finding

active

finding:among-17-chaotic-complex-cellular-automata-rules-30-show-causal-emergence-70-show-causal-degradation

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.

Source paper

extracted_from

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

(2023) · Bing Yuan · Jiang Zhang · Aobo Lyu · Jiayun Wu +5

Neighborhood — ranked by edge-count

Communities (2)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Causal emergence in learning and adaptation
members_of
Framework measuring how coarse-grained causal structure increases during learning across biological and artificial agents, using effective information and interventional methods.

Concepts (1)

concept

Causal Emergence
supports
Core concept: degree to which an agent exerts unique predictive power on its future; key to cognition at all scales.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

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).finding0.765
Example from Hoel et al. (2013) replicated in the survey.
Representational dynamics of causal emergence align with reward improvement in most tasks.finding0.755
The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.
Finite state automata-like feature assemblies emerge from autoregressive training on data patterns, not from co-evolved circuit designclaim0.752
Authors distinguish FSA assemblies from circuits — features interact via the token stream from learning data statistics, not from being jointly optimized
Causal emergence predictive of final reward early in RL training across multiple algorithms, architectures, and environments.finding0.751
Empirical result: CE measurements correlate with and predict learning performance in RL agents.
There exists a phase transition of emergent causality in complex systems when a key parameter changes.hypothesis0.749
Speculation in the discussion.
Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.749
Insight that coarse-graining filters external noise but not intrinsic noise.
Causal emergence is widespread across measures of causation, not just EI.claim0.748
Claim by Comolatti & Hoel (2022) endorsed by this survey.
successful agents exhibited causal emergence that was consistently predictive of final reward early in training and whose representational dynamics aligned with reward improvement in most tasks.quote0.747
Load-bearing summary of the main empirical finding that anchors the Causally Emergent Alignment Hypothesis.