A Constrained Winner under Competing Robust Objectives
Abstract
Constrained best-arm identification defines admissibility, while a multi-objective robust criterion defines preference across protected conditions. Evaluation should retain component outcomes and identify whether sampling resolves feasibility or objective uncertainty. A changed tradeoff can move the winner without any new evidence, so preference disagreement must remain distinct from statistical imprecision. 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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