Do Preferences Matter in Balanced Task Allocation? [arXiv] [latest draft]
In balanced task allocation, a firm allocates stochastically arriving tasks to workers. Balance requires that workers complete the same number of tasks or have equally difficult task allocations. Foster care agencies, U.S. courts, and other firms rotate allocation across workers to satisfy balance. Yet, the Rotation mechanism is not Pareto efficient. I design the Dynamic Pseudomarket (DPM) mechanism, and it satisfies Pareto efficiency, asymptotic balance, and strategic robustness in equilibrium. I develop a sufficient statistic that estimates second-order effects on productivity because DPM could decrease it in practice. An online lab experiment verifies that the theoretical results are consistent: worker welfare increases, and the sufficient statistic correctly predicts the direction of productivity. These results pave the way for testing and research in high-stakes fields like foster care.
Presentations: POMS (2026), Kellogg Theory Summer School 2026 (preliminary poster)
Note: The latest draft is a non-archival version containing preliminary results from a non-preregistered, pilot experiment.
How to Use Prices for Efficient Online Matching [link]
Many matching markets feature unknown, dynamic arrivals of agents that must match immediately. A caseworker must match an abused child to a foster home, a hospital must assign a patient in critical condition to a room, or a city must place a homeless individual into a shelter. We design an online matching algorithm---the Sequential Equilibrium Mechanism (SEM)---that approximates large market equilibria to match arriving agents to objects. SEM is asymptotically efficient, fair, and strategy-proof with probability one. Our application plans to deploy a lab-in-the-field experiment where real caseworkers match vulnerable children to host homes, and we currently provide simulation evidence that SEM can substantially improve welfare.
Presentations: Conference on Mechanism and Institution Design (2026)
A Dynamic Matching Framework for Faster Child Adoptions [link]
Caseworkers in foster care systems match waiting children to adoptive homes. We use dynamic matching market design to characterize a class of mechanisms that incentivize expedient matches that homes can accept or decline. We design mechanisms satisfying fairness and limited strategy-proofness. They also avoid costly patience. Our empirically-based simulations suggest the mechanisms could increase adoptions by at least 25% versus the status quo. A naive dynamic extension of Deferred Acceptance does not attain these benefits. Our mechanisms sidestep direct preference elicitation by predicting preferences, and they are robust to prediction error.
How Are Good Matches Made in Foster Care? (pre-analysis draft available upon request)
In the United States, caseworkers match neglected and abused children to temporary foster homes in decentralized markets with uncertainty over match quality. I theoretically and experimentally study the impact of supply and demand in foster care. There are not enough family foster homes for children (supply-side constraint), and caseworkers representing children often have limited time to deliberate matches (demand-side constraint). I build a theoretical framework that shows how supply and demand could impact match quality. Alleviating the demand and supply constraints could either increase or worsen child welfare, suggesting that policies aimed at recruiting foster homes must be paired with effective interventions to aid caseworker decision-making. In a novel experiment design, I recruit real caseworkers to participate in foster care matching markets while varying these constraints.