Entropic dynamics on Gibbs statistical manifolds

TL;DR

This paper develops an entropic dynamics framework on Gibbs statistical manifolds, deriving system evolution from probabilistic principles and exploring the influence of manifold curvature, with examples including Gaussian and discrete systems.

Contribution

It introduces a novel entropic dynamics approach on Gibbs manifolds, incorporating information geometry and an intrinsic arrow of time, extending previous probabilistic derivations of physical laws.

Findings

Dynamics on Gaussian distributions analyzed

Discrete 3-state system dynamics explored

Entropic time exhibits an intrinsic arrow of time

Abstract

Entropic dynamics is a framework in which the laws of dynamics are derived as an application of entropic methods of inference. Its successes include the derivation of quantum mechanics and quantum field theory from probabilistic principles. Here we develop the entropic dynamics of a system the state of which is described by a probability distribution. Thus, the dynamics unfolds on a statistical manifold which is automatically endowed by a metric structure provided by information geometry. The curvature of the manifold has a significant influence. We focus our dynamics on the statistical manifold of Gibbs distributions (also known as canonical distributions or the exponential family). The model includes an "entropic" notion of time that is tailored to the system under study; the system is its own clock. As one might expect, entropic time is intrinsically directional; there is a natural arrow of time which is lead by entropic considerations. As illustrative examples we discuss dynamics on a space of Gaussians and the discrete 3-state system.