Mathieu Faure


Mathieu Faure

Learning without information

IBD Salle 21

Îlot Bernard du Bois - Salle 21

5-9 boulevard Maurice Bourdet
13001 Marseille

Jeudi 8 février 2018| 12:30 - 13:15

Ugo Bolletta : ugo.bolletta2[at]
Mathieu Faure : mathieu.faure[at]


We introduce a stochastic learning process designed for games with continuous action sets, called the dampened gradient approximation process, which requires from players no sophistication and no knowledge of the game (i.e. a payoff -based learning process). We show that despite such limited information, players will converge to Nash in large classes of games, as soon as payoff  functions are single-peaked. In particular, convergence to a Nash equilibrium which is stable is guaranteed in all games with strategic complements as well as in generalized zero-sum games; convergence to Nash often happens in all locally ordinal potential games; and convergence to Nash occurs with positive probability in all games with isolated equilibria. Our paper shows that it is possible to construct simple payoff-based learning procedures for continuous games with good convergence properties.