Agent-based modeling within forced migration research: A review and critique

The rapidly evolving landscape of data science and computational modeling has prompted social scientists and humanitarian practitioners to reevaluate in what ways these new methods can impact planning, decision-making, and scientific advancement. In the field of forced migration, there is a need to understand the computational options available and limitations of each. In this article, agent-based modeling as a computational approach to refugee-related issues is summarized. Applications of agent-based modeling within this realm are reviewed and critiqued to explain the limitations of agent-based modeling for the interest of social scientists and practitioners. The critique centers around several themes which include timeliness, computational skills and resources, data access, and validation of models. Additionally, a set of guidelines is provided for social scientists and practitioners to consider in what manner these computational models might be useful for their particular needs. Finally, we suggest that computational modeling and in particular, agent-based modeling, is a powerful tool for forced migration research where multiple factors, as well as environmental and political contexts, interact to make complex, dynamic systems. To effectively use agent-based
modeling, however, it is important to be knowledgeable of the limitations and considerable effort required to develop, test, and validate such models.