Input Uncertainty in an Adoption and Avoidance Portfolio
Abstract
Best-and-worst subsets define both attractive and undesirable candidates, while input uncertainty can alter those classifications. Evaluation should preserve scenario-specific membership and distinguish false adoption from false avoidance. New input observations and simulator replications purchase different evidence. Precise nominal interior ranks cannot defend the entire portfolio across plausible operating distributions. 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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