Measurement Utility and the Speed of Learning a Preferred Design
Abstract
Improved knowledge-gradient utility and convergence-rate analysis can explain different reasons an allocation becomes effective. The comparison should separate initialization, update cost, and later measurements under a common final loss. A theoretically fast learning sequence is useful only in relation to the practical budget range and the independently checked action it produces. 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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