Energy dissipation and information flow in coupled Markovian systems

TL;DR

This paper investigates the relationship between energy dissipation and information flow in coupled Markovian systems, revealing how model complexity influences thermodynamic costs under different driving conditions.

Contribution

It introduces a theoretical analysis of how model complexity bounds dissipation in stochastic systems, especially beyond quasi-static driving.

Findings

Model complexity saturates dissipation under quasi-static driving.

A lower bound on the ratio of model complexity to total dissipation is established.

Weak driving conditions realize the lower bound in the system.

Abstract

A stochastic system under the influence of a stochastic environment is correlated with both present and future states of the environment. Such a system can be seen as implicitly implementing a predictive model of future environmental states. The non-predictive model complexity has been shown to lower-bound the thermodynamic dissipation. Here we explore these statistical and physical quantities at steady state in simple models. We show that under quasi-static driving this model complexity saturates the dissipation. Beyond the quasi-static limit, we demonstrate a lower bound on the ratio of this model complexity to total dissipation, that is realized in the limit of weak driving.