Information Thermodynamics on Causal Networks

TL;DR

This paper extends nonequilibrium thermodynamics to complex systems modeled by causal networks, revealing how information flow constrains entropy production and generalizing fundamental thermodynamic laws.

Contribution

It introduces a novel framework linking causal network topology with thermodynamic inequalities, generalizing the second law and fluctuation theorem for interconnected systems.

Findings

Entropy production is bounded by information flow in multi-system interactions.

Causal network topology influences thermodynamic bounds.

Application to biochemical adaptation demonstrates the theory's relevance.

Abstract

We study nonequilibrium thermodynamics of complex information flows induced by interactions between multiple fluctuating systems. Characterizing nonequilibrium dynamics by causal networks (i.e., Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity characterized by the topology of the causal network. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. We demonstrate our general result by a simple model of biochemical adaptation.