arXiv subjects

Yeon-Koo Che

Publications and source records attributed to Yeon-Koo Che.

At least 19 recordsLinked to original sources

arXiv2026

First They Came for the Others: A Theory of Divide-and-Conquer

Divide-and-conquer tactics often succeed not through mechanical coordination failures, but through epistemic friction regarding an aggressor's underlying intent. When an attacker strikes a first target, bystanders must infer whether the assault represents a localized grievance or a systemic campaign. If the attack is rationally interpreted as particularized, bystanders abstain, prompting the isolated victim to surrender. We demonstrate how higher attack costs and lower correlation between victims' fates facilitate this division. We then study how behavioral responses, rhetoric, treaty commitments, and downstream defense networks modify this inference.

econ.TH↗

arXiv2026

Top Trading Cycles in Large Markets: The Asymptotic Irrelevance of Priorities

Top Trading Cycles (TTC) is Pareto efficient and strategy-proof and explicitly uses agents' priorities. Although TTC favors higher-priority agents in each round, we show that this priority advantage vanishes as the market grows large under a canonical random model of preferences and priorities. In the limit, TTC produces assignments with virtually the same incidence of justified envy as Random Serial Dictatorship (RSD) -- a mechanism entirely blind to priorities. This stark asymptotic equivalence implies that TTC effectively fails to satisfy standard fairness criteria in large markets, casting significant doubt on its practical appeal for balancing efficiency and fairness.

econ.TH↗

arXiv2026

Learning Against Nature: Minimax Regret and the Price of Robustness

We study how a decision-maker (DM) learns from data of unknown quality to form robust, ''general-purpose'' posterior beliefs. We develop a framework for robust learning and belief formation under a minimax-regret criterion, cast as a zero-sum game: the DM chooses posterior beliefs to minimize ex-ante regret, while an adversarial Nature selects the data-generating process (DGP). We show that, in large samples of $n$ signal draws, Nature optimally induces ambiguity by choosing a process whose precision converges to the uninformative signals at the rate $1/\sqrt{n}$. As a result, learning against the adversarial DGP is nontrivial as well as incomplete: the DM's ex-ante regret remains strictly positive even with an infinite amount of data. However, when the true DGP is fixed and informative (even if only slightly), our DM with a robust updating rule eventually learns the state with enough data. Still, learning occurs at a sub-exponential rate -- quantifying the asymptotic price of robustness -- and it exhibits ''under-inference'' bias. Our framework provides a decision-theoretic dual to the local alternatives method in asymptotic statistics, deriving the characteristic $1/\sqrt{n}$-scaling endogenously from the signal ambiguity.

econ.TH↗

arXiv2026

Dynamic Market Design

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.

econ.TH↗

arXiv2025

Pandora's Box Reopened: Robust Search and Choice Overload

This paper revisits the classic Pandora's box problem, studying a decision-maker (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.

econ.TH↗

arXiv2025

Allocating Students to Schools: Theory, Methods, and Empirical Insights

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 strategy-proofness, 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?

econ.GN↗

arXiv2025

Optimal Auction Design for Dynamic Stochastic Environments: Myerson Meets Naor

Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study optimal selling mechanisms for dynamic markets with stochastic supply and demand. In our model, buyers with private valuations and homogeneous goods arrive stochastically and can be held in queues at a cost. The optimal mechanism pairs allocative efficiency with dynamic admission control: goods are assigned to the highest-value buyer, while entry is restricted by value thresholds that strictly increase with the queue length and decrease with available inventory. This policy smooths competitive pressure across time and is implemented in dominant strategies via auctions with dynamic reserve prices.

econ.TH↗

arXiv2024

Data-Driven Monitoring and Deterrence in a Changing Environment

We study a dynamic model in which a principal monitors agents based on historical data of infractions. This data informs when and at what intensity to monitor; the monitoring decision, in turn, selects the collected data, shaping the principal's future learning. We analyze this feedback loop using a bandit model in which the underlying monitoring environment evolves according to a hidden Markov process. Because data collection is endogenous, how the principal uses this information is critical: surprisingly, a myopic approach renders historical data completely valueless. By endogenizing the agent's incentives, we demonstrate that the principal's purely informational motive to explore serves as an endogenous commitment device. This inherent drive to gather data compels persistent vigilance, strictly lowering the equilibrium infraction rate and restoring the power of deterrence.

