A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah, Hanane Azzag

TL;DR
This paper introduces a unifying variational principle linking Bayesian inference, game theory, and thermodynamics to explain collective intelligence across systems.
Contribution
It presents the Game-Theoretic Free Energy Principle, connecting free-energy minimization with strategic game equilibria in multi-agent systems.
Findings
Stationary points of collective free energy approximate Nash equilibria.
A variational representation of cooperative games with Gibbs distributions.
Prediction of a non-monotonic relationship between sensory precision and influence.
Abstract
Collective intelligence emerges across biological, physical, and artificial systems without central coordination, yet a unifying principle governing such behaviour remains elusive. The Free Energy Principle explains how individual agents adapt through variational inference, while game theory formalises strategic interactions. Here we introduce the Game-Theoretic Free Energy Principle, a unified framework showing that multi-agent systems performing local free-energy minimisation implicitly implement a stochastic game. We prove that, under bounded rationality and local information constraints, stationary points of collective free energy correspond to approximate Nash equilibria of an induced game. Conversely, a broad class of cooperative games admits a variational representation in which equilibria arise as Gibbs distributions over coalitions, establishing a bridge between Bayesian…
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