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

Decentralized Fictitious Play in Near-Potential Games with Time-Varying Communication Networks