econ.TH↗

arXiv2023

Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the mistakes in a deferred-acceptance matching environment. The key insight is that the uncertainties faced by applicants, e.g., due to tie-breaking lotteries, render some mistakes costly, allowing us to reliably infer relevant preferences. Our approach extracts all information on preferences robustly to payoff-insignificant mistakes. We apply it to school-choice data from Staten Island, NYC. Counterfactual analysis suggests that we underestimate the effects of proposed desegregation reforms when applicants' mistakes are not accounted for in preference inference and estimation.

econ.GN↗

arXiv2023

Optimal Queue Design

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.

econ.TH↗

arXiv2022

Prolonged Learning and Hasty Stopping: the Wald Problem with Ambiguity

This paper studies sequential information acquisition by an ambiguity-averse decision maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting prematurely when facing high uncertainty. In the latter case, the DM's stopping rule is non-monotonic in beliefs and features randomized stopping.

econ.TH↗

arXiv2022

Stable Matching with Mistaken Agents

Motivated by growing evidence of agents' mistakes in strategically simple environments, we propose a solution concept -- robust equilibrium -- that requires only an asymptotically optimal behavior. We use it to study large random matching markets operated by the applicant-proposing Deferred Acceptance (DA). Although truth-telling is a dominant strategy, almost all applicants may be non-truthful in robust equilibrium; however, the outcome must be arbitrarily close to the stable matching. Our results imply that one can assume truthful agents to study DA outcomes, theoretically or counterfactually. However, to estimate the preferences of mistaken agents, one should assume stable matching but not truth-telling.

econ.TH↗

arXiv2021

Robustly Optimal Mechanisms for Selling Multiple Goods

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 various moment conditions on the values of subsets of goods. The analysis reveals general properties of the ambiguity set that justifies categorical bundling, which includes separate sales and pure bundling as special cases.

econ.TH↗

arXiv2020

"Near" Weighted Utilitarian Characterizations of Pareto Optima

We characterize Pareto optimality via "near" weighted utilitarian welfare maximization. One characterization sequentially maximizes utilitarian welfare functions using a finite sequence of nonnegative and eventually positive welfare weights. The other maximizes a utilitarian welfare function with a certain class of positive hyperreal weights. The social welfare ordering represented by these "near" weighted utilitarian welfare criteria is characterized by the standard axioms for weighted utilitarianism under a suitable weakening of the continuity axiom.

econ.TH↗

arXiv2020

Bailout Stigma

We develop a model of bailout stigma where accepting a bailout signals a firm's balance-sheet weakness and worsens its funding prospect. To avoid stigma, high-quality firms either withdraw from subsequent financing after receiving bailouts or refuse bailouts altogether to send a favorable signal. The former leads to a short-lived stimulation with a subsequent market freeze even worse than if there were no bailouts. The latter revives the funding market, albeit with delay, to the level achievable without any stigma, and implements a constrained optimal outcome. A menu of multiple bailout programs also compounds bailout stigma and worsens market freeze.

q-fin.GN↗

arXiv2020

Statistical Discrimination in Ratings-Guided Markets

We study statistical discrimination of individuals based on payoff-irrelevant social identities in markets that utilize ratings and recommendations for social learning. Even though rating/recommendation algorithms can be designed to be fair and unbiased, ratings-based social learning can still lead to discriminatory outcomes. Our model demonstrates how users' attention choices can result in asymmetric data sampling across social groups, leading to discriminatory inferences and potential discrimination based on group identities.

cs.GT↗

arXiv2020

Keeping the Listener Engaged: a Dynamic Model of Bayesian Persuasion

We consider a dynamic model of Bayesian persuasion in which information takes time and is costly for the sender to generate and for the receiver to process, and neither player can commit to their future actions. Persuasion may totally collapse in a Markov perfect equilibrium (MPE) of this game. However, for persuasion costs sufficiently small, a version of a folk theorem holds: outcomes that approximate Kamenica and Gentzkow (2011)'s sender-optimal persuasion as well as full revelation and everything in between are obtained in MPE, as the cost vanishes.

econ.TH↗

arXiv2019

Monotone Comparative Statics without Lattices

The theory of Monotone Comparative Statics (MCS) has traditionally required a lattice structure, excluding certain multidimensional environments such as mixed-strategy games where this property fails. We show that this structure is not essential. We introduce a weaker notion, the pseudo-lattice property, and preserve the theory's core results by generalizing the MCS theorems for individual choice and Tarski's fixed-point theorem. Our framework expands comparative statics to pseudo quasi-supermodular games. Crucially, it enables the first MCS analysis of mixed-strategy Nash equilibria and trembling-hand perfect equilibria.

econ.TH↗