A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models

TL;DR

This paper introduces a Bayesian framework using Chain Event Graphs to improve inference and validation in Agent-Based Models of migration, enabling more principled analysis of complex decision-making processes.

Contribution

It presents a novel method to transform Agent-Based Models into Bayesian models using Chain Event Graphs, enhancing their inferential and validation capabilities.

Findings

Transforming ABMs into Bayesian models improves inference accuracy.

CEGs provide a structured way to represent complex migration decision processes.

The approach facilitates validation of ABMs against real-world data.

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.