community

active

leiden_hybrid_concepts

label: sonnet

community:leiden_hybrid_concepts-run4-c3

Causal emergence in biological systems

Examines how macro-scale causal power exceeds micro-scale in living and learning systems.

118 members. Each node is clickable.

Loading graph…

Sub-communities (10)

Finer clusters this community splits into. Each is its own community page.

Hierarchical structure and multiscale coherence in physical systems21 Evolutionary individuality and hierarchical agency16 Causal emergence in learning and adaptation15 Quantum mechanics foundations of genetic stability12 Causal emergence via information-geometric coarse-graining12 Hierarchical emergence through nested centers and constraints10 Bioelectric computation and morphological intelligence10 Substrate-independent associative learning in biological networks10 Multi-scale credit assignment in evolutionary systems7 Autopoietic organization and nested autonomy4

Drawn from 36 sources

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

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies19 members
Topological constraints on self-organisation in locally interacting systems12 members
2021-11-09_dorian_schrodinger-life.pdf_c3c4f59 members
Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms9 members
The Causally Emergent Alignment Hypothesis: Causal Emergence Aligns with and Predicts Final Reward in Reinforcement Learning Agents8 members
Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality6 members
Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds5 members
cognitive-glue-and-alexander.md4 members
Generalizing frameworks for sentience beyond natural species3 members
2026-05-14_phil-trans-A-goodfire-aboutblank-impact.md3 members
Topological constraints on self-organization in locally interacting systems3 members
Life as we know it3 members
published: 28 March 20223 members
Harmony-Seeking Computations: a Science of Non-Classical Dynamics based on the Progressive Evolution of the Larger Whole3 members
Multiple ways to implement and infer sentience3 members
Collective intelligence: A unifying concept for integrating biology across scales and substrates3 members
Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology2 members
AI: A Bridge Toward Diverse Intelligence.md2 members
2026-05-15_manifold-overlap-papers-economy-strategy.md2 members
Learning without neurons in physical systems2 members
An association-based model of dynamic behaviour2 members
feucht-goodfire-geometric-calculator-2026.md2 members
Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds2 members
2022-09-23_Prabros._dynamics-in-action-pdf1.pdf_2f6a2b1 member
Diagrammatic Writing1 member
2026 02 02_2328_Search_Papers_The Literature Shows Strong Theoretical Foundation1 member
Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue1 member
alexander-and-levin.md1 member
The collective intelligence of evolution and development1 member
guo-atlas-2026.md1 member
Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior1 member
2022-08-21_Prabros._ACT2022_slides_4223.pdf_890d161 member
Active Inference: A Process Theory1 member
GEOMETRY-OF-CARE.md1 member
The computational boundary of a 'self': developmental bioelectricity drives multicellularity and scale-free cognition1 member
2023-03-15_Hibai-Unzueta_2020-RRNW-Joe-Wheaton.pdf_9b7f3d1 member

Bridges (20)

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

Causal emergence in learning agents23 shared
Hierarchical structure and multiscale coherence in physical systems21 shared
Agential evolution & biological individuality20 shared
Evolutionary individuality and hierarchical agency16 shared
Causal emergence in learning and adaptation15 shared
Causal emergence via information-geometric coarse-graining12 shared
Hierarchical network ordering & thermodynamics12 shared
Quantum mechanics foundations of genetic stability12 shared
Bioelectric computation and morphological intelligence10 shared
Hierarchical emergence through nested centers and constraints10 shared
Substrate-independent associative learning in biological networks10 shared
Multi-scale credit assignment in evolutionary systems7 shared
Quantum basis of genetic stability6 shared
Non-neural associative learning in GRNs5 shared
Autopoietic organization and nested autonomy4 shared
Multi-scale competency architecture & evolvability3 shared
Lattice topology and thermodynamic phase transitions3 shared
Combinatorial constraints on emergent ordering in networks2 shared
Autoregressive LLMs & formal thought disorder2 shared
Hierarchical competence and organizational emergence2 shared

