Neuromorphic Realization of Best Response in Finite-Action Games

TL;DR

This paper introduces a neuromorphic dynamical system that models best response in finite-action games, capturing decision-making processes with stability, commitment, and evidence-based switching.

Contribution

It presents a novel mechanistic framework embedding relational structure into decision dynamics, enabling convergence to Nash equilibria in potential games.

Findings

Dynamics compute geometry-aware utility and converge exponentially to best response.

Relational structure embedded in the mechanism is essential for desired properties.

Framework demonstrated in a repeated coverage game with proven equilibrium convergence.

Abstract

We develop a mechanistic dynamical-systems formulation of best response in finite-action games with relational structure on the action set. The proposed neuromorphic decision dynamics realize best response as the stable outcome of an internal state-space process, rather than as an externally imposed choice rule. This provides a deterministic account of commitment formation, symmetry resolution through basins of attraction, and hysteresis and decision persistence under perturbations. For action spaces with circulant coupling, we prove using Lyapunov-Schmidt reduction that the action-coupling operator determines which components of evidence govern decision formation. We further show that the dynamics implicitly compute a geometry-aware utility, converge exponentially to the corresponding best response with rate independent of the number of actions, and switch only when evidence is sufficiently strong. In contrast, supplying the same geometry-aware utility directly to logit dynamics does not recover these properties, showing that relational structure must be embedded in the decision mechanism itself. We illustrate the framework in a repeated coverage game, prove that the induced game is an exact potential game, and show that its Nash equilibria are reached by the neuromorphic dynamics.