A Bound for Adoption and an Explicit Avoidance Set
Abstract
A bound on loss for one selected design does not automatically price errors in both the best and worst subsets. The comparison should define adoption and avoidance consequences separately and validate the resulting retained objects. A false avoidance can remove a useful action even when the chosen winner is acceptable, making one generic correct-selection score incomplete. 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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