International audienceWe study repeated games where players use an exponential learning scheme in order to adapt to an ever-changing environment. If the game's payoffs are subject to random perturbations, this scheme leads to a new stochastic version of the replicator dynamics that is quite different from the ``aggregate shocks'' approach of evolutionary game theory. Irrespective of the perturba- tions' magnitude, we find that strategies which are dominated (even iteratively) eventually become extinct and that the game's strict Nash equilibria are stochastically asymptotically stable. We complement our analysis by illustrating these results in the case of congestion games
We apply stochastic stability to undiscounted finitely repeated two player games without common inte...
The predominant paradigm in evolutionary game theory and more generally online learning in games is ...
abstract: This thesis explores and explains a stochastic model in Evolutionary Game Theory introduce...
International audienceWe study repeated games where players use an exponential learning scheme in or...
We consider a simple model of stochastic evolution in population games. In our model, each agent occ...
Traditional game theory studies strategic interactions in which the agents make rational decisions. ...
We investigate the impact of payoff shocks on the evolution of large populations of myopic players t...
International audienceWe investigate the impact of payoff shocks on the evolution of large populatio...
International audienceWe consider a family of stochastic distributed dynamics to learn equilibria in...
International audienceMotivated by the scarcity of accurate payoff feedback in practical application...
We establish how a rich collection of evolutionary games can arise as asymptotically exact descripti...
We study the repeated congestion game, in which multiple populations of players share resources, and...
Stochastic evolutionary game dynamics for finite populations has recently been widely explored in th...
Abstract. We present a general model of stochastic evolution in games played by large populations of...
We extend the notion of evolutionarily stable strategies introduced by Maynard Smith and Price (1973...
We apply stochastic stability to undiscounted finitely repeated two player games without common inte...
The predominant paradigm in evolutionary game theory and more generally online learning in games is ...
abstract: This thesis explores and explains a stochastic model in Evolutionary Game Theory introduce...
International audienceWe study repeated games where players use an exponential learning scheme in or...
We consider a simple model of stochastic evolution in population games. In our model, each agent occ...
Traditional game theory studies strategic interactions in which the agents make rational decisions. ...
We investigate the impact of payoff shocks on the evolution of large populations of myopic players t...
International audienceWe investigate the impact of payoff shocks on the evolution of large populatio...
International audienceWe consider a family of stochastic distributed dynamics to learn equilibria in...
International audienceMotivated by the scarcity of accurate payoff feedback in practical application...
We establish how a rich collection of evolutionary games can arise as asymptotically exact descripti...
We study the repeated congestion game, in which multiple populations of players share resources, and...
Stochastic evolutionary game dynamics for finite populations has recently been widely explored in th...
Abstract. We present a general model of stochastic evolution in games played by large populations of...
We extend the notion of evolutionarily stable strategies introduced by Maynard Smith and Price (1973...
We apply stochastic stability to undiscounted finitely repeated two player games without common inte...
The predominant paradigm in evolutionary game theory and more generally online learning in games is ...
abstract: This thesis explores and explains a stochastic model in Evolutionary Game Theory introduce...