nucleation

LIT-1843: Causal emergence is widespread across measures of causation

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Properties8
StatusActive — read 2026-10-29 (NOTE-1416) in full, from arXiv's v1; worth reading as the defenders' survey that rewrites a dozen measures of causal strength (Eells, Suppes, Cheng, Lewis, Pearl's PN/PS/PNS, Galton, bit-flip, effect information, EI) as functions of sufficiency P(e | c) and necessity 1 − P(e | C\c), and that reports causal emergence for every measure on one bipartite Markov chain under maximum-entropy, stationary and local intervention distributions. Read it knowing that the model is built so its macro is a deterministic NOT gate whatever the micro noise; that in its symmetric version the macro distributions are the images of the micro ones, so it is a common-distribution test, and there the system-level EI cannot show emergence under either the stationary or the maximum-entropy distribution (data processing; the record's computation agrees), which the paper concedes only for the stationary case; that "we prove" in the abstract covers rewritings of definitions, not a theorem that emergence must occur; and that the per-transition and normalized measures that do show emergence are not bounded by data processing. It does not meet THEORY-787's promote_when.
Version2
Tagscausality, information-theory, complex-systems
ExtendsLIT-1038 — Quantifying causal emergence shows that macro can beat micro
RivalsLIT-1839 — Causal Emergence: When Distortions in a Map Obscure the Territory
Cited as “grounds” byARG-041 — From the effective-information bound and its ties to proportionality: a gain that depends on the intervention distribution is not a property of the system, the coarse-grainings that lose nothing are, and the coarsest of them is where the causes are
Cited as “paper” byNOTE-1416 — Comolatti and Hoel — Causal emergence is widespread across measures of causation
Cited as “source” byTHEORY-1167 — When one intervention distribution over micro states is used at both levels, the effective information of a coarse-graining of a discrete mechanism, with each macro intervention realized by that distribution within its block, cannot exceed the micro effective information; so a positive causal emergence of effective information, scored with a uniform distribution at each level, equals the micro gain from changing the distribution minus a non-negative loss, and is a gain from the change of distribution

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Comolatti, R. & Hoel, E. (2022). Causal emergence is widespread across measures of causation. arXiv:2202.01854.

Read in full on 2026-10-29 (NOTE-1416).

Why this is here

The triage of 2026-10-29 (curation entry of 20:59) filed it for QUESTION-204 and THEORY-787’s promote_when, which is met by a result that the macro advantage survives an intervention distribution other than the uniform one imposed separately at each level, on systems not built for it. This is the paper Hoel offers as that result. Read with Eberhardt and Lee 2022 (LIT-1839), it does not meet the promote_when (NOTE-1416, THEORY-1167).

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