamic structure that allows for learning. The framework presented allows for deriving beliefs by characterizing the reliability of the signals, and the players' degree of confidence in these signals. This makes it particularly suitable for games with a large number of heterogenous players.

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