Research

• Peer Preferences in Centralized School Choice Markets: Theory and Evidence (with Natalie Cox, Bobak Pakzad-Hurson, Matthew Pecenco) - Reject & Resubmit at Journal of Political Economy

Abstract: School-choice clearinghouses often advise students to "rank their true preferences" despite not allowing students to express preferences over peers. We evaluate the consequences of doing so. Empirically, we find students have preferences over relative peer ability in the college admissions market in New South Wales, Australia. Theoretically, we show stable matchings exist even with peer preferences under mild conditions, but finding one via one-shot mechanisms is unlikely. The status quo procedure frequently employed by clearinghouses is to inform applicants about the assignment of students in the previous cohort, inducing a tâtonnement process which potentially provides useful information about likely peers in the current cohort. We theoretically argue this process likely leads to an unstable outcome, and we find instability in our empirical setting. We propose a mechanism that yields stability and incentivizes truthful reporting in the presence of peer preferences.

• Staggered Rollout for Innovation Adoption, 

Abstract: I consider a mechanism design approach to innovation adoption and show how it is optimal for the principal to induce artificial scarcity to speed it up. Take-up of a new product generates information about its value for others, so agents want to free-ride before irreversibly adopting it themselves. This causes a time-delay externality that a principal seeking to achieve an adoption target as quickly as possible (for example, a government trying to reach herd immunity through vaccination while agents are uncertain of their personal vaccination benefits, not internalizing the positive externality of reaching the adoption target)  seeks to avoid. Scarcity speeds up learning because it limits free-riding. I show that the possibility of imposing supply restrictions is always beneficial compared to free supply. I also show that optimal supply plans are simple in that there is a batched supply release with fewer batches than agents' value types. I fully characterize such optimal plans for settings with up to three types and show that (non-optimal) supply plans may be Pareto improving.

• Entry and Exit in School Choice Markets (with Bobak Pakzad-Hurson and Matthew Pecenco)

Abstract: Guided by matching theory, school choice markets are designed to generate stable matchings. The entry and exit of educational programs poses a barrier to stability if a long horizon is required for students to learn their preferences. In this paper, we study how entry and exit affect learning about a payoff relevant feature of educational programs: student quality. Theoretically, we show how entry and exit can inhibit stability. Empirically, using data from the college admissions market in New South Wales, Australia, we find gradual within-program convergence to stability and show how the persistent churn of programs in this marketplace inhibits overall-market convergence, leading to an unstable matching. This instability is primarily experienced by lower-ability students and those from marginalized groups, thus potentially increasing inequality.

•  Keeping Experts Honest 

Abstract: Decision-makers often rely on advice from specialists who possess superior information but whose preferences are not perfectly aligned with those of the decision-maker. A canonical example is the funding of scientific research by agencies such as the NIH or NSF, which depend on peer reviewers who may be biased toward particular methodologies or research programs. This paper studies how a principal who lacks monetary transfers can discipline a biased expert through commitment to future decision rules. In an infinite-horizon, discrete-time model with noisy binary signals and a known state-independent bias, I show that a simple randomized grim-trigger mechanism is optimal: the principal follows the expert’s recommendation whenever possible but, with a carefully chosen probability, commits to permanently ignoring the expert after any recommendation that proves incorrect. I characterize the optimal punishment probability as a function of the signal precision, prior, and bias, and establish optimality within the full class of history-dependent mechanisms under a natural patience condition. 

• Iterative Reasoning Through Heuristics (with G. De Clippel, P. Ortoleva, K. Rozen, G Rubbini) - Work in Progress

Advanced Microeconomics II (PhD), Experimental Economics (syllabus), Game Theory (syllabus) - Pontificia Universidad Javeriana

Real Analysis - Summer 2025 (syllabus) - Bogotá Summer School, with Andrés Carvajal

Math Camp (PhD, syllabus), Behavioral Game Theory: Experiments in Strategic Interaction (Pre-college, syllabus)- Brown University