claim

active

claim:ei-maximization-serves-as-an-objective-standard-for-selecting-coarse-graining-and-macro-dynamics

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.

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 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.
Causal emergence & effective information
members_of
Using EI/normalized EI to evaluate macro-scale causal structure and coarse-graining quality.

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.

If EI maximization is used as a regularization in representation learning, then OOD generalization will improve beyond current invariant risk minimization methods.hypothesis0.825
Proposed conjecture in §4.3.1.
EI and normalized EI could serve as a unified metric for out-of-distribution generalization.claim0.807
Conjecture that maximizing EI yields causal representations invariant to distribution shifts.
The strict version of the simulation objective is optimized by the actual time evolution rule that created the training samples.claim0.744
Equivalence of optimal predictor to the physics of the data.
Acting to optimize value and perception are two aspects of exactly the same principle: minimization of free energy.claim0.741
Foundational claim unifying action and perception within single optimization framework.
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).finding0.740
Example from Hoel et al. (2013) replicated in the survey.
acting to optimize value and perception are two aspects of exactly the same principle; namely the minimisation of a quantity [free energy] that bounds the probability of sensory input, given a particular agent or phenotype.quote0.740
Concise statement of the free-energy principle's unification of action and perception.
What coarse-graining strategy generates the maximum effective information?question0.735
Central optimization problem in CE identification.
Curiosity, insight, decision-making, and diverse phenomena can all be accommodated by a single imperative: minimization of expected free energy (resolution of uncertainty).claim0.731
Central thesis of the paper unifying cognitive phenomena under one objective function