Designing Persuasive Experiments

Abstract

Incentives in experimental design are often misaligned: experimenters design and finance experiments to seek regulatory approval, while regulators seek to maximize social-welfare. We propose a framework to resolve this conflict, wherein regulators set a minimum welfare threshold, and experimenters optimize designs subject to this constraint. It requires no knowledge of experimenters' private preferences or costs and mitigates strategic Bayesian persuasion. Under normal priors, Neyman-allocation is always the optimal-sampling strategy, regardless of specific objectives. We also characterize the optimal stopping-rule. A numerical study calibrated to clinical-trial data shows sample-size reductions of over 48% relative to classical designs attaining the same social-welfare.