The Value of a Measurement within a Fixed Design List
Abstract
An improved knowledge-gradient utility values a prospective observation through its final decision. A fixed-design opportunity-cost procedure makes the consequential loss explicit but leaves discovery outside the task. Evaluation should retain the candidate list and compare independently measured losses, distinguishing information efficiency inside the list from the quality of the list itself. 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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