Gday! My name is Nawaaz, and I'm a PhD student at the University of Pennsylvania. I will be on the job market in 2022/2023. You can find my vitae here and placement file here.

I study economic theory, specifically information acquisition, mechanism design, and game theory. My recent work focuses on the use of costly inspection to acquire information through screening and discovery. You can read more about it here.

My academic advisors are:

  • Rakesh Vohra, George A. Weiss and Lydia Bravo Weiss University Professor, and

  • George Mailath, Professor of Economics, Walter H. Annenberg Professor in the Social Sciences.

You can reach me at

Working Papers

Optimal Allocation with Noisy Inspection [draft] [slides]

In a standard principal-agent allocation model, endow the agent with a noisy private signal about the principal's return and allow the principal to inspect the return at a cost. The inspection and allocation mechanism that maximizes the principal's expected return without the use of transfers describes optimal inspection as both an exploration and a screening tool. This relates to many important applied settings including employer hiring strategies, public grant mechanisms and portfolio investment rules.

Bonus material: [simple summary] [short slides]

Strategic Private Exploration [abstract] [draft forthcoming]

In a strategic exploration game, multiple players determine the order in which they explore unknown options with the objective of maximizing the sum of discovered rewards. Exploration is private in the sense that players cannot condition the order in which they explore on their competitor's decisions. Equilibrium exploration procedures are determined, and losses characterized as a function of how the rewards are split when simultaneously explored. This informs us about many areas of policy design including patent and copyright regimes, R&D tournaments, and competition regulation.

Pandora's Linear Program w/ Rakesh Vohra [abstract] [draft forthcoming]

In this article, we map Weitzman's canonical search problem into a linear program that allows us to re-derive existing results and extend the setup to problems pertaining to strategic search, information acquisition, index manipulation and robust search.