AI-Enhanced Agent-Based Modelling Approach for Forced Displacement Predictions

Abstract

The increasing occurrence and complexity of forced displacement require robust predictive models to aid humanitarian responses. However, existing predictive models for forced displacement lack accurate and timely data, have gaps in existing datasets, struggle with the unpredictability of human behaviour, do not account for rapidly evolving political and environmental factors and introduce methodological uncertainties. Hence, this paper proposes a theoretical discussion of an artificial intelligence (AI)-enhanced agent-based modelling (ABM) approach to assist effective humanitarian planning and efficient resource allocation. This novel approach aims to predict the movements of internally displaced people and the arrival of forcibly displaced individuals in neighbouring countries. Importantly, it introduces a combination of quantitative models and qualitative insights from expert knowledge, along with humanitarian reports. Our AI-enhanced ABM approach (i) uses an agent-based simulation tool, Flee, incorporating behavioural assumptions and customisable rulesets for scenario modelling, (ii) explores innovative near real-time data sources from geospatial data and social media activity to satellite imagery with AI techniques, and (iii) discusses the ABM model with AI-generated inputs to enhance the granularity, accuracy, and reliability of predictions.