Input Learning before a Joint Feasibility Claim
Abstract
Multiple-constraint feasibility and input-uncertain allocation address different evidence gaps. Simulator replications estimate joint outcomes conditional on inputs; new input observations improve the driving distribution. Evaluation should count both and identify the limiting constraint. A narrow conditional simulation interval cannot establish admissibility under an input process it has not represented. 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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