An Improved Information Utility before Myopic Learning Has Converged
Abstract
Changing knowledge-gradient utility can redirect early measurements even when a myopic procedure has a favorable limiting allocation. The evaluation should preserve complete paths and compare independently checked terminal choices at operating budgets. Posterior calculation quality and asymptotic allocation quality remain distinct from the observed consequence of the finite sequence. 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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