Multi-Objective Disagreement inside a Partitioned Feasible Region
Abstract
Partition regression approximates local outcomes, while robust multiple objectives determine which approximated alternatives are preferred. Evaluation should vary partition resolution separately from tradeoff weights and directly check favored boundaries. More precise prediction cannot resolve a substantive preference disagreement, and a new scalar weight cannot repair an unobserved local feasibility failure. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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