Regulators set welfare thresholds constraining experimenters' designs, making Neyman allocation optimal under normal priors and reducing sample sizes over 48% in calibrated simulations compared to classical approaches.
For eachz∈D[0,T], define τT,ξ(z) = inf{t:zt̸∈(b−(t),b +(t) +ξ)}∧T
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Designing Persuasive Experiments
Regulators set welfare thresholds constraining experimenters' designs, making Neyman allocation optimal under normal priors and reducing sample sizes over 48% in calibrated simulations compared to classical approaches.