This paper studies decentralized learning of socially optimal equilibria in finite normal-form games over dynamic communication networks.
Each agent observes only its own realized payoffs, does not know the game a priori, and can communicate only with time-varying neighbors using low-bandwidth messages.
We propose networked decentralized optimal equilibrium learning dynamics in which agents generate randomized semantic content/discontent signals from local payoff comparisons and exchange time-stamped time-stacked tables rather than raw actions, payoff information or local estimates/parameters.
The method combines table fusion with temporal majority reconstruction to mitigate dynamic communication while preserving fully decentralized operation.
We establish finite-time logarithmic regret guarantees, with an in-phase exploration perturbation, for optimal equilibrium selection under utilitarian and proportional-fair social welfare objectives.
Simulation results further show that the proposed approach can effectively select socially desirable equilibria over dynamic communication networks.