Machine Learning to Derive Complex Behaviour in Agent-Based Modellzing

The use of machine learning algorithms to enrich agent-based models has increased over the past years. This integration adds value when combining the advantages of the data-driven approach and the possibilities to explore future situations and human interventions. However, this integrating is still in its infant stage. Full integration of learning algorithms and agent-based models is often technically challenging and can make the behavioural rules of the agents less transparent. Experiments are needed in which different integration strategies are compared using the same agent-based model to determine when each of these approaches is most effective. In this paper, we present a comparison of two versions of the same cholera model. In the initial version, agent behaviour was driven directly by a learning algorithm. In our experiments, we replace this strategy by applying a learning algorithm directly on the data and implement the outcomes as behaviour rules in the model. The results showed that when the integration aims to create agents that show characteristics that are data-driven, deriving rules based on these data is a good alternative. In addition, a key element in this strategy is the dataset. A large dataset representing the behaviour of different types of agents over the complete time period is needed.