Thomas F. Varley (2022), Entropy 25(1), 54.

Key takeaways

From the reading, NOTE-756.

  • The identity underneath. Local mutual information i(x; y) = log P(x,y)/(P(x)P(y)) is negative whenever P(x,y) < P(x)P(y), while its expectation I(X; Y) is never negative. Local excess entropy e(x) = i(x_{t−1}; x_t) is negative when the past makes the realised future less likely than chance: “The past transiently ceases to inform of the future and instead misinforms.”
  • Coarse-graining. A four-node Boolean network, coarse-grained by AND over pairs, is emergent on Hoel’s measure (log(F̄(X̃)/F̄(X)) ≈ 0.202), yet ≈52.73% of informative micro transitions map to misinformative macro ones. They are the low-probability transitions, so the average holds.
  • ΦID synergy. The same toy’s macro system has temporal synergy ≈0.031 bit on average and a negative local value at 31.25% of its sixteen transitions.
  • A connectome. On one Human Connectome Project matrix with Infomap macro-nodes, ≈22.47% of informative micro edges map to misinformative macro edges, and ≈80.4% of misinformative edges run between communities.
  • How general. The possibility is argued to hold for any measure built on time-delayed mutual information; two further frameworks (Barnett and Seth, Chang et al.) are expected to show it, not computed.
  • What a negative moment means is open. It may be fluctuation or a change in causal structure; the statistics alone do not say.

Standing in the record

Filed on 2026-10-12 from the owner’s papers-feed for QUESTION-035, and read on 2026-10-14 in NOTE-756. The seed claim, that emergence can hold on average and fail at particular configurations, is confirmed. The reading adds that an average verdict on unity gives no verdict about a moment, which bears on the duration clause of CLAIM-157 and on the persistence test of CLAIM-161. THEORY-268 is filed from it: emergence measured as an expectation belongs to the ensemble of transitions, not to each moment. It complements THEORY-222, which holds the average effective-information gain of the same coarse-graining account.