
TL;DR
This paper develops a mathematical framework for ranking systems based on pairwise comparisons, analyzing influence, factor interactions, and geometric properties to better understand and interpret ranking models.
Contribution
It introduces a pairwise margin-based analysis, proving uniqueness of influence sharing, and extends to nonlinear scoring with path-dependent decompositions and geometric diagnostics.
Findings
L1 local influence share is uniquely determined by pure factor refinement.
Global influence structure forms a convex potential gradient with a competition-graph Laplacian.
Factorwise path attribution is path-independent only in the additive case.
Abstract
Ranking systems produce ordered lists from scalar scores, yet the ranking itself depends only on pairwise comparisons. We develop a mathematical theory that takes this observation seriously, centering the analysis on pairwise margins rather than absolute scores. In the linear case, each pairwise margin decomposes exactly into factor-level contributions. We prove that the resulting L_1 local influence share is the unique budgeting rule consistent with pure factor refinement. Aggregating local shares yields a global influence structure: in log-absolute-weight coordinates, this structure is the gradient of a convex potential, its Jacobian is a competition-graph Laplacian, and Influence Exchange -- the reallocation of pairwise control across model states -- satisfies a finite energy identity with a zero-exchange rigidity law. For nonlinear scoring, the pairwise margin remains…
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