A myopic adjustment process for mean field games with finite state and action space

Abstract In this paper, we introduce a natural learning rule for mean field games with finite state and action space, the so-called myopic adjustment process. The main motivation these considerations is complexity of computations necessary to determine dynamic equilibria, which makes it seem questionable whether agents are indeed able play equilibria. We prove that process converges locally towards strict stationary equilibria under rather broad conditions. Moreover, also obtain global convergence result stronger, yet intuitive