A Simulation Framework for Studying the Social Impacts of Algorithm-Based Refugee Matching

The integration chances of refugees in their host country are critically shaped by the contextual conditions of the location to which they are assigned upon arrival. Several research groups have developed algorithmic tools to optimize refugee-location matching, with the overall aim of improving refugees’ integration outcomes. These tools are used in a highly sensitive context and thus their design, social impacts, and potential long-term consequences need to be systematically assessed. To investigate such effects, we propose an agent-based simulation framework that allows to simulate different allocation mechanisms and to study their impacts on integration outcomes. We illustrate the simulation framework in the German context by comparing the current approach of the Königsteiner Schlüssel (i.e., quasi-random allocation) with the algorithm-based procedure GeoMatch. We study each procedures’ impacts on both labor market and social integration and assess structural effects on inequalities between subgroups of asylum seekers. The decision models and agents’ characteristics are based on the IAB-BAMF-SOEP survey of asylum seekers and refugees in Germany. Our study shows how agent-based models can be used to study unintended consequences of algorithmic allocations of asylum seekers in dynamically changing social environments.