This paper studies the problem of experiment design by an ambiguity-averse decision-maker who trades off subjective expected performance against robust performance guarantees. This framework accounts for real-world experimenters’ preference for randomization. It also clarifies the circumstances in which randomization is optimal: when the available sample size is large and robustness is an important concern. We apply our model to shed light on the practice of rerandomization, used to improve balance across treatment and control groups. We show that rerandomization creates a trade-off between subjective performance and robust performance guarantees. However, robust performance guarantees diminish very slowly with the number of rerandomizations. This suggests that moderate levels of rerandomization usefully expand the set of acceptable compromises between subjective performance and robustness. Targeting a fixed quantile of balance is safer than targeting an absolute balance objective. (JEL C90, D81)

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This paper proposes a decision-theoretic framework for experiment design. We model experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters' preference for randomization, and clarifies the circumstances in which randomization is optimal: when the available sample size is large enough or robustness is an important concern. We illustrate the practical value of such a framework by studying the issue of rerandomization. Rerandomization creates a trade-off between subjective performance and robustness. However, robustness loss grows very slowly with the number of times one randomizes. This argues for rerandomizing in most environments.

We develop and estimate a model of learning that accounts for the observed correlation between economic development and democracy and for the clustering of democratization events. In our model, countries’ own and neighbors’ past experiences shape elites’ beliefs about the effects of democracy on economic growth and their likelihood of retaining power. These beliefs influence the choice to transition into or out of democracy. We show that learning is crucial to explaining observed transitions since the mid-twentieth century. Moreover, our model predicts reversals to authoritarianism if the world experienced a growth shock the size of the Great Depression.

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