The paper is concerned with distributed learning in large-scale games. The well-known
fictitious play (FP) algorithm is addressed, which, despite theoretical convergence results …

We consider a group of Bayesian agents who try to estimate a state of the world θ through
interaction on a social network. Each agent v initially receives a private measurement of θ: a …

We consider an n agents distributed optimization problem with misspecified information
characterized in a parametric sense, where the unknown parameter can be solved by a …

A repeated network game where agents have quadratic utilities that depend on information
externalities—an unknown underlying state—as well as payoff externalities—the actions of …

The role of social networks in learning and opinion formation has been demonstrated in a
variety of scenarios such as the dynamics of technology adoption [1], consumption behavior …

We consider distributed learning problem in games with an unknown cost-relevant
parameter, and aim to find the Nash equilibrium while learning the true parameter. Inspired …

This paper studies adaptive learning in the class of weighted network games. This class of
games includes applications like research and development within interlinked firms, crime …

We consider finite games where the agents only share their beliefs on the possible
equilibrium configuration. Specifically, the agents experience the strategies of their …

We study a dynamic game in which a group of players attempt to coordinate on a desired,
but only partially known, outcome. The desired outcome is represented by an unknown state …

The paper is concerned with learning in large-scale multi-agent games. The empirical
centroid fictitious play (ECFP) algorithm is a variant of the well-known fictitious play …