Statistical Physics of Adaptation

TL;DR

This paper explores the concept of physical adaptation using statistical physics, demonstrating a generalized free energy framework that explains how driven many-particle systems tend to self-organize into states that efficiently absorb and dissipate energy.

Contribution

It introduces a generalized Helmholtz free energy for driven stochastic systems, linking physical properties to adaptation and self-organization in non-equilibrium conditions.

Findings

Driven systems tend to self-organize into energy-efficient states

Generalized free energy predicts physical adaptation mechanisms

Analysis of energy landscape hopping illustrates adaptation process

Abstract

All living things exhibit adaptations that enable them to survive and reproduce in the natural environment that they inhabit. From a biological standpoint, it has long been understood that adaptation comes from natural selection, whereby maladapted individuals do not pass their traits effectively to future generations. However, we may also consider the phenomenon of adaptation from the standpoint of physics, and ask whether it is possible to delineate what the difference is in terms of physical properties between something that is well-adapted to its surrounding environment, and something that is not. In this work, we undertake to address this question from a theoretical standpoint. Building on past fundamental results in far-from-equilibrium statistical mechanics, we demonstrate a generalization of the Helmholtz free energy for the finite-time stochastic evolution of driven Newtonian matter. By analyzing this expression term by term, we are able to argue for a general tendency in driven many-particle systems towards self-organization into states formed through exceptionally reliable absorption and dissipation of work energy from the surrounding environment. Subsequently, we illustrate the mechanism of this general tendency towards physical adaptation by analyzing the process of random hopping in driven energy landscapes.