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Placement Optimization in Refugee Resettlement

In a recently published paper, Professor Tommy Andersson, Affiliated Professor at Department of Economics, together with co-authors developed an innovative software tool, Annie™ Moore, integrating machine learning and integer optimization to support a US resettlement agency with their matching operations.

By Narges Ahani, Tommy Andersson, Alessandro Martinello, Alexander Teytelboym, Andrew C. Trapp 

The increasing number of refugees in need of resettlement, and a growing body of literature supporting the relevance of initial placement as a determinant of their long run outcomes, makes the question of how to optimize resettlement decision extremely salient.  The the paper "Placement Optimization in Refugee Resettlement" the authors developed an innovative software tool, Annie™ Moore, integrating machine learning and integer optimization to support a US resettlement agency with their matching operations. Abstract presented below.

Abstract

Every year, tens of thousands of refugees are resettled to dozens of host countries. Although there is growing evidence that the initial placement of refugee families profoundly affects their lifetime outcomes, there have been few attempts to optimize resettlement decisions. We integrate machine learning and integer optimization into an innovative software tool, Annie™ Matching and Outcome Optimization for Refugee Empowerment (Annie™ Moore), that assists a U.S. resettlement agency with matching refugees to their initial placements. Our software suggests optimal placements while giving substantial autonomy to the resettlement staff to fine-tune recommended matches, thereby streamlining their resettlement operations. Initial back testing indicates that Annie™ can improve short-run employment outcomes by 22%–38%. We conclude by discussing several directions for future work.

Dept. of Economics Development Artificial intelligence Governance Public economics Strategy Technology Economics Article Journal Publication Research

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