Abstract
Agent-Based Models (ABMs) are often used to model migration and are increasingly used to simulate individual migrant decision-making and unfolding events through a sequence of heuristic if-then rules. However, ABMs lack the methods to embed more principled strategies of performing inference to estimate and validate the models, both of which are of significant importance for real-world case studies. Chain Event Graphs (CEGs) can fill this need: they can be used to provide a Bayesian framework which represents an ABM accurately. Through the use of the CEG, we illustrate how to transform an elicited ABM into a Bayesian framework and outline the benefits of this approach.
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Acknowledgements
Peter Strong was supported by the EPSRC and the MRC [grant number EP/L015374/1]. Alys McAlpine was supported by UKRI [grant number ES/V006681/1] Jim Q. Smith was funded by the EPSRC [grant number EP/K03 9628/1]. We would like to thank Aditi Shenvi for her valuable comments.
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Strong, P., McAlpine, A., Smith, J.Q. (2022). Towards a Bayesian Analysis of Migration Pathways Using Chain Event Graphs of Agent Based Models. In: Argiento, R., Camerlenghi, F., Paganin, S. (eds) New Frontiers in Bayesian Statistics. BAYSM 2021. Springer Proceedings in Mathematics & Statistics, vol 405. Springer, Cham. https://doi.org/10.1007/978-3-031-16427-9_3
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