An Agent–Based Model of Tax Compliance with Social Networks

In this paper, we use a computational modeling approach to examine the long–standing social issue of tax compliance. Specifically, we design an agent–based model—the Networked Agent–Based Compliance Model (NACSM)—where taxpayers not only exist within localized social networks, but also possess heterogeneous characteristics such as perceptions about the likelihood of audit and apprehension. When making compliance decisions, agents in our model factor in their neighbors' compliance strategy payoffs. We find that for a given enforcement regime, a world with limited knowledge of neighbor payoffs appears to lead to higher levels of aggregate compliance than when agents are aware of neighbor strategy payoffs and factor these into their individual compliance decisions. As this paper demonstrates the strength and initial results of our approach, we point to the need for further research using the NACSM approach and similar models as well as the development of higher fidelity agent–based compliance models.