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


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.

SUBMITTER: Rosas FE 

PROVIDER: S-EPMC7833221 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Rosas Fernando E FE   Mediano Pedro A M PAM   Jensen Henrik J HJ   Seth Anil K AK   Barrett Adam B AB   Carhart-Harris Robin L RL   Bor Daniel D  

PLoS computational biology 20201221 12


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 modali  ...[more]

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