I study multidimensional sequential screening. A monopolist contracts with a buyer who privately observes information about the distribution of their eventual valuations for multiple goods. After initial private information is reported and the contract is signed, the buyer learns and reports realized valuations. In these settings, the monopolist frontloads surplus extraction: Any information rents given to the buyer to elicit their true valuations can be extracted in expectation before those valuations are drawn, transforming the multidimensional screening problem by distorting buyer information rents compared to static screening. If the buyer's distributions over valuations are commonly FOSD ordered and regular for each good; and satisfy invariant dependencies (valuations can be dependent across goods, but how valuations are coupled cannot vary), the optimal mechanism coincides with independently offering the optimal sequential screening mechanism for each good. This rationalizes membership payments followed by separate sales schemes seen across multiple industries.

In many auctions, bidders may be reluctant to reveal private information to the auctioneer and other bidders. Among deterministic bilateral communication protocols, reducing what bidders learn requires increasing what the auctioneer learns. A protocol implementing a given social choice rule is on the Privacy Frontier if no alternative protocol reveals less to both bidders and the auctioneer. For first-price auctions, the descending protocol and the sealed-bid protocol are both on the Privacy Frontier. For second-price auctions, the ascending protocol and the ascending-join protocol of Haupt and Hitzig (2025) are both on the Privacy Frontier, but the sealed-bid protocol is not. We provide sufficient conditions for a protocol to be on the Privacy Frontier and devise alternative protocols on the Privacy Frontier for first-price auctions that allow the designer to flexibly trade off between privacy from bidders and the auctioneer.

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.

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.

Artificial intelligence (AI) tools such as large language models (LLMs) are already altering student learning. Unlike previous technologies, LLMs can independently solve problems regardless of student understanding, yet are not always accurate (due to hallucination) and face sharp performance cutoffs (due to emergence). Access to these tools significantly alters a student's incentives to learn, potentially decreasing the sum knowledge of humans and AI. Additionally, the marginal benefit of learning changes depending on which side of the AI frontier a human is on, creating a discontinuous gap between those that know more than or less than AI. This contrasts with downstream models of AI's impact on the labor force which assume continuous ability. Finally, increasing the portion of assignments where AI cannot be used can counteract student mis-specification about AI accuracy, preventing underinvestment. A better understanding of how AI impacts learning and student incentives is crucial for educators to adapt to this new technology.

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.