Robust Information Acquisition Design

Abstract

We study the design of information acquisition games-environments where a designer contracts their action on Sender's choice of experiment and the realized signals about some state-and identify which predictions can be made absent knowledge about the prior. To do so, we characterize robust mechanisms: those which induce the same allocation rule (mappings from the state to actions) for all priors. These mechanisms take a simple form: they (1) incentivize fully revealing experiments, (2) depend only on the induced posterior, and (3) maximally punish pooling deviations. In binary action problems, all (and only) ordinally monotone allocation rules are robust. We apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences depend on students' unknown ability. For general good allocation problems, we show all efficient allocations are robust, even when agent preferences feature state-dependent outside options and allocation externalities.