claim

active

claim:causal-emergence-alignment-with-learning-is-a-shared-axis-comparing-biological-and-artificial-creatures

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.

Source paper

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The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents

(2026) · Federico Pigozzi · Michael Levin

Neighborhood — ranked by edge-count

Hypotheses (1)

hypothesis

Causally Emergent Alignment Hypothesis
extends
The hypothesis that successful RL agents will display causal emergence that is predictive of final reward early in training and whose representational dynamics align with reward improvement.

Communities (3)

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 agents
members_of
Uses effective information (EI) and coarse-graining to link causal emergence with RL and biological learning.
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.

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.

Biological and artificial agents share causal emergence as an axis of learning and reorganization.claim0.886
Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.849
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.claim0.848
Cross-fertilization claim made in discussion.
Biological agents increase causal emergence after learning new memories.claim0.846
Prior empirical observation from biological systems; motivates investigation in artificial agents.
Causal emergence is defined by the difference in the EI values between the macro-level and micro-level.quote0.843
Core definition from §1.
Causal emergence identification tasks can be understood as causal representation learning tasks.claim0.828
Authors propose a conceptual mapping between CE identification and CRL.
Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.824
Insight that coarse-graining filters external noise but not intrinsic noise.
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).finding0.819
Example from Hoel et al. (2013) replicated in the survey.

Cross-corpus bridges (1)

same_concept_as · Nomic cosine

External markdown files that talk about the same concept as this entity.

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Can machine learning principles and algorithms help explain biological evolution and development?questions/can-machine-learning-principles-and-algorithms-help-explain.md0.806