Toward a unified taxonomy of information dynamics via Integrated Information Decomposition

• 成果类型: Article
• 署名作者: Mediano, Pedro A. M.; Rosas, Fernando E.; Luppi, Andrea I.; Carhart-Harris, Robin L.; Bor, Daniel; Seth, Anil K.; Barrett, Adam B.
• 署名单位: Imperial College London; University of London; University College London; University of Sussex; Imperial College London; Imperial College London; University of Oxford; University of Cambridge; University of Cambridge; University of California System; University of California San Francisco; University of London; Queen Mary University London; University of Cambridge; Canadian Institute for Advanced Research (CIFAR); University of Sussex
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11918
• DOI: 10.1073/pnas.2423297122
• 发表日期: 2025-09-30
• 关键词: redundancy; synergy; complexity; INDEPENDENCE; AGE

摘要

Our ability to understand and control complex systems of many interacting parts remains limited. A key challenge is that we still do not know how best to describe-and quantify-the many-to-many dynamical interactions that characterize their complexity. To address this limitation, we introduce the mathematical framework of Integrated Information Decomposition, or IID. IID provides a comprehensive framework to disentangle and characterize the information dynamics of complex multivariate systems. On the theoretical side, 1ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer, information storage, and dynamical complexity as aggregates of these modes, thereby overcoming some of their known theoretical shortcomings. On the empirical side, we validate our theoretical results with computational models and examples from over 1,000 biological, social, physical, and synthetic dynamical systems. Altogether, IID improves our understanding of the behavior of widely used measures for characterizing complex systems across disciplines and leads to new more refined analyses of dynamical complexity.