PLoS computational biologyNov 5, 2025

New Method for Comparing Information in Complex Systems

Updated Nov 7, 2025

Abstract

This paper presents a new approach to help compare information across different complex systems. Information theory is versatile, but measuring information can vary greatly between similar systems. To address this, we introduce a method called NuMIT that improves how we normalize data, making comparisons easier and more meaningful. We demonstrate this method using various examples, including human brain imaging data, showing that NuMIT is a reliable tool for analyzing complex systems and allows for better cross-dataset comparisons.

Simplified

A key feature of information theory is its universality, as it can be applied to study a broad variety of complex systems. However, many information-theoretic measures can vary significantly even across systems with similar properties, making normalisation techniques essential for allowing meaningful comparisons across datasets. Inspired by the framework of Partial Information Decomposition (PID), here we introduce Null Models for Information Theory (NuMIT), a null model-based non-linear normalisation procedure which improves upon standard entropy-based normalisation approaches and overcomes their limitations. We provide practical implementations of the technique for systems with different statistics, and showcase the method on synthetic models and on human neuroimaging data. Our results demonstrate that NuMIT provides a robust and reliable tool to characterise complex systems of interest, allowing cross-dataset comparisons and providing a meaningful significance test for PID analyses.

Funding

Competing interests

The authors have declared that no competing interests exist.

PubMed

Abstract

Funding

Competing interests

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