Single-Cover Sensitivity Separations for Myrheim-Meyer and Midpoint-Scaling Dimension Estimators on Finite Posets

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Authors: Panasenko
Cited by: 3

Abstract

Myrheim–Meyer and midpoint-scaling dimension estimators are established order-theoretic diagnostics for finite causal sets and related directed acyclic structures. We study a worst-case combinatorial question: how differently can these estimators respond when two finite bounded posets have canonical Hasse diagrams that differ by exactly one cover? We construct two explicit infinite families. In the first, the Myrheim–Meyer change tends to zero while the midpoint-scaling change grows as Θ(log n). In the second, midpoint scaling is exactly invariant while the Myrheim–Meyer change tends to a positive constant. The separations persist under standard endpoint-counting conventions, a longest-path formulation of midpoint scaling, and common finite normalizations of the Myrheim–Meyer statistic. Thus neither estimator's vanishing sensitivity uniformly forces vanishing sensitivity of the other under strict one-cover edits. The result is combinatorial and worst-case; the witness families are not asserted to be manifoldlike causal sets.

Fields of Study & Subjects

Bayesian Modeling and Causal Inference Topological and Geometric Data Analysis Random Matrices and Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
20 August 2026
Language
EN
Document type
Preprint