Mutual Linearity is a Generic Property of Steady-State Markov Networks

TL;DR

This paper reveals that in steady-state Markov networks, the probabilities of states are linearly related, and this mutual linearity extends to various observables, with exact relations derived for responses and constraints linked to network topology.

Contribution

It demonstrates the universal mutual linearity of state probabilities in steady-state Markov networks and derives exact relations and constraints based on network topology and kinetics.

Findings

Probabilities of any two states are linearly related in steady state.

Mutual linearity extends to currents, counting, and state-dependent observables.

Derived an exact relation between response and probability ratios.

Abstract

Understanding and predicting how complex systems respond to external perturbations is a central challenge in nonequilibrium statistical physics. Here we consider continuous-time Markov networks, which we subject to perturbations along a single edge. We find that in steady state the probabilities of any two states are linearly related to one another. We show that this mutual linearity of probabilities extends to a broad class of observables, including currents but also generic counting and state-dependent observables. Moreover, we derive an exact relation between the relative response of any state's probability and the ratio of two steady-state probabilities. Leveraging the Markov chain tree theorem, we further show that probabilities and the considered observables are constrained by the topological and kinetic properties of the network and provide analytical expressions in terms of spanning tree polynomials. Our results are general, holding for arbitrary rate parameterizations and extending far from equilibrium.