Agent-based modeling for migration and modern slavery research: a systematic review

Alys McAlpine ORCID logo; Ligia Kiss ORCID logo; Cathy Zimmerman ORCID logo; Zaid Chalabi; (2020) Agent-based modeling for migration and modern slavery research: a systematic review. Journal of Computational Social Science, 4 (1). pp. 243-332. ISSN 2432-2717 DOI: 10.1007/s42001-020-00076-7
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<jats:title>Abstract</jats:title><jats:p>This systematic review aims to synthesize how agent-based modeling (ABM) has been used in migration and modern slavery research and provide the basis to model development for social science researchers exploring the use of ABM. We searched five bibliographic databases using two terminology categories: (1) migration or modern slavery terminology; (2) complex system methods terminology. Two reviewers conducted independent article screening. Peer-reviewed articles presenting original migration or modern slavery ABMs were included. Data extraction included model development steps and model characteristics. The dataset was synthesized and compared across studies. We identified 28 articles for inclusion. Many of the ABMs tested theories and about half were based on empirical data. Model development varied considerably and reported methods were extremely opaque. Only five studies used a structured development framework. The most common model involved agents deciding whether and where to migrate and attempting migration. Climate change was a common exogenous scenario modeled. Most of the ABMs did not undergo any sensitivity analysis or validation.ABM has a greater capacity to account for heterogeneous and dynamic decision-making than more frequently applied methods in research on migration and modern slavery. However, there is still a paucity of studies adopting ABM methods. These reviewed ABMs highlight gaps in the reporting and implementing of model development. ABM is a promising technique to address many urgent and complex questions in research on migration and modern slavery to better support decision-makers, but addressing current methodological gaps is a critical first step.</jats:p>


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