Working Papers:

Predictive Enforcement, (with Jinwoo Kim and Konrad Mierendorff)

Abstract: We study law enforcement guided by data-informed predictions of “hot spots” for likely criminal offenses. Such “predictive” enforcement could lead to data being selectively and disproportionately collected from neighborhoods targeted for enforcement by the prediction. Predictive enforcement that fails to account for this endogenous “datafication” may lead to the over-policing of traditionally high-crime neighborhoods and performs poorly, in particular, in some cases as poorly as if no data were used. Endogenizing the incentives for criminal offenses identifies additional deterrence benefits from the informationally efficient use of data.

Prestige Seeking in College Application and Major Choice (with Dong Woo Hahm, Jinwoo Kim, Se-jik Kim, and Olivier Tercieux)

Abstract: We develop a signaling model of prestige seeking in competitive college applications. A prestigious program attracts high-ability applicants, making its admissions more selective, which in turn further increases its prestige, and so on. This amplifying effect results in a program with negligible quality advantage enjoying a significant prestige in equilibrium. Furthermore, applicants “sacrifice” their fits for programs in pursuit of prestige, which results in the misallocation of program fits. Major choice data from Seoul National University provides evidence for our theoretical predictions when majors are assigned through competitive screening—a common feature of college admissions worldwide.

Robustly-Optimal Mechanisms for Selling Multiple Goods (with Weijie Zhong)

Abstract: We study robustly-optimal mechanisms for selling multiple items. The seller maximizes revenue against a worst-case distribution of a buyer’s valuations within a set of distributions, called an “ambiguity” set. We identify the exact forms of robustly-optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies a variety of moment conditions on the values of subsets of goods. We also identify general properties of the ambiguity set that lead to the robust optimality of partial bundling, which includes separate sales and pure bundling as special cases.

Optimal Queue Design (with Olivier Tercieux): online appendix Slides, Recordings: Short (15min); VSET; Columbia IEOR-DRO

Abstract: We study the optimal method for rationing scarce resources through a queue system. The designer controls agents’ entry into a queue and their exit, their service priority—or queueing discipline—as well as their information about queue priorities, while providing them with the incentive to join the queue and, importantly, to stay in the queue, when recommended by the designer. Under a mild condition, the optimal mechanism induces agents to enter up to a certain queue length and never removes any agents from the queue; serves them according to a first-come-first-served (FCFS) rule; and provides them with no information throughout the process beyond the recommendations they receive. FCFS is also necessary for optimality in a rich domain. We identify a novel role for queueing disciplines in regulating agents’ beliefs and their dynamic incentives and uncover a hitherto unrecognized virtue of FCFS in this regard.

Weak Monotone Comparative Statics (with Jinwoo Kim and Fuhito Kojima):

Abstract: We develop a theory of monotone comparative statics based on weak set order, or in short \textit{weak monotone comparative statics}, and identify the enabling conditions in the context of individual choices, Pareto optimal choices for a coalition of agents, and Nash equilibria of games. Compared with the existing theory based on strong set order, the conditions for weak monotone comparative statics are weaker, sometimes considerably, in terms of the structure of the choice environment and underlying preferences of agents. We apply the theory to establish the existence and monotone comparative statics of Nash equilibria in games with strategic complementarities and of stable many-to-one matchings in two-sided matching problems, allowing for general preferences that accommodate indifferences and incomplete preferences.

Statistical Discrimination in Ratings-Guided Markets (with Kyungmin Kim and Weijie Zhong):

Abstract: We study statistical discrimination of individuals based on payoff-irrelevant social identities in markets where ratings/recommendations facilitate social learning among users. Despite the potential promise and guarantee for the ratings/recommendation algorithms to be fair and free of human bias and prejudice, we identify the possible vulnerability of ratings-based social learning to discriminatory inferences on social groups. In our model, users' equilibrium attention decisions may lead data to be sampled differentially across different groups so that differential inferences on individuals may emerge based on their group identities. We explore policy implications in terms of regulating trading relationships as well as algorithm design.