finding

active

finding:nis-automatically-discovers-two-group-macro-states-in-boid-model-simulations-matching-the-two-boid-groups

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.

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.

In AOMIC ID1000 movie-watching fMRI data, NIS+ finds a one-dimensional macro-state representing 100-dimensional micro-states.finding0.816
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.finding0.812
Contrast to movie-watching condition, showing context-dependent emergence.
NIS+ learns macro-dynamics matching ground-truth SIR dynamics from noisy micro-level data.finding0.798
Experimental result from Yang et al. (2023) reported in the survey.
NIS+ captures emergent static/dynamic patterns such as 'gliders' in Conway's Game of Life within the latent space.finding0.758
Yang et al. (2023) demonstration of emergent pattern recognition.
NIS+ outperforms NIS, variational autoencoders, and feed-forward neural networks in out-of-distribution generalization experiments.finding0.755
Yang et al. (2023) result linking EI maximization to robust generalization.
A computational model of melanocyte regulatory pathway revealed state-space decision points explaining all-or-none conversion (Lobikin et al. 2015)finding0.730
Mathematical modeling showed how cells navigate biochemical state space and face collective decision points.
Collective nucleation dynamics in Hebbian-learned molecular interaction systems can perform pattern recognition by assembling different structures in response to different concentration patternshypothesis0.723
Theoretical prediction that molecular systems with proximity-based learning can recognize patterns; has mathematical connections to Hopfield associative memory
Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.722
Insight that coarse-graining filters external noise but not intrinsic noise.