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. ISSN 1553-734X (Accepted)

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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: 03 Sep 2020 10:02
URI: http://sro.sussex.ac.uk/id/eprint/93475

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