ArticleEntropy (Basel, Switzerland)2022
Neural Information Squeezer for Causal Emergence.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- A Sequential Markov Probabilistic Aggregation Algorithm for Causal Emergence in Markov Aggregation.Entropy (Basel, Switzerland) · 2026Article
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
- Intrinsic units: identifying a system's causal grain.Neuroscience of consciousness · 2026Article
- Emergent behaviors in multiagent pursuit evasion games within a bounded 2D grid world.Scientific reports · 2025Article
- Consilience in Causation: Causal Emergence Is Found Across Measures of Causation.Entropy (Basel, Switzerland) · 2025Article
- Finding emergence in data by maximizing effective information.National science review · 2025Article
- An Exact Theory of Causal Emergence for Linear Stochastic Iteration Systems.Entropy (Basel, Switzerland) · 2024Article
- Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies.Entropy (Basel, Switzerland) · 2024Review
- Correction: Zhang, J.; Liu, K. Neural Information Squeezer for Causal Emergence.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.