Information Sufficiency for Conditional University Ranking Decisions Under the QS Reform

Ranking reforms can leave changes in university positions unresolved when published scores are rounded, missing or difficult to compare across editions. We examine which observations suffice to certify improvement or worsening across all admissible configurations in a conditional Quacquarelli Symonds (QS) reform scenario for a fixed cohort of 1,422 institutions. The analysis retains source-edition legacy-score intervals, applies the announced reform weights and varies disclosure of International Research Network, Employment Outcomes and Sustainability scores. Publication-constrained marginal bounds screen the cohort, while mixed-integer linear programming constructs jointly feasible extrema and counterexamples. Competitor disclosure supports substantially more direction certificates than focal disclosure alone. Exhaustive comparison establishes minimum disclosure within declared field subsets, and selected competitor observations sharply reduce information volume without establishing global cell-level minimality. Marginal and joint optimization agree on the tested classifications; uncertified marginal results remain inconclusive. Sensitivity checks show material dependence on retaining legacy scores, while tested linkage and shared-scale variants preserve the audited directions. The findings support reproducible information-sufficiency audits under explicit assumptions; prospective forecasting utility remains unvalidated.