AI 中文总结
研究任务随机到达的均衡任务分配问题，设计动态伪市场（DPM）机制，该机制满足帕累托效率和渐近均衡，推导出其生产率提升方程，模拟结果显示有大幅提升，表明基于偏好的分配可优化现状。
AI 中文摘要
我对均衡任务分配进行建模，任务随机到达且必须与固定的一组代理匹配，新约束是代理必须获得平均努力水平相同的分配。社会工作主管、呼叫中心经理和法院都通过在员工间轮换分配来满足此约束，但轮换机制并非帕累托有效。我设计了动态伪市场（DPM）机制，它满足帕累托效率和渐近均衡。我推导出一个明确方程，表征DPM相对于轮换在预期生产率上的提升，仅可从公司层面数据的汇总统计中估计。模拟结果显示平均生产率有大幅提升，表明基于偏好的分配可帕累托优于现状。
英文摘要
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.
