Quantifying system-environment synergistic information by effective information decomposition

TL;DR

This paper introduces a new dynamic indicator based on effective information decomposition to measure a system's ability to respond flexibly to its environment, with applications to various complex systems including gene networks.

Contribution

It proposes a novel indicator satisfying multivariate information decomposition axioms, linking system-environment entanglement to adaptive capacity, and demonstrates its applicability across different system types.

Findings

The indicator correlates with system type and Langton parameter in cellular automata.

Feedback loops in gene regulatory networks enhance environmental responsiveness.

Machine learning confirms the framework's effectiveness with unknown dynamics.

Abstract

What is the most crucial characteristic of a system with life activity? Currently, many theories have attempted to explain the most essential difference between living systems and general systems, such as the self-organization theory and the free energy principle, but there is a lack of a reasonable indicator that can measure to what extent a system can be regarded as a system with life characteristics, especially the lack of attention to the dynamic characteristics of life systems. In this article, we propose a new indicator at the level of dynamic mechanisms to measure the ability of a system to flexibly respond to the environment. We proved that this indicator satisfies the axiom system of multivariate information decomposition in the partial information decomposition (PID) framework. Through further disassembly and analysis of this indicator, we found that it is determined by the degree of entanglement between system and environmental variables in the dynamics and the magnitude of noise. We conducted measurements on cellular automata (CA), random Boolean networks, and real gene regulatory networks (GRN), verified its relationship with the type of CA and the Langton parameter, and identified that the feedback loops have high abilities to flexibly respond to the environment on the GRN. We also combined machine learning technology to prove that this framework can be applied in the case of unknown dynamics.