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An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems
File(s)
entropy-20-00793.pdf (837.6 KB)
Published version
Author(s)
Rosas De Andraca, Fernando Ernesto
Martinez Mediano, Pedro
Ugarte, Martin
Jensen, Henrik
Type
Journal Article
Abstract
Self-organisation lies at the core of fundamental but still unresolved scientific questions, and holds the promise of de-centralised paradigms crucial for future technological developments. While self-organising processes have been traditionally explained by the tendency of dynamical systems to evolve towards specific configurations, or attractors, we see self-organisation as a consequence of the interdependencies that those attractors induce. Building on this intuition, in this work we develop a theoretical framework for understanding and quantifying self-organisation based on coupled dynamical systems and multivariate information theory. We propose a metric of global structural strength that identifies when self-organisation appears, and a multi-layered decomposition that explains the emergent structure in terms of redundant and synergistic interdependencies. We illustrate our framework on elementary cellular automata, showing how it can detect and characterise the emergence of complex structures.
Date Issued
2018-10-16
Date Acceptance
2018-10-03
Citation
Entropy, 2018, 20 (10)
URI
DOI
ISSN
1099-4300
Publisher
MDPI AG
Journal / Book Title
Entropy
Volume
20
Issue
10
Copyright Statement
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
Commission of the European Communities
Grant Number
702981
Subjects
Science & Technology
Physical Sciences
Physics, Multidisciplinary
Physics
self-organisation
multivariate information theory
coupled dynamical systems
partial information decomposition
statistical synergy
high-order interactions
CELLULAR-AUTOMATA
NETWORKS
BRAIN
PRINCIPLES
nlin.AO
01 Mathematical Sciences
02 Physical Sciences
Fluids & Plasmas
Publication Status
Published
Article Number
793