Gday! My name is Nawaaz, and I'm a research fellow in the Impact Labs, at Monash University.  I received my PhD from the University of Pennsylvania, and will be on the job market in 2024/2025. You can find my vitae 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 postdoc is supervised by Simon Wilkie, Dean of the Faculty of Business and Economics and Head of Monash Business School, and my academic advisors are:

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.

Sequential Information Acquisition and Optimal Search w/ Rakesh Vohra [abstract] [draft forthcoming]

Consider the principal-agent allocation model from Optimal Allocation with Noisy Inspection, and suppose there are now many agents, each with a noisy signal of the principals return from allocating to them. The inspection and allocation mechanism that maximizes the principal's expected return now sequentially searches through the agents in a way that incentivises truthful reports . This essentially generalises Optimal Allocation with Noisy Inspection to many agents and Ben-Porath, Dekel, Lipman (2014) to noisy types.