Reforming Rotations
In many settings, tasks are assigned to agents via a rotation. Such quasi-random allocation ignores heterogeneity in agents’ performance and preferences. We study how a designer can exploit such heterogeneity to improve aggregate performance while simultaneously ensuring that no agent is worse off relative to the status-quo system. The key challenge is that, while the designer may be able to estimate agents’ performance, their preferences are inherently unobservable. We develop a mechanism-design framework to study this problem and characterize optimal mechanisms in both static and dynamic settings. Optimal mechanisms can be interpreted as competitive equilibria in which agents trade tasks facing personalized, kinked budget sets. As an illustration, we apply our results to the assignment of Child Protective Services investigators to maltreatment cases. Simulations show that the mechanism reduces false positives (unnecessary foster care placements) by up to 14% while also lowering false negatives (missed maltreatment cases) and overall placements.
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Copy CitationE. Jason Baron, Richard Lombardo, Joseph P. Ryan, Jeongsoo Suh, and Quitze Valenzuela-Stookey, "Reforming Rotations," NBER Working Paper 32369 (2024), https://doi.org/10.3386/w32369.Download Citation
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