Causal emergence in learning and adaptation

Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

13 members. Each node is clickable.

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Drawn from 4 sources

The papers/notes whose extracted claims & findings make up this cluster.

The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents7 members
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies5 members
feucht-goodfire-geometric-calculator-2026.md2 members
2026-05-14_phil-trans-A-goodfire-aboutblank-impact.md1 member

Bridges (2)

Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.

Causal emergence in biological systems15 shared
Causal emergence in learning agents11 shared

Claims (9)

Biological agents increase causal emergence after learning new memories.Prior empirical observation from biological systems; motivates investigation in artificial agents.
Biological and artificial agents share causal emergence as an axis of learning and reorganization.Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
Causal emergence can enable causal interventions to create better RL agents.Assertion that understanding causal emergence may lead to methods for manipulating agent representations to improve performance.
Causal emergence identification tasks can be understood as causal representation learning tasks.Authors propose a conceptual mapping between CE identification and CRL.
Causal emergence is widespread across measures of causation, not just EI.Claim by Comolatti & Hoel (2022) endorsed by this survey.
Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
Causal emergence provides new perspectives for causal representation learning, interpreting latent variables as emergent causalities.Cross-fertilization claim made in discussion.
Empowerment as intrinsic reward bridges causal learning and reinforcement learning in agent development.

Findings (4)

Among 17 chaotic/complex cellular automata rules, 30% show causal emergence, 70% show causal degradation.Varley (2020) analysis using ordinal partition network on cellular automata.
Causal emergence depends on the coarse-graining strategy: different partitions of the same boolean network yield EI values 1.55 (emergence) vs 0.18 (degradation).Example from Hoel et al. (2013) replicated in the survey.
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.Empirical result: CE measurements correlate with and predict learning performance in RL agents.
Representational dynamics of causal emergence align with reward improvement in most tasks.The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.