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).

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

• Biological networks exhibit the lowest EI among real networks and show the most significant causal emergence after coarse-graining.finding0.849
  Finding from Klein & Hoel (2020) on real network analysis.
• Causal emergence is defined by the difference in the EI values between the macro-level and micro-level.quote0.847
  Core definition from §1.
• Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.819
  Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
• Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.809
  Insight that coarse-graining filters external noise but not intrinsic noise.
• Representational dynamics of causal emergence align with reward improvement in most tasks.finding0.799
  The trajectory of causal emergence through training mirrors the reward improvement curve across the majority of tested environments.
• Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.786
  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.claim0.785
  Cross-fertilization claim made in discussion.
• Biological agents increase causal emergence after learning new memories.claim0.782
  Prior empirical observation from biological systems; motivates investigation in artificial agents.