Recent Publications

Optimal Bailouts in  Diversified Financial Networks, Review of Economic Studies, forthcoming 2026 (with Krishna Dasaratha & Santosh Venlatesh).

Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks.

College Applications with Non-Homogeneous Application Costs, ACM Transactions on Economics and Computation, Volume 14, Issue 2, 2026 (with Eshwar Arunachaleswaran & Sampath Kannan).

A college applicant submits costly applications to a set of colleges while uncertain which colleges will admit her. If she knows the application costs, her utility of attending each college, and her probability of being admitted to each college, which subset of colleges should she apply to maximize her expected net payoff? The problem is an instance of non-monotone submodular maximization. There are two principal variants in the literature, one where the events of getting into different colleges are independent, and the other where they are correlated. The main results in the literature handle cases where all application costs are the same. We provide exact and approximate algorithms for the problem when college application costs vary.

On Inner Independence Systems, Naval Research Logistics, vol 72, 133–147, 2025 (with Stephan Raach  & Sven de Vries).

A classic result of Korte and Hausmann [1978] and Jenkyns [1976] bounds the quality of the greedy solution to the problem of finding a maximum value basis of an independence system (𝐸,I) in terms of the rank-quotient. We extend this result in two ways. First, we apply the greedy algorithm to an inner independence system contained in I. Additionally, following an idea of Milgrom [2017], we incorporate exogenously given prior information about the set of likely candidates for an optimal basis in terms of a set 𝒪⊆I. We provide a generalization of the rank-quotient that yields a tight bound on the worst-case performance of the greedy algorithm applied to the inner independence system relative to the optimal solution in 𝒪. Furthermore, we show that for a worst-case objective, the inner independence system approximation may outperform not only the standard greedy algorithm but also the inner matroid approximation proposed by Milgrom [2017]. Second, we generalize the inner approximation framework of independence systems to inner approximations of packing instances in ℤ𝑛 by inner polymatroids and inner packing instances. We consider the problem of maximizing a separable discrete concave function and show that our inner approximation can be better than the greedy algorithm applied to the original packing instance. Our result provides a lower bound to the generalized rank-quotient of a greedy algorithm to the optimal solution in this more general setting and subsumes Malinov and Kovalyov [1980]. We apply the inner approximation approach to packing instances induced by the FCC incentive auction and by two knapsack constraints.

Bayesian Bullshit, Journal of Mechanism and Institution Design,  Volume 9 (1), 2024 (with Sajan Srivastava & Tymofiy Mylovanov).

A bullshitter neither knows nor cares about the truth, and therefore, it has been asserted, is more pernicious than a liar. We examine this assertion within the standard model of cheap talk communication where a bullshitter is modeled as an uninformed Sender. We show that in some circumstances, uncertainty about whether the Sender is informed or not can increase the welfare of the Receiver.