Causal emergence in complex systems

WANG Zhipeng; ZHANG Jiang

doi:10.12202/j.0476-0301.2023049

Journal of Beijing Normal University(Natural Science) > 2023 > 59(5): 725-733. > DOI: 10.12202/j.0476-0301.2023049

WANG Zhipeng, ZHANG Jiang. Causal emergence in complex systems[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 725-733. DOI: 10.12202/j.0476-0301.2023049

Citation:

WANG Zhipeng, ZHANG Jiang. Causal emergence in complex systems[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 725-733. DOI: 10.12202/j.0476-0301.2023049

Citation:

WANG Zhipeng, ZHANG Jiang. Causal emergence in complex systems[J]. Journal of Beijing Normal University(Natural Science), 2023, 59(5): 725-733. DOI: 10.12202/j.0476-0301.2023049

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Causal emergence in complex systems

WANG Zhipeng¹,
ZHANG Jiang^{1, 2, ,}

1.
School of Systems Science，Beijing Normal University，100875，Beijing，China
2.
Swarma Research，100085，Beijing，China

More Information

Received Date: April 03, 2023
Accepted Date: September 05, 2023
Available Online: September 20, 2023

Graphical Abstract

Abstract

Abstract

Focusing on complex systems: a quantitative description is provided for emergence, causality, and causal emergence．The relationship between causality and emergence is elucidated． Two quantitative methods, coarse-graining and information decomposition, are reviewed for characterizing causal emergence． Two causal emergence identification methods based on information decomposition and neural information compression are presented． Basic principles, advantages, disadvantages, and relevant applications of each method are detailed． The framework of causal emergence is defined based on coarse-graining method, which can be applied to discrete dynamical systems． The use of information decomposition method in solving problems relies on redundant information． The proposed indicators based on information decomposition can identify causal emergence in data and find a sufficient condition． The data-driven neural information compression method is scalable and applicable to continuous dynamical systems． It can automatically extract coarse-grained functions at different levels and construct hierarchical dynamics, and identify causal emergence in different types of dynamical systems． Existing methods are improved and applied to identify more complex systems, automate learning groups, and address challenges in general dynamics of large-scale models and other related problems．
- complex system,
- causal emergence,
- effective information,
- coarse-graining,
- information decomposition,
- neural information compression

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References (52)

References

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