mlsanityData to 14 Oct 2026

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

arXiv 2312.16815 ↗First posted 28 Dec 2023physics.soc-ph cs.AI nlin.AOcited by 3

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Abstract (arXiv)

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 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 causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 48 of its 204 references that are in the map. First posted on arXiv 28 Dec 2023.

Builds on

48 of 204 references.

References · 48

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36 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Computational Irreducibility as the Foundation of Agency: A Formal Model Connecting Undecidability to Autonomous Behavior in Complex Systemscited by 2
  2. 2026To Use AI as Dice of Possibilities with Timing Computationcited by 0
  3. 2026Partial Effective Information Decomposition for Synergistic Causalitycited by 0

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  2. 2026Symmetries and Causality: Causal Effect Identification Beyond IID Data
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  4. 2022Variational Causal Dynamics: Discovering Modular World Models from Interventions
  5. 2026CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations
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  7. 2026A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
  8. 2026xWhyL: Causal Interactive Learning