Mean-Field Games and Ambiguity Aversion

Abstract

This paper looks at a general framework for mean-field games with ambiguity averse players based on the probabilistic framework described in Carmona (2013). A framework for mean-field games with ambiguity averse players is presented, using a version of the stochastic maximum principle to find the optimal controls of the players. The dynamics under the optimal control are characterized through a forwards-backwards stochastic differential equation and a relationship between the finite player game and the mean-field game is established. Explicit solutions are derived in the case of the linear-quadratic mean-field game.