An Information Utility Based on Estimated Variance
Abstract
Improved knowledge-gradient allocation still depends on how observation uncertainty is learned. Unknown-variance ranking makes that dependency explicit. Evaluation should vary pilot precision and retain complete measurement paths, identifying alternatives starved after a misleading early estimate. A better utility calculation cannot compensate for uncertainty inputs that were never adequately measured. 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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