finding

active

finding:nis-learns-macro-dynamics-matching-ground-truth-sir-dynamics-from-noisy-micro-level-data

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.

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

Claims (1)

claim

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

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 via information-geometric coarse-graining
members_of
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.

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.

NIS+ automatically discovers two-group macro-states in Boid model simulations matching the two boid groups.finding0.798
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.finding0.797
Yang et al. (2023) demonstration of emergent pattern recognition.
In AOMIC ID1000 movie-watching fMRI data, NIS+ finds a one-dimensional macro-state representing 100-dimensional micro-states.finding0.795
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.finding0.794
Contrast to movie-watching condition, showing context-dependent emergence.
Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.781
Insight that coarse-graining filters external noise but not intrinsic noise.
NIS+ outperforms NIS, variational autoencoders, and feed-forward neural networks in out-of-distribution generalization experiments.finding0.781
Yang et al. (2023) result linking EI maximization to robust generalization.
These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.764
Connection between active inference neuronal dynamics and predictive processing theory.
Internal subsystem dynamics significantly predict external subsystem motion via canonical variates analysis (χ²-distributed, p=0.00052).finding0.756
Empirical validation from primordial soup that internal states encode information about hidden environmental states.