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.
@article{arxiv.2111.04368,
title = {A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models},
author = {Peter Strong and Alys McAlpine and Jim Q Smith},
journal= {arXiv preprint arXiv:2111.04368},
year = {2021}
}