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Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
Fernando E Rosas1,2,3, Pedro A M Mediano4, Henrik J Jensen3,5,6
1Center for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK.
Plos Computational Biology
|December 21, 2020
Summary
This study introduces a formal theory for causal emergence in complex systems. It quantifies downward causation and causal decoupling, offering practical criteria for analyzing emergent phenomena.
Area of Science:
- Complex Systems Science
- Theoretical Physics
- Computational Neuroscience
Background:
- Emergence is a key concept in science, but lacks quantitative theories.
- Understanding the relationship between system parts and macroscopic behavior is challenging.
Purpose of the Study:
- To introduce a formal theory of causal emergence in multivariate systems.
- To provide quantitative definitions for downward causation and causal decoupling.
- To develop practical criteria for analyzing emergence in large systems.
Main Methods:
- Formal theory development for causal emergence.
- Quantitative analysis of system dynamics and macroscopic features.
- Application to case studies like Conway's Game of Life, flocking models, and neural activity.
Main Results:
- A formal theory of causal emergence is presented.
- Quantitative definitions for downward causation and causal decoupling are established.
- Efficient calculation criteria for large systems are derived.
Conclusions:
- The proposed framework offers a quantitative approach to emergence.
- The theory is applicable to diverse systems, from cellular automata to neural networks.
- This work provides tools for studying complex emergent behaviors across scientific disciplines.
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