A repeated network game where agents’ utilities depend on information and
payoff
externalities is considered. Agents play Bayesian Nash Equilibrium strategies
with respect to their beliefs on the state of the world and the actions of all
other nodes in the network. These beliefs are refined over subsequent stages
based on the observed actions of neighboring peers. This paper introduces the
Quadratic Network Game (QNG) filter that agents can run locally to update
their
beliefs, select corresponding optimal actions, and eventually learn a
sufficient
statistic of the network’s state. The QNG filter is demonstrated on a
coordination game.