Robin A. A. Ince (2017), “Measuring multivariate redundant information with pointwise common change in surprisal”, Entropy 19(7): 318 — ARXIV-1602.05063
Key takeaways
From the reading, NOTE-968.
- The measure (Defs. 4.1–4.2). I_ccs(S; A1…An) = Σ p̂ · c_p̂(a1…an, s) over the events where sgn Δ_s h(ai) for every i, sgn Δ_s h(a1…an) and sgn c agree; other events count zero. P̂ maximises entropy subject to Q(Ai, S) = P(Ai, S) for each i and Q(A1…An) = P(A1…An).
- Axioms kept and dropped (§§3, 4.4). Kept: symmetry, self-redundancy, subset equality (claimed; it fails, see NOTE-968’s note of 2026-10-20), and the independent identity property, I(A1; A2) = 0 ⇒ I∩((A1, A2); A1, A2) = 0. Dropped: Harder et al.’s identity, deliberately; monotonicity, by the Table 7 counterexample (redundancy 0.77 bit against I(S; X2) = 0.61); and so non-negative atoms.
- Against Bertschinger et al. (§4.3). In REDUCEDOR a stake-setting, simultaneous one-shot game gives each player an advantage, though no decision problem does. Ince concludes unique information is not invariant to P(X1, X2). I_broja reports 0.31 bit redundancy, I_ccs 0.31 bit unique to each source.
- Three sources (§6). XORCOPY gives {i}{jk} = 1 and {12}{13}{23} = −1; DBLXOR gives {12} = {13} = {23} = 1 and {123} = −1. Giant bit, parity and XORUNQ come out as built.
- Gaussians (§7). Unlike the minimum-MI redundancy, I_ccs moves with the correlation between the predictors.
Standing in the record
Filed on 2026-10-17 for the gap left open by the PID filing, and read the same day. It is the held multivariate measure. It does not close the gap as the record stated it, a multivariate redundancy with the identity property and a non-negative decomposition, because Rauh et al. prove there can be none on this lattice (LIT-1202). I_ccs keeps an independent-sources form of identity and gives up non-negativity. It is a rival to LIT-1162 and LIT-1168 on what unique information and identity should mean. ΦID work held here uses the minimum-mutual-information redundancy (NOTE-799), not I_ccs. Filed as THEORY-714. It carries no instruction for machine-learning practice.