Proceedings of the 55th Hawaii International Conference on System Sciences | 2022The article of record at published may be found at https://hdl.handle.net/10125/80256Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-the- art Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The human- human interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decision- making than merely using classical probability theory (CPT). In this paper, we examine the HMI from a QPT perspective. Applying QPT to studying HMI for decision-making shows improvement in understanding the decision process when interacting with machines because it provides insights into the mental uncertainty of a human that is not apparent in CPT.This research is supported by Department of the Navy, Office of Naval Research, Consortium for Robotics Unmanned Systems Education and Research at the Naval Postgraduate School
