Causal emergence via information-geometric coarse-graining

Framework using effective information (EI) and NIS+ to automatically discover macro-scale dynamics from micro-level data, validated on fMRI, Conway's Game of Life, and SIR models.

12 members. Each node is clickable.

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

Bridges (3)

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

Causal emergence in biological systems12 shared
Causal emergence in learning agents9 shared
Causal emergence & effective information2 shared

Findings (9)

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.
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.
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.
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.
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.

Claims (3)

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.
The NIS and NIS+ frameworks provide effective solutions for causal emergence identification from data.Central claim of the machine-learning section, summarizing the contribution.