An Iterative Analysis Method Using Causal Discovery Algorithms to Enhance ABM as a Policy Tool.

Agent-based modeling (ABM) is becoming a popular policy tool by modeling the reasoning processes and interactive behaviors of individuals against external environments. However, the presence of heterogeneous agents, non-linear interactions and complex emergent patterns raised by even simple behavior rules pose challenges in the model explanation process. In this work, we propose a novel iterative analysis method that leverages causal discovery algorithms to facilitate policy formulation and evaluation based on a causal understanding of the model. It strengthens the explanation power of ABM by elucidating causal relations among modeled components. We applied the method to an agent-based simulator that models passengers’ routing behaviors in a virtual airport terminal. By discovering the causal relations among passengers’ goals, actions, and an airport terminal environment under different COVID-19 regulations, we showed that the method can inform more effective indirect-control policies leading to positive passenger experiences, compared with a conventional ABM analysis method.