Claims (79)

Evolution is more intelligent than we realised.Watson's reinterpretation of formal equivalence between evolution and learning, beyond random variation framework.
Quantum mechanics is essential for explaining long-term stability of genetic structures against thermal perturbation.Central claim: without quantum-mechanical energy thresholds and discrete states, hereditary information could not survive across generations.
Self-Sustaining Systems are the Solution
A Markov blanket is (almost) inevitable in coupled dynamical systems with short-range interactions.Argument that physical laws inevitably produce Markov blankets.
Acting with unity of purpose in multicellular organisms does not require genetic homogeneity
All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
Aperiodic structure of genetic molecules enables exponential diversity of encodable information compared to periodic crystals.Schrödinger argues that non-repeating molecular structure (aperiodic solid) allows information density far exceeding periodic/crystalline alternatives.
Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.
Before a transition, the higher-level unit of selection does not exist, yet complex adaptations creating that unit must evolve through bottom-up selection on lower-level units.Central theoretical puzzle in ETI research: explains why existing frameworks struggle with ETI explanation.
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.
Biology's robustness, open-endedness, evolvability, and unique complexity likely depend on the fact that evolution works with an agential material.Central claim linking life's properties to the inherent competencies of its material substrate.
By using a variational autoencoder-like architecture for genomic compression, evolution is freed from over-training and pushed to evolve general-purpose problem-solving machines.Claim linking the indirect genotype-phenotype mapping to robustness and open-endedness.
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.
Close-knit adaptation of system elements arising over time is central observable in nature and human-formed ecological systems but eludes simple algorithmic formulation.Alexander's core assertion that subtle adaptive processes are too simple and common-sense-based for conventional computation but profoundly important.
Constraints enable emergence of complexity through self-organization in far-from-equilibrium systems.
Dimensional Unbounded Space of Related Self-Similar MechanismsPiumarta's central thesis: all diverse dynamic mechanisms (object-oriented, subject-oriented, context-oriented, Worlds, etc.) are specializations of points within a parameterizable multidimensional associative space.
Distinction between Organismic and Evolutionary Individuality
EI and normalized EI could serve as a unified metric for out-of-distribution generalization.Conjecture that maximizing EI yields causal representations invariant to distribution shifts.
EI maximization serves as an objective standard for selecting coarse-graining and macro-dynamics.Claim by Hoel et al. and endorsed by this survey; used to counter subjectivity critiques.
Evolution actually searches not only genotype space but a more tractable space of behavior-shaping signals that exploit cellular intelligence.Central thesis of the paper.
Evolution does not simply make hardwired machines that execute a predetermined set of steps; instead, it produces problem-solving hardware.Key insight about the nature of evolved systems.
Evolution learns to generalize beyond default morphologies, producing problem-solving machines.Argues that evolutionary learning goes beyond specific adaptations.
Evolution makes problem-solving agents, not solutions; commitment to mutation and uncontrollable environments as ratchet for intelligence
Evolution re-uses many of the same mechanisms and strategies across scales of organization and problem spaces.Claims that scale-free dynamics, like bioelectric networks, are ancient and conserved.
+49 more

Findings (39)

