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  • 2022
  • Working Paper
  • HBS Working Paper Series

Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

By: Kirk Bansak and Elisabeth Paulson
  • Format:Print
  • | Language:English
  • | Pages:48
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Abstract

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 pilot 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. Although the proposed algorithm achieves nearoptimal expected employment compared to the hindsight-optimal solution (and improves upon the status quo procedure by about 40%), it results in a periodically imbalanced allocation to the localities over time. This leads to undesirable workload inefficiencies for resettlement resources and agents. To address this problem, the second algorithm balances the goal of improving refugee outcomes with the desire for an even allocation over time. The performance of the proposed methods is illustrated using real refugee resettlement data from a large resettlement agency in the United States. On this dataset, we find that the allocation balancing algorithm can achieve near-perfect balance over time with only a small loss in expected employment compared to the pure 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.

Keywords

AI and Machine Learning; Refugees; Mathematical Methods

Citation

Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Harvard Business School Working Paper, No. 23-048, January 2022.
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About The Author

Elisabeth C. Paulson

Technology and Operations Management
→More Publications

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    By: Lu Chen, Donovan Guittieres, Retsef Levi, Elisabeth Paulson, Georgia Perakis, Nicholas Renegar and Stacy Springs
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    Reverse Information Sharing: Reducing Costs in Supply Chains with Yield Uncertainty

    By: Pavithra Harsha, Ashish Jagmohan, Retsef Levi, Elisabeth Paulson and Georgia Perakis
More from the Authors
  • Group Fairness in Dynamic Refugee Assignment By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
  • Public Health Risks Arising from Food Supply Chains: Challenges and Opportunities By: Lu Chen, Donovan Guittieres, Retsef Levi, Elisabeth Paulson, Georgia Perakis, Nicholas Renegar and Stacy Springs
  • Reverse Information Sharing: Reducing Costs in Supply Chains with Yield Uncertainty By: Pavithra Harsha, Ashish Jagmohan, Retsef Levi, Elisabeth Paulson and Georgia Perakis
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