In the context of anonymous games (i.e., games where the payoff of a player is, apart from his/her own action, determined by the distribution of the actions made by the other players), we present a model in which, generically (in a precise sense), finite‐player games have strict pure strategy...

We present a new approach to studying equilibrium dynamics in a class of stochastic games with a continuum of players with private types and strategic complementarities. We introduce a suitable equilibrium concept, called Markov Stationary Nash Distributional Equilibrium (MSNDE), prove its...

We study models of learning in games where agents with limited memory use social information to decide when and how to change their play. When agents only observe the aggregate distribution of payoffs and only recall information from the last period, aggregate play comes close to Nash...

We develop a dynamic framework of strategic information transmission through cheap talk in a social network. Privately informed agents have different preferences about the action to be implemented by each agent and repeatedly communicate with their neighbors in the network. We first characterize...

We study the informed-principal problem in a bilateral asymmetric information trading setting with interdependent values and quasi-linear utilities. The informed seller proposes a mechanism and voluntarily certifies information about the good's characteristics. When the set of certifiable...

I present a model of observational learning with payoff interdependence. Agents, ordered in a sequence, receive private signals about an uncertain state of the world and sample previous actions. Unlike in standard models of observational learning, an agent's payoff depends both on the state and...

We analyze the social and private learning at the symmetric equilibria of a queueing game with strategic experimentation. An infinite sequence of agents arrive at a server which processes them at an unknown rate. The number of agents served at each date is either: a geometric random variable in...

A learning rule is uncoupled if a player does not condition his strategy on the opponent's payoffs. It is radically uncoupled if a player does not condition his strategy on the opponent's actions or payoffs. We demonstrate a family of simple, radically uncoupled learning rules whose...