Intermittency as metastability: a predictive approach to evolution in disordered environments

TL;DR

This paper introduces a predictive theory for intermittency in disordered linear systems, unifying concepts of metastability and localization, with broad applications across physics, evolution, and epidemiology.

Contribution

It maps positive linear systems onto a generalized maximal entropy random walk, revealing localization as potential minima and jumps as barrier crossings.

Findings

Localization islands are local minima of an effective potential.

Intermittent jumps correspond to barrier crossings in this potential.

The method unifies intermittency and metastability concepts across disciplines.

Abstract

Many systems across the sciences evolve through a combination of multiplicative growth and diffusive transport. In the presence of disorder, these systems tend to form localized structures which alternate between long periods of relative stasis and short bursts of activity. This behaviour, known as intermittency in physics and punctuated equilibrium in evolutionary theory, is difficult to forecast; in particular there is no general principle to locate the regions where the system will settle, how long it will stay there, or where it will jump next. Here I introduce a predictive theory of linear intermittency that closes these gaps. I show that any positive linear system can be mapped onto a generalization of the "maximal entropy random walk", a Markov process on graphs with non-local transition rates. This construction reveals the localization islands as local minima of an effective potential, and intermittent jumps as barrier crossings in that potential. My results unify the concepts of intermittency in linear systems and Markovian metastability, and provide a generally applicable method to reduce, and predict, the dynamics of disordered linear systems. Applications span physics, evolutionary dynamics and epidemiology.