Working Papers:

Dynamic Market Design forthcoming in Econometric Society monograph 2025 World Congress, Vol 2, Ch 3.

Abstract: Classic market design theory is rooted in static models where all participants trade simultaneously. In contrast, modern platform-mediated digital markets are fundamentally dynamic, defined by the asynchronous and stochastic arrival of supply and demand. This chapter surveys recent work that brings market design to this dynamic setting. We focus on a methodological framework that transforms complex dynamic problems into tractable static programs by analyzing the long-run stationary distribution of the system. The survey explores how priority rules and information policy can be designed to clear markets and screen agents when monetary transfers are unavailable, and, when they are available, how queues of participants and goods can be managed to balance intertemporal mismatches of demand and supply and to spread competitive pressures across time.

Allocating Students to Schools: Theory, Methods, and Empirical Insights (with Julien Grenet, Yinghua He), Ch. 4, Handbook of the Economics of Matching

Abstract: This chapter surveys the application of matching theory to school choice, motivated by the shift from neighborhood assignment systems to choice-based models. Since educational choice is not mediated by price, the design of allocation mechanisms is critical. The chapter first reviews theoretical contributions, exploring the fundamental trade-offs between efficiency, stability, and strategyproofness, and covers design challenges such as tie-breaking, cardinal welfare, and affirmative action. It then transitions to the empirical landscape, focusing on the central challenge of inferring student preferences from application data, especially under strategic mechanisms. We review various estimation approaches and discuss key insights on parental preferences, market design trade-offs, and the effectiveness of school choice policies.

Pandora’s Box Reopened: Robust Search and Choice Overload (with Sarah Auster)

Abstract: This paper revisits the classic Pandora’s box problem, studying a decisionmaker (DM) who seeks to minimize her maximal ex-post regret. The DM decides how many options to explore and in what order, before choosing one or taking an outside option. We characterize the regret-minimizing search rule and show that the likelihood of opting out often increases as more options become available for exploration. We show that this “choice overload” is driven by the DM’s fear of “selection error”—the regret from searching the wrong options— suggesting that steering choice via recommendations or cost heterogeneity can mitigate regret and encourage search.

Optimal Auction Design for Dynamic Stochastic Environments: Myerson Meets Naor (with Andy Choi)

Abstract: Allocation of goods and services often involves both stochastic supply and stochastic demand. Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study the design of optimal selling mechanisms in an environment where buyers with private valuations arrive stochastically and are assigned goods that also arrive stochastically, and either buyers or goods can be held in a queue at costs until allocation. The optimal mechanism dynamically leverages competitive pressure across time by managing the queue of buyers and inventory of goods, using reserve prices that increase with the number of buyers in the queue and decrease with the number of items in inventory, and an auction to allocate the goods.

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.

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.