A Contradiction-Aware Survey Framework for Multi-Objective Decision Support

Abstract

Multi-objective decision support now spans classical scalarization, Pareto-evolutionary search, outranking-based multicriteria ranking, and uncertainty-aware formulations, but practical method selection remains unstable because evidence is fragmented across incompatible metrics, reporting protocols, and software defaults. This manuscript presents a contradiction-aware survey framework that unifies these families through a shared mathematical problem setting, explicit assumption gates, and a dual-layer recommendation rule separating candidate-set quality from stakeholder-facing utility. We formalize three linked contributions: an affine-normalization invariance and stability-guard criterion for recommendation robustness, a scalarization theorem gate that combines convex-regime completeness with a constructive non-convex counterexample boundary, and a descriptor-aware decision rule that integrates uncertainty and provenance penalties into utility judgments. We then assemble and analyze a synthesis validation package that reports calibrated instability thresholds, theorem-gate outcomes, transportability behavior under descriptor perturbations, and cross-framework lineage sensitivity. The resulting evidence supports bounded recommendations rather than universal rankings: two claims are strongly supported, while transportability claims remain conditional under implementation drift. The manuscript closes with a reproducibility and limitations synthesis that converts negative evidence into actionable follow-up protocols for future survey and benchmarking work.