BIO
I am a PostDoctoral Associate in the Division of Social Sciences at New York University Abu Dhabi (NYUAD). Previously, I obtained my PhD in Social Sciences from California Institute of Technology and MS in Quantitative Economics from Indian Statistical Institute (Delhi). My research interests lie in the field of microeconomic theory, with a focus on mechanism design, contest design, matching, and social choice.
RESEARCH
Working papers

Contest design with a finite typespace
(with Andrzej Baranski)
We study the classical contest design problem in an incomplete information environment with linear costs and a finite typespace. For any contest with an arbitrary finite typespace and distribution over this typespace, we characterize the unique symmetric BayesNash equilibrium of the contest game. We find that the equilibrium is in mixed strategies, where agents of different types mix over disjoint but connected intervals, so that more efficient agents always exert greater effort than less efficient agents. Using this characterization, we solve for the expected equilibrium effort under any arbitrary contest, and find that a winnertakesall contest maximizes expected effort among all contests feasible for a budgetconstrained designer. Our results extend the optimality of the winnertakesall contest under a continuum typespace (Moldovanu and Sela [2001]) to the finite typespace environment, and our analysis introduces new techniques for the study of contest design problems in such environments.

Optimality of weighted contracts for multiagent contract design with a budget
(with Wade HannCaruthers)
Ext. abs. in Proc. of EC 2024
We study a contract design problem between a principal and multiple agents. Each agent participates in an independent task with binary outcomes (success or failure), in which it may exert costly effort towards improving its probability of success, and the principal has a fixed budget which it can use to provide outcomedependent rewards to the agents. Crucially, each agent's reward may depend not only on whether she succeeds or fails, but also on whether other agents succeed or fail, and we assume the principal cares only about maximizing the agents' probabilities of success, not how much of the budget it expends.
We first show that a contract is optimal for some objective if and only if it gives no reward to unsuccessful agents and always splits the entire budget among the successful agents. An immediate consequence of this result is that piecerate contracts and bonuspool contracts, two types of contracts which are wellstudied and motivated in the literature on multiagent contract design, are never optimal in this setting. We then show that for any objective, there is an optimal prioritybased weighted contract, which assigns positive weights and priority levels to the agents, and splits the budget among the highestpriority successful agents, with each such agent receiving a fraction of the budget proportional to her weight. This result provides a significant reduction in the dimensionality of the principal's optimal contract design problem and gives an interpretable and easily implementable optimal contract.
Finally, we discuss an application of our results to the design of optimal contracts with two agents and quadratic costs. In this context, we find that the optimal contract assigns a higher weight to the agent whose success it values more, irrespective of the heterogeneity in the agents' cost parameters. This suggests that the structure of the optimal contract depends primarily on the bias in the principal's objective and is, to some extent, robust to the heterogeneity in the agents' cost functions. 
Optimal grading contests
Ext. abs. in Proc. of EC 2023
R&R at Games and Economic Behavior
We study the design of grading contests between agents with private information about their abilities under the assumption that the value of a grade is determined by the information it reveals about the agentâ€™s productivity. Towards the goal of identifying the effortmaximizing grading contest, we study the effect of increasing prizes and increasing competition on effort and find that the effects depend qualitatively on the distribution of abilities in the population. Consequently, while the optimal grading contest always uniquely identifies the best performing agent, it may want to pool or separate the remaining agents depending upon the distribution. We identify sufficient conditions under which a rankrevealing grading contest, a leaderboardwithcutoff type grading contest, and a coarse grading contest with at most three grades are optimal. In the process, we also identify distributions under which there is a monotonic relationship between the informativeness of a grading scheme and the effort induced by it.

Stable allocations in discrete exchange economies
(with Federico Echenique and SangMok Lee)
R&R at Journal of Economic Theory
We study stable allocations in an exchange economy with indivisible goods. The problem is wellknown to be challenging, and rich enough to encode fundamentally unstable economies, such as the roommate problem. Our approach stems from generalizing the original study of an exchange economy with unit demand and unit endowments, the housing model. Our first approach uses Scarf's theorem, and proposes sufficient conditions under which a ``convexify then round'' technique ensures that the core is nonempty. The upshot is that a core allocation exists in categorical economies with dichotomous preferences. Our second approach uses a generalization of the TTC: it works under general conditions, and finds a solution that is a version of the stable set.

Project selection with partially verifiable information
(with Wade HannCaruthers)
Ext. abs. in Proc. of WINE 2022
R&R at Mathematical Social Sciences
We study a principalagent project selection problem with asymmetric information. The principal must choose exactly one of $N$ projects, each defined by the utility it provides to the principal and to the agent. The agent knows all the utilities, and the principal can commit to a mechanism (without transfers) that maps the agent's report about the utilities to a chosen project. Unlike the typical literature, which assumes the agent can lie arbitrarily, we examine the principal's problem under partial verifiability constraints. We characterize the class of truthful mechanisms under a family of partial verifiability constraints and study the principal's problem for the specific cases of nooverselling and nounderselling. Our results suggest significant benefits for the principal from identifying or inducing such partial verifiability constraints, while also highlighting the simple mechanisms that perform well.
Publications

Optimal tiebreaking rules
(with Amit Goyal)
Journal of Mathematical Economics
We consider twoplayer contests with the possibility of ties and study the effect of different tiebreaking rules on effort. For ratioform and differenceform contests that admit purestrategy Nash equilibrium, we find that the effort of both players is monotone decreasing in the probability that ties are broken in favor of the stronger player. Thus, the effortmaximizing tiebreaking rule commits to breaking ties in favor of the weaker agent. With symmetric agents, we find that the equilibrium is generally symmetric and independent of the tiebreaking rule. We also study the design of random tiebreaking rules that are unbiased exante and identify sufficient conditions under which breaking ties before the contest actually leads to greater expected effort than the more commonly observed practice of breaking ties after the contest.

Optimality of the coordinatewise median mechanism for strategyproof facility location in two dimensions
(with Wade HannCaruthers)
Ext. abs. in Proc. of SAGT 2022
Social Choice and Welfare
We consider the facility location problem in two dimensions. In particular, we consider a setting where agents have Euclidean preferences, defined by their ideal points, for a facility to be located in \(\mathbb{R}^2\). We show that for the pnorm (\(p \geq 1\)) objective, the coordinatewise median mechanism (CM) has the lowest worstcase approximation ratio in the class of deterministic, anonymous, and strategyproof mechanisms. For the minisum objective and an odd number of agents \(n\), we show that CM has a worstcase approximation ratio (AR) of \(\sqrt{2}\frac{\sqrt{n^2+1}}{n+1}\). For the pnorm social cost objective (\(p\geq 2\)), we find that the AR for CM is bounded above by \(2^{\frac{3}{2}\frac{2}{p}}\). We conjecture that the AR of CM actually equals the lower bound \(2^{1\frac{1}{p}}\) (as is the case for \(p=2\) and \(p=\infty\)) for any \(p\geq 2\).
TEACHING
Instructor at NYU Abu Dhabi
 ECONUH 2010: Intermediate Microeconomics (Spring 2024)
Instructor at Econschool
Econschool is an initiative to assist undergraduate students in India who wish to pursue higher studies in Economics. As an instructor, I've taught a course on mathematics for economists and also helped with the development and delivery of various online resources.