Abstract: An informed seller designs a dynamic mechanism to sell an experience good. The seller has private information about product match, which affects the buyer's private consumption experience. The belief gap between both parties, coupled with the buyer's learning, yields mechanisms providing the skeptical buyer with limited access to the product and an option to upgrade if the buyer is swayed by a good experience. Depending on the seller's screening technology, this takes the form of free/discounted trials or dynamic tiered pricing, which are prevalent in digital markets. Unlike static environments, having consumer data can reduce sellers' revenue in equilibrium.

Abstract:    We study a sender-receiver game in which the receiver can commit to a decision rule before the sender determines the information policy. We ask how the receiver should commit, in advance, to a rule that maps the information of the sender into decisions—when the receiver knows neither the sender's true preferences nor the full range of information the sender could supply. To handle this dual uncertainty, we adopt a unified robust framework that nests max-min utility, min-max regret, and min-max competitive ratio as special cases. Across all criteria, the same answer emerges: the optimal rule is always a quota rule.

Abstract: This paper investigates a two-agent mechanism design problem without transfers, where the principal must decide one action for each agent. In our framework, agents only care about their own adaptation, and any deterministic dominant incentive compatible decision rule is equivalent to contingent delegation: the delegation set offered to one agent depends on the other's report. By contrast, the principal cares about both adaptation and coordination. We provide sufficient conditions under which contingent interval delegation is optimal and solve the optimal contingent interval delegation under fairly general conditions. Remarkably, the optimal interval delegation is completely determined by combining and modifying the solutions to a class of simple single-agent problems, where the other agent is assumed to report truthfully and choose his most preferred action. 

Abstract: A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data---whereby a consumer's data are predictive of others' behavior---generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large. 

Abstract:  A platform charges a producer for disclosing quality evidence to consumers before trade. It aims to maximize its revenue guarantee across potentially multiple equilibria which arise from the interdependence of producer purchase decisions and consumer beliefs. The platform's optimal pricing strategy entrenches itself as a market gatekeeper: it induces a unique equilibrium in which non-disclosed products’ perceived values are lower than the production cost. To achieve this goal, this pricing strategy iteratively destabilizes under-disclosure equilibria by luring producers to disclose slightly more. Higher-quality producers receive higher rents as their disclosure is prioritized. Despite losing rents, the platform optimally induces socially efficient information transmission for any given evidence structure, and it never benefits from garbling evidence. Compared to the non-robust benchmark, our framework generates more intuitive comparative statics: the platform’s ability to extract surplus increases with its value as an information intermediary.

Abstract: In a multi-agent setting, we study the optimal design of monitoring and compensation to uniquely implement work under contracting frictions. Our principal monitors workers flexibly but is constrained in the number of messages incorporated into the incentive contract. With only two messages, the optimal contract features two sub-teams competing for a bonus. Infrafirm competition allows workers to have a larger impact on their remuneration, implying lower wages are sufficient to incentivize effort. With more messages, partial misalignment of incentives enables the principal to extract the full surplus from a team whose size grows exponentially in the number of available messages. 

Abstract:  An employer contracts with a worker to incentivize efforts whose productivity depends on ability; the worker then enters a market that pays him contingent on ability evaluation. With non-additive monitoring technology, the interdependence between market expectations and worker efforts can lead to multiple equilibria (contrasting Holmström (1982/1999); Gibbons and Murphy (1992)). We identify a sufficient and necessary criterion for the employer to face such strategic uncertainty—one linked to skill-effort complementarity, a pervasive feature of labor markets. To fully implement work, the employer optimally creates private wage discrimination to iteratively eliminate pessimistic market expectations and low worker efforts. Our result suggests that present contractual privacy, employers' coordination motives generate within-group pay inequality. The comparative statics further explain several stylized facts about residual wage dispersion.

Abstract: I study the optimal pricing process for selling a unit good to a buyer with prospect theory preferences, which provides a theoretical rationale for loot box mechanisms observed in many industries such as gacha games. In the presence of probability weighting, the buyer is dynamically inconsistent and can be either sophisticated or naive about her own inconsistency. If the buyer is naive, the uniquely optimal mechanism is to sell a "loot box'" that delivers the good with some constant probability in each period. In contrast, if the buyer is sophisticated, the uniquely optimal mechanism introduces worst-case insurance: after successive failures in obtaining the good from all previous loot boxes, the buyer can purchase the good at full price.

Abstract:  We study how a privately informed decision maker (DM) uses recommendations from an AI system. We treat the AI as a certified but under-specified recommender system: a black box guaranteeing a minimum level of performance when its advice is followed. A conservative Bayesian DM combines the AI recommendation with her private signal to maximize worst-case performance consistent with that guarantee. We characterize when the DM ignores the AI recommendation, combines it with her expertise, or fully delegates to it, as a function of its guarantee. We then establish when AI is ``complementary''---by augmenting DM's expertise---or ``substitutive''---as the DM either ignores it or delegates to it.  We derive optimal robust strategies to use AI by reinterpreting the DM's problem as an optimal reliance on AI. Our key practical finding is that the value of AI depends on the form of expertise: experts with equal standalone productivity may use the same AI differently, generating different value from its advice. We characterize which types of experts are the best/worst match for the AI.

Abstract:  A hegemon imposes resource demands on agents who may nullify them through protests. Agents differ in endowments and protest costs; their protest success increases with the total mass of protesters, creating multiple equilibria. Under highest-resistance equilibrium selection, optimal hegemonic demands accommodate part of agents, making their acquiescence uniquely rationalizable. We recast optimal accommodation as optimal transport, and solve it via Kantorovich duality. We find that the optimal divide-and-conquer order is nonmonotone, in contrast to standard literature focusing on contracting-for-coordination. We also use dual potentials to characterize the set of accommodated agents, which is non-trivial with two dimensional heterogeneity. While coordinating on high resistance unambiguously benefits all agents for any fixed hegemonic demand, it may shift the optimal hegemonic demand and hurt some agents.