Improved estimators of causal emergence for large systems

TL;DR

This paper introduces improved, computationally efficient information-theoretic measures for quantifying emergence in large systems, correcting for double-counting issues to enhance accuracy.

Contribution

The authors develop a family of measures that iteratively correct for double-counted shared components, improving emergence detection in complex systems.

Findings

Successfully detects emergence in simulated data

Effective in real-world flocking behavior data

Offers a controllable trade-off between sensitivity and computational load

Abstract

A central challenge in the study of complex systems is the quantification of emergence -- understood as the ability of the system to exhibit collective behaviours that cannot be traced down to the individual components. While recent work has proposed practical measures to detect emergence, these approaches tend to double-count the contribution of shared components, which substantially hinders their capability to effectively study large systems. In this work, we introduce a family of improved information-theoretic measures of emergence that iteratively correct for double-counted terms. Our approach is computationally efficient and provides a controllable trade-off between computational load and sensitivity, leading to more accurate and versatile estimates of emergence. The benefits of the proposed approach are demonstrated by successfully detecting emergence in both simulated and real-world data related to flocking behaviour.