  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.
