SOURCE-LINKED INTELLIGENCE

Decentralized Optimal Equilibrium Learning Over Dynamic Networks

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

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.

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-13T09:39:19.000Z

First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.