Plants display goal-directed, anticipatory, flexible, and adaptive behaviors including kin discrimination, cooperation, mimicry, and risk evaluationEmpirical evidence from plant neurobiology showing behavioral patterns historically attributed to animal sentience.
All local Hamiltonians on lattices with the same combinatorial structure have asymptotically equivalent free energies (Theorem 1)Topological equivalence theorem for local Hamiltonians
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.
Ant colony task assignment: interactions between foragers show higher noise than nurses/cleaners; CE stabilizes overall colony cohesion.Swain et al. (2022) EI-based study of ant colonies.
At thermal equilibrium, ability to converge to an ordered phase is independent of energy levels and window sizes (Lemma 1)Scaling argument depends only on perimeter, not details of energy magnitudes or window length
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.
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 measured by NIS+ increases with observational noise but decreases with dynamical noise.Insight that coarse-graining filters external noise but not intrinsic noise.
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.
EI of ER random networks converges to -log2(p) with increasing size, with a phase transition at average degree ≈ log2(N).From Klein & Hoel (2020) analysis of artificial complex networks.
Folding pathways of creased sheets can be trained for specific topologies including classification of mechanical force patterns analogous to neural networksExperimentally validated finding that origami/kirigami systems can solve classification tasks through physical learning of crease stiffnesses
For a graph with independent cliques, individual cliques may flip magnetisation while remaining uniformly magnetised if intra-clique coupling > (T/2) log n_i (Theorem 4)Condition for hierarchical order with locally coherent but globally varying phases
For one-dimensional local Hamiltonian with m>1 stored patterns at non-zero temperature, domain wall formation is thermodynamically favourable (Theorem 2)No ordered phase in 1D with multiple stored patterns
Free-energy scaling under domain-wall formation in Potts, autoregressive, and hierarchical networks shows that combinatorics of interactions on a graph prevent or allow spontaneous ordering.Core result demonstrating topological constraints on self-organization
Functionally closed subsystems are systematically expelled to the periphery of the ensemble.Simulated result showing that subsystems unable to influence others cannot invade internal organization, supporting Markov blanket partition.
Gene regulatory network models exhibit associative learning and pattern completion.Analysis of GRN models shows they can perform several kinds of learning, supporting the view of cellular networks as agents on a cognitive continuum.
Gene regulatory networks exhibit associative learningEvidence that non-neural systems meet Crump's criterion #7; supports generalization of sentience criteria beyond neural substrates.
In AOMIC ID1000 movie-watching fMRI data, NIS+ finds a one-dimensional macro-state representing 100-dimensional micro-states.Real brain imaging result suggesting a compressed emergent representation.
In AOMIC PIOP2 resting-state fMRI data, NIS+ finds a seven-dimensional macro-state with widely distributed attributions.Contrast to movie-watching condition, showing context-dependent emergence.
In hierarchical systems with independent cliques, there exist parameter regimes where individual cliques maintain uniform magnetisation while others flip.Shows how hierarchical topology enables local order within global flexibility; explains biological multiscale organization
Internal subsystem dynamics significantly predict external subsystem motion via canonical variates analysis (χ²-distributed, p=0.00052).Empirical validation from primordial soup that internal states encode information about hidden environmental states.
Isomeric molecules are equally stable despite identical energy levels because transition requires passage through higher-energy intermediate configurations.
NIS+ automatically discovers two-group macro-states in Boid model simulations matching the two boid groups.Yang et al. (2023) experiment on emergent herding behavior.
NIS+ captures emergent static/dynamic patterns such as 'gliders' in Conway's Game of Life within the latent space.Yang et al. (2023) demonstration of emergent pattern recognition.
NIS+ learns macro-dynamics matching ground-truth SIR dynamics from noisy micro-level data.Experimental result from Yang et al. (2023) reported in the survey.
NIS+ outperforms NIS, variational autoencoders, and feed-forward neural networks in out-of-distribution generalization experiments.Yang et al. (2023) result linking EI maximization to robust generalization.
Ornament evolution from evenly spaced dots through six steps using alternating repetition, strong centers, good shape, levels of scale, boundaries, contrast, and local symmetries.Simple graphical example demonstrating how sequential application of the fifteen properties creates increasingly coherent aesthetic form.
Plants Display Action-Potential-Like Depolarizations Along Vascular Networks
Plants synthesize and signal with common neurotransmitters including glutamate and display action-potential-like depolarizations.
Population Dynamics of Perception and Emergence of Translational MembranesStudy of interacting perceptual agents with adaptive internal structures; exemplifies how perception emerges from population-level dynamics.
+9 more