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Neural Information Squeezer for Causal Emergence
Conventional studies of causal emergence have revealed that stronger causality can be obtained on the macro-level than the micro-level of the same Markovian dynamical systems if an appropriate coarse-graining strategy has been conducted on the micro-states. However, identifying this emergent causali...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858212/ https://www.ncbi.nlm.nih.gov/pubmed/36673167 http://dx.doi.org/10.3390/e25010026 |
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author | Zhang, Jiang Liu, Kaiwei |
author_facet | Zhang, Jiang Liu, Kaiwei |
author_sort | Zhang, Jiang |
collection | PubMed |
description | Conventional studies of causal emergence have revealed that stronger causality can be obtained on the macro-level than the micro-level of the same Markovian dynamical systems if an appropriate coarse-graining strategy has been conducted on the micro-states. However, identifying this emergent causality from data is still a difficult problem that has not been solved because the appropriate coarse-graining strategy can not be found easily. This paper proposes a general machine learning framework called Neural Information Squeezer to automatically extract the effective coarse-graining strategy and the macro-level dynamics, as well as identify causal emergence directly from time series data. By using invertible neural network, we can decompose any coarse-graining strategy into two separate procedures: information conversion and information discarding. In this way, we can not only exactly control the width of the information channel, but also can derive some important properties analytically. We also show how our framework can extract the coarse-graining functions and the dynamics on different levels, as well as identify causal emergence from the data on several exampled systems. |
format | Online Article Text |
id | pubmed-9858212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98582122023-01-21 Neural Information Squeezer for Causal Emergence Zhang, Jiang Liu, Kaiwei Entropy (Basel) Article Conventional studies of causal emergence have revealed that stronger causality can be obtained on the macro-level than the micro-level of the same Markovian dynamical systems if an appropriate coarse-graining strategy has been conducted on the micro-states. However, identifying this emergent causality from data is still a difficult problem that has not been solved because the appropriate coarse-graining strategy can not be found easily. This paper proposes a general machine learning framework called Neural Information Squeezer to automatically extract the effective coarse-graining strategy and the macro-level dynamics, as well as identify causal emergence directly from time series data. By using invertible neural network, we can decompose any coarse-graining strategy into two separate procedures: information conversion and information discarding. In this way, we can not only exactly control the width of the information channel, but also can derive some important properties analytically. We also show how our framework can extract the coarse-graining functions and the dynamics on different levels, as well as identify causal emergence from the data on several exampled systems. MDPI 2022-12-23 /pmc/articles/PMC9858212/ /pubmed/36673167 http://dx.doi.org/10.3390/e25010026 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Jiang Liu, Kaiwei Neural Information Squeezer for Causal Emergence |
title | Neural Information Squeezer for Causal Emergence |
title_full | Neural Information Squeezer for Causal Emergence |
title_fullStr | Neural Information Squeezer for Causal Emergence |
title_full_unstemmed | Neural Information Squeezer for Causal Emergence |
title_short | Neural Information Squeezer for Causal Emergence |
title_sort | neural information squeezer for causal emergence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858212/ https://www.ncbi.nlm.nih.gov/pubmed/36673167 http://dx.doi.org/10.3390/e25010026 |
work_keys_str_mv | AT zhangjiang neuralinformationsqueezerforcausalemergence AT liukaiwei neuralinformationsqueezerforcausalemergence |