Imperial College London
Professor Henrik Jeldtoft Jensen

Professor Henrik Jeldtoft Jensen

Faculty of Natural Sciences, Department of Mathematics

Senior Research Investigator
 
 
 
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Contact

 

+44 (0)20 7594 9853h.jensen Website

 
 
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Location

 

1201Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rosas:2020:10.1371/journal.pcbi.1008289,
author = {Rosas, FE and Mediano, PAM and Jensen, HJ and Seth, AK and Barrett, AB and Carhart-Harris, RL and Bor, D},
doi = {10.1371/journal.pcbi.1008289},
journal = {PLoS Computational Biology},
title = {Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data},
url = {http://dx.doi.org/10.1371/journal.pcbi.1008289},
volume = {16},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Rosas,FE
AU  - Mediano,PAM
AU  - Jensen,HJ
AU  - Seth,AK
AU  - Barrett,AB
AU  - Carhart-Harris,RL
AU  - Bor,D
DO  - 10.1371/journal.pcbi.1008289
PY  - 2020///
SN  - 1553-734X
TI  - Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1008289
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000603071900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008289
UR  - http://hdl.handle.net/10044/1/97539
VL  - 16
ER  -
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