An Information Utility for Alternatives Still Being Discovered
Abstract
Knowledge-gradient improvement values a prospective measurement, while partition search expands the alternatives being compared. Evaluation should distinguish new regional coverage from replication of a known candidate. It fixes the terminal loss and reserves independent checks. A precise comparison inside the current list is incomplete evidence when discovery remains a consequential part of the task. 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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