arXiv subjects

Terence Highsmith

Publications and source records attributed to Terence Highsmith.

3 recordsLinked to original sources

arXiv2026

Do Preferences Matter in Balanced Task Allocation?

I model balanced task allocation where tasks stochastically arrive and must be matched to a fixed set of agents; the novel constraint is that agents must receive allocations that require the same level of average effort. Social work supervisors, call center managers, and courts all rotate allocation across workers to satisfy this constraint, but the Rotation mechanism is not Pareto efficient. I design the Dynamic Pseudomarket (DPM) mechanism, and it satisfies Pareto efficiency and asymptotic balance. I derive an explicit equation characterizing DPM's expected productivity gain over Rotation that can be estimated only from aggregate statistics in firm-level data. Simulation results indicate large average productivity gains. These results indicate that preference-based allocation can Pareto dominate the status quo.

econ.TH↗

arXiv2026

How to Use Prices for Efficient Online Matching

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 provide simulation evidence that SEM can substantially improve welfare.

econ.TH↗

arXiv2024

A Dynamic Matching Framework for Faster Child Adoptions

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.

econ.TH↗