We analyze prior-free predictions in the design of persuasion games: settings where Receiver contracts their action on Sender's choices of experiment and realized signals about some state. To do so, we characterize robust mechanisms - those which induce the same allocation rules (mappings from the state to actions) regardless of prior beliefs. 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. We then highlight a tight connection between ordinal preference uncertainty and prior-dependent predictions - all such rules are implementable if and only if the sender has a state-independent least favorite action. This, in turn, implies all (and only) ordinally monotone allocation rules are robust in binary action problems. We apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences depend on students' unknown ability. Finally, we study good allocation settings with externalities and state-dependent outside options and show all efficient allocation rules are robust, even with significant preference heterogeneity.

Serial dictatorship is efficient for any given one-sided matching problem, but may not be if there are multiple markets under consideration. One environment where this phenomenon is welfare-relevant is in course and dorm allocation at universities, where serial dictatorship is often used interdependently in each market. This paper introduces and considers paired serial dictatorship, an adaptation of serial dictatorship for problems where two goods are allocated simultaneously. Paired serial dictatorship allows students to first report relative preferences between courses and dorms, which then influence their priority in either market. I find that paired serial dictatorship induces screening along relative preferences and is generally welfare-improving compared to running random serial dictatorship independently for courses and dorms.

I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes the marginal cost per probability of making the product go viral. These results rationalize startup behavior in practice. 

Addiction is a major societal issue leading to billions in healthcare losses per year. Policy makers often introduce ad hoc quantity limits-limits on the consumption or possession of a substance-something which current economic models of addiction have failed to address. This paper enriches Bernheim and Rangel (2004)'s model of addiction driven by cue-triggered decisions by incorporating endogenous choice of how much of the addictive good to consume, instead of just whether or not consumption happens. Stricter quality limits improve welfare as long as they do not preclude the myopically optimal level of consumption. 

We analyze two-sided asymmetric matching markets on 7 Cups, a site for social-emotional support where users in need of help can request to be matched with volunteer listeners who have the sole power to accept requests. The aim of this paper is to analyze user incentives to characterize what their dominant strategies are when deciding what to reveal when requesting a conversation. Listeners are treated as myopic in our model, with their only actions being to accept matches that work and terminate conversations that become undesirable for them. We find truth-telling to be a dominant strategy up to sufficiently small misrepresentations. Finally, we propose implementable suggestions to improve match outcomes. 

One assumption behind "paired" kidney exchange is that each patient can only bring one donor into the matching algorithm. However, many individuals may have multiple willing donors. We adapt top trading cycles and unpaired exchange to this situation and find significant welfare gains even if only one of a patent's multiple donors ends up donating.