ReviewEntropy (Basel, Switzerland)2024
Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies.
Review in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed.
- Beyond Entropy: Organization as the Preservation of Structural Identity.Entropy (Basel, Switzerland) · 2026Article
- A Sequential Markov Probabilistic Aggregation Algorithm for Causal Emergence in Markov Aggregation.Entropy (Basel, Switzerland) · 2026Article
- Statistical learning of stochastic complex systems via the Yau-Yau nonlinear filter.Innovation (Cambridge (Mass.)) · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Complex Networks in Contemporary Science and Technology.Entropy (Basel, Switzerland) · 2026Article
- Review
- When Wholes Resist Decomposition: A Spectral Measure of Epistemic Emergence.Entropy (Basel, Switzerland) · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- A reframed landscape of causal emergence.Patterns (New York, N.Y.) · 2026Article
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
- How causal is "circular causality"?Frontiers in network physiology · 2026Article
- Intrinsic units: identifying a system's causal grain.Neuroscience of consciousness · 2026Article
- Consilience in Causation: Causal Emergence Is Found Across Measures of Causation.Entropy (Basel, Switzerland) · 2025Article
- Information-Theoretical Analysis of Team Dynamics in Football Matches.Entropy (Basel, Switzerland) · 2025Article
- Analyzing Mega-mobility Systems in Smart Cities: A Macro-Micro Integration with Feedback Paradigm Empowered by Artificial Intelligence.Research (Washington, D.C.) · 2025Review
- 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
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence (CE) theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of CE. It focuses on two primary challenges: quantifying CE and identifying it from data. The latter task requires the integration of machine learning and neural network techniques, establishing a significant link between causal emergence and machine learning. We highlight two problem categories: CE with machine learning and CE for machine learning, both of which emphasize the crucial role of effective information (EI) as a measure of causal emergence. The final section of this review explores potential applications and provides insights into future perspectives.
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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.