finding

active

finding:biological-networks-exhibit-the-lowest-ei-among-real-networks-and-show-the-most-significant-causal-emergence-after-coarse-graining

Biological networks exhibit the lowest EI among real networks and show the most significant causal emergence after coarse-graining.

Finding from Klein & Hoel (2020) on real network analysis.

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 (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.
Substrate-independent associative learning in biological networks
members_of
Learning and memory mechanisms (Pavlovian conditioning, pattern completion) emerge in gene regulatory and molecular networks through coarse-graining and causal emergence analysis.

Frameworks (1)

framework

Hoel's Causal Emergence Theory
supports
Quantitative emergence theory based on Markov dynamics and effective information (EI).

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).finding0.849
Example from Hoel et al. (2013) replicated in the survey.
Bioelectric networks discovered by evolution ~time of bacterial biofilms; served as ideal medium for scaling computation and information synthesis before neural systems.finding0.802
Evidence that pre-neural bioelectric infrastructure predates and likely precedes neurobiology; supports continuity of intelligence across substrates.
Biological and artificial agents share causal emergence as an axis of learning and reorganization.claim0.799
Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
Bioelectric networks scale cell computation into anatomical homeostasis and are a mechanism for evolving larger Selves.claim0.796
Developmental bioelectricity is proposed as a tractable entry point to understand the informational architecture of collective intelligence in morphogenesis.
Bioelectric networks scale agency across organizational levels by integrating homeostatic competencies of cells into emergent systems with larger cognitive light cones.claim0.791
Core thesis: bioelectric networks provide the mechanism by which single-cell homeostasis becomes organism-level agency through integration and feedback loops.
EI of ER random networks converges to -log2(p) with increasing size, with a phase transition at average degree ≈ log2(N).finding0.790
From Klein & Hoel (2020) analysis of artificial complex networks.
Biological agents increase causal emergence after learning new memories.claim0.787
Prior empirical observation from biological systems; motivates investigation in artificial agents.
Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.784
Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.