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

Christoph Kern, Jakob Kappenberger, Frederic Gerdon, Clara Strasser Ceballos, Daria Szafran, Florian Rupp, Ruben Bach
Proceedings of Fourth European Workshop on Algorithmic Fairness, PMLR 294:487-491, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v294-kern25a, title = {A Simulation Framework for Studying the Social Impacts of Algorithm-Based Refugee Matching}, author = {Kern, Christoph and Kappenberger, Jakob and Gerdon, Frederic and Ceballos, Clara Strasser and Szafran, Daria and Rupp, Florian and Bach, Ruben}, booktitle = {Proceedings of Fourth European Workshop on Algorithmic Fairness}, pages = {487--491}, year = {2025}, editor = {Weerts, Hilde and Pechenizkiy, Mykola and Allhutter, Doris and Corrêa, Ana Maria and Grote, Thomas and Liem, Cynthia}, volume = {294}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--02 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v294/main/assets/kern25a/kern25a.pdf}, url = {https://proceedings.mlr.press/v294/kern25a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T A Simulation Framework for Studying the Social Impacts of Algorithm-Based Refugee Matching %A Christoph Kern %A Jakob Kappenberger %A Frederic Gerdon %A Clara Strasser Ceballos %A Daria Szafran %A Florian Rupp %A Ruben Bach %B Proceedings of Fourth European Workshop on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2025 %E Hilde Weerts %E Mykola Pechenizkiy %E Doris Allhutter %E Ana Maria Corrêa %E Thomas Grote %E Cynthia Liem %F pmlr-v294-kern25a %I PMLR %P 487--491 %U https://proceedings.mlr.press/v294/kern25a.html %V 294 %X 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.
APA
Kern, C., Kappenberger, J., Gerdon, F., Ceballos, C.S., Szafran, D., Rupp, F. & Bach, R.. (2025). A Simulation Framework for Studying the Social Impacts of Algorithm-Based Refugee Matching. Proceedings of Fourth European Workshop on Algorithmic Fairness, in Proceedings of Machine Learning Research 294:487-491 Available from https://proceedings.mlr.press/v294/kern25a.html.

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