  We study the convergence properties of decentralized fictitious play (DFP)
for the class of near-potential games where the incentives of agents are nearly
aligned with a potential function. In DFP, agents share information only with
their current neighbors in a sequence of time-varying networks, keep estimates
of other agents' empirical frequencies, and take actions to maximize their
expected utility functions computed with respect to the estimated empirical
frequencies. We show that empirical frequencies of actions converge to a set of
strategies with potential function values that are larger than the potential
function values obtained by approximate Nash equilibria of the closest
potential game. This result establishes that DFP has identical convergence
guarantees in near-potential games as the standard fictitious play in which
agents observe the past actions of all the other agents.
