Abstract: We study reputation formation where a long-run player repeatedly observes private signals and takes actions. Short-run players observe the long-run player's past actions but not her past signals. The long-run player can thus develop a reputation for playing a distribution over actions, but not necessarily for playing a particular mapping from signals to actions. Nonetheless, we show that the long-run player can secure her Stackelberg payoff if distinct commitment types are statistically distinguishable and the Stackelberg strategy is confound-defeating. This property holds if and only if the Stackelberg strategy is the unique solution to an optimal transport problem. If the long-run player's payoff is supermodular in one-dimensional signals and actions, she secures the Stackelberg payoff if and only if the Stackelberg strategy is monotone.  An application of our results provides a reputational foundation for a class of Bayesian persuasion solutions when the sender has a small lying cost. Our results extend to the case where distinct commitment types may be indistinguishable but the Stackelberg type is salient under the prior.

Abstract: I study dynamic contracting where a principal (Sender) privately observes a Markovian state and seeks to motivate an agent (Receiver) who takes actions. Sender can both use payments to augment ex-post payoffs or persuasion to alter the informational environment as ways to provide incentives. For any stage-game payoffs, cost of transfers, rate of future discounting, and Markov transition rule, optimal transfers are backloaded—payments occur only when Sender commits to reveal the state at all continuation histories. In a rideshare example, the optimal contract is a loyalty program: drivers receive the static optimal information structure until a random promotion time, after which the state is fully revealed and only payments are used to motivate the driver.

Abstract: We study economies where consumers interact independently with many monopolists but have correlated values over goods. We rank correlation structures by their distributions of information rents and identify which ones lead to fairer distributions of consumer surplus. We then study the role taxation can have on surplus distribution, and characterize the fairness-efficiency tradeoff induced by policies that tax and subsidize across goods. We identify regularity conditions on the distribution of valuations under which the tax authority never benefits from randomizing the allocation of goods. Furthermore, we show under these conditions that all allocations on the fairness-efficiency frontier ration the good at prices higher than the monopoly price whenever the monopolist serves at least half the market. Finally, we discuss implications of our model for luxury commodity taxation. 

Abstract: I study reputation formation in repeated games where player actions endogenously determine the probability the game permanently ends. Permanent exit can render reputation useless even to a patient long-lived player whose actions are perfectly monitored, in stark contrast to canonical commitment payoff theorems. However, I identify tight conditions for the long-run player to attain their Stackelberg payoff in the unique Markov equilibrium. Along the way, I highlight the role of Markov strategies in pinning down the value of reputation formation. I apply my results to give qualified commitment foundations for the infinite chain-store game. I also analyze repeated global games with exit, and obtain new predictions about regime survival.

Abstract: We study persuasion games—environments where Receiver 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 char- acterize robust mechanisms: those which induce the same allocation rules (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. This characterization uncovers a tight rela- tionship between ordinal preference uncertainty and prior-independent predictions— allocation rules are robust 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 ro- bust in binary action problems. We then apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences de- pend on students’ unknown ability. Finally, in general good allocation settings, we show all efficient allocations are robust, even when agent preferences feature state-dependent outside options and allocation externalities.

Abstract: What is the optimal order in which a researcher should submit their papers to journals of differing quality? I analyze a sequential search model without recall where the researcher's expected value from journal submission depends on the history of past submissions. Acceptances immediately terminate the search process and deliver some payoff, while rejections carry information about the paper's quality, affecting the researcher's belief in acceptance probability over future journals. When journal feedback does not change the paper's quality, the researcher's optimal strategy is monotone in their acceptance payoff. Submission costs distort the researcher's effective acceptance payoff, but maintain monotone optimality. If journals give feedback which can affect the paper's quality, such as through referee reports, the search order can change drastically depending on the agent's prior belief about their paper's quality. However, I identify a set of assortatively matched conditions on feedback such that monotone strategies remain optimal whenever the agent's prior is sufficiently optimistic.

Description: We study repeated delegation problems where the principal msut pay a cost to monitor the agent. When the agent has state-independent preferences, the principal solves a standard optimal quantization problem, which we characterize using tools from information theory. When the agent and principal's preferences are both supermodular, we give conditions under which interval delegation is optimal, and explore comparative statics of the degree of flexibility afforded to the agent as the cost of monitoring decreases. We use our result to speak to workplace organizations in the age of AI. 

Description: Coming Soon!