In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@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}
}
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 -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
Email us.
Sign up to our mailing list.
Follow us on Twitter, LinkedIn and Instagram.
