Optimizing Energetic cost of Uncertainty in a Driven System With and Without Feedback

TL;DR

This paper investigates the energy-efficiency tradeoff in driven nonequilibrium systems with feedback, analyzing how entropy production influences variance minimization and the cost of effective parameters in coarse-grained models.

Contribution

It introduces a framework linking function variance to entropy production and quantifies hidden dissipation in effective equilibrium limits.

Findings

Minimum variance decreases monotonically with entropy production.

Effective potential costs relate to hidden entropy production.

Timescale separation impacts the energetic cost of coarse-grained dynamics.

Abstract

Many biological functions require the dynamics to be necessarily driven out-of-equilibrium. In contrast, in various contexts, a nonequilibrium dynamics at fast timescales can be described by an effective equilibrium dynamics at a slower timescale. In this work we study the two different aspects, (i) the energy-efficiency tradeoff for a specific nonequilibrium linear dynamics of two variables with feedback, and (ii) the cost of effective parameters in a coarse-grained theory as given by the "hidden" dissipation and entropy production rate in the effective equilibrium limit of the dynamics. To meaningfully discuss the tradeoff between energy consumption and the efficiency of the desired function, a one-to-one mapping between function(s) and energy input is required. The function considered in this work is the variance of one of the variables. We get a one-to-one mapping by considering the minimum variance obtained for a fixed entropy production rate and vice-versa. We find that this minimum achievable variance is a monotonically decreasing function of the given entropy production rate. When there is a timescale separation, in the effective equilibrium limit, the cost of the effective potential and temperature is the associated "hidden" entropy production rate.