Abstract: We propose a three-stage analysis procedure to interpret input-output relationships of agent-based simulation models. In the first stage, we use metamodels of agent-based models to approximate the input-output dynamics by constructing a functional relationship. For this purpose, we employ Random Forest technique from machine learning domain. Based on the results of an experimental analysis, we observe that Random Forest is an appropriate technique when the main aim is to clearly depict input-output dynamics with a satisfactory level of accuracy. In the second stage, we focus on efficient training of Random Forest metamodels.

We observe that a sequential sampling strategy considering feedbacks from the metamodel generates more accurate metamodels compared to metamodels trained on randomly selected input-output couples. Besides, the iterative training process of metamodels also gives valuable information about the dynamics of the model by depicting boundary points between different types of model behaviors and counterintuitive outcomes. In the last stage, we devise a rule extraction technique from Random Forest metamodels based on a set partitioning problem formulation. Finally, we apply the three-stage analysis technique to an agent-based influenza epidemic model to analyze the effectiveness of different combinations of intervention strategies in the presence of various transmissibility scenarios of influenza.

Biography: Mert Edalı received his B.Sc. degree in Industrial Engineering from Yıldız Technical University, İstanbul, in 2011. Later, he joined the Department of Industrial Engineering at Boğaziçi University as a master’s student and received his M.Sc. degree in 2014. He received his Ph.D. degree from the same department in 2019. Besides, he has been working as a research assistant and lecturer in the Department of Industrial Engineering at Yıldız Technical University since 2013. His main research interests include agent-based modeling, system dynamics, simulation model analysis, and machine learning.