Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data

Rosas, Fernando E, Mediano, Pedro A M, Jensen, Henrik J, Seth, Anil K, Barrett, Adam B, Carhart-Harris, Robin L and Bor, Daniel (2020) Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data. PLoS Computational Biology, 16 (12). a1008289 1-23. ISSN 1553-734X

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Abstract

The broad concept of emergence is instrumental in various of the most challenging open scientific questions—yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour—which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway’s Game of Life, Reynolds’ flocking model, and neural activity as measured by electrocorticography.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 03 Sep 2020 06:55
Last Modified: 18 Jan 2021 16:00
URI: http://sro.sussex.ac.uk/id/eprint/93475

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