结果驱动的动态难民分配与分配平衡
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
AI总结：
本研究提出两种动态难民分配算法，一种最大化就业结果，另一种平衡结果与分配均衡，后者在保持近优就业的同时实现时间上的均衡分配，并具有鲁棒性和探索性优势。
摘要：
本研究提出了两种新的动态分配算法，用于将难民和寻求庇护者匹配到东道国境内的地理区域。第一种算法目前在瑞士的一项多年期随机对照试验中实施，旨在通过一种最小不一致的在线分配算法，最大化难民的平均预测就业水平（或任何感兴趣的结果指标）。该算法的性能在美国和瑞士的真实难民重新安置数据上进行了测试，我们发现，与事后最优解相比，它能够实现接近最优的预期就业，并且能够比现状程序提高40-50%。然而，纯粹的结果最大化可能导致随着时间的推移，分配给各地区的结果周期性失衡，从而导致实施困难，并为重新安置资源和工作人员带来不良的工作流程。为了解决这些问题，第二种算法平衡了改善难民结果的目标与随时间均匀分配的愿望。我们发现，该算法能够实现随时间近乎完美的平衡，与就业最大化算法相比，预期就业仅略有损失。此外，与纯粹的结果最大化相比，分配平衡算法还提供了许多附带好处，包括对未知到达流的鲁棒性和更大的探索性。
英文摘要：
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year randomized control trial in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. The performance of this algorithm is tested on real refugee resettlement data from both the US and Switzerland, where we find that it is able to achieve near-optimal expected employment compared to the hindsight-optimal solution, and is able to improve upon the status quo procedure by 40-50%. However, pure outcome maximization can result in a periodically imbalanced allocation to the localities over time, leading to implementation difficulties and an undesirable workflow for resettlement resources and agents. To address these problems, the second algorithm balances the goal of improving refugee outcomes with the desire for an even allocation over time. We find that this algorithm can achieve near-perfect balance over time with only a small loss in expected employment compared to the employment-maximizing algorithm. In addition, the allocation balancing algorithm offers a number of ancillary benefits compared to pure outcome maximization, including robustness to unknown arrival flows and greater exploration.
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