Abstract: Every year, tens of thousands of refugees and asylum seekers are resettled in host countries across the world. In many host countries, newcomers are assigned to a specific locality (e.g., city) upon arrival by a resettlement agency. This assignment decision has a profound long-term impact on integration outcomes. The high-level goal of this line of work is to improve these outcomes through prediction and optimization algorithms.

We will describe two new dynamic assignment algorithms to dynamically match refugees and asylum seekers to geographic localities within a host country. The first---currently implemented in a multi-year pilot in Switzerland---achieves near-optimal expected employment (and improves upon the status quo procedure by about 40%). However, it can result in an imbalanced allocation to the localities over time, which creates undesirable workload inefficiencies for resettlement agencies. To address this problem, the second algorithm—currently being deployed in the US—balances the goal of improving outcomes with the desire for a balanced allocation over time. We will also discuss extensions of these methods that improve predictive performance in the face of non-stationarity, and enhance robustness and fairness across demographic groups.