Learning with minimal information in continuous games, by Mario Bravo (Univ. de Chile)

In this talk we introduce a learning process for games with continuous action sets. The procedure is payoff-based and thus requires no sophistication from players and no knowledge of the game. We show that despite such limited information, players will converge to Nash in large classes of games (possibly with a continuum of equilibria). In particular, convergence to stable Nash equilibrium is guaranteed in all games with strategic complements as well as in concave games. Time permitting, we will also discuss convergence results for locally ordinal potential games and games with isolated equilibria.

This is joint work with Sebastian Bervoets and Mathieu Faure.