  Consider a set of agents who play a network game repeatedly. Agents may not
know the network. They may even be unaware that they are interacting with other
agents in a network. Possibly, they just understand that their payoffs depend
on an unknown state that is, actually, an aggregate of the actions of their
neighbors. Each time, every agent chooses an action that maximizes her
instantaneous subjective expected payoff and then updates her beliefs according
to what she observes. In particular, we assume that each agent only observes
her realized payoff. A steady state of the resulting dynamic is a
selfconfirming equilibrium given the assumed feedback. We characterize the
structure of the set of selfconfirming equilibria in the given class of network
games, we relate selfconfirming and Nash equilibria, and we analyze simple
conjectural best-reply paths whose limit points are selfconfirming equilibria.
