A regulator-constrained optimization framework for experimental design that makes Neyman allocation optimal under normal priors and achieves over 48% sample-size reduction relative to classical designs at the same welfare level.
For the second statement, we start by applying Lemma 1 to takeα= 1/2without loss of generality
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Designing Persuasive Experiments
A regulator-constrained optimization framework for experimental design that makes Neyman allocation optimal under normal priors and achieves over 48% sample-size reduction relative to classical designs at the same welfare level.