Understanding Human Decision Processes: Inferring Decision Strategies From Behavioral Data

This work investigates a method to infer and classify decision strategies from human behavior, with the goal of improving human-agent team performance by providing AI-based decision support systems with knowledge about their human teammate. First, an experiment was designed to mimic a realistic emergency preparedness scenario in which the test participants were tasked with allocating resources into I of 100 possible locations based on a variety of dynamic visual heat maps. Simple participant behavioral data, such as the frequency and duration of information access, were recorded in real time for each participant. The data were examined using a partial least squares regression to identify the participants' likely decision strategy, that is, which heat maps they relied upon the most. The behavioral data were then used to train a random forest classifier, which was shown to be highly accurate in classifying the decision strategy of new participants. This approach presents an opportunity to give AI systems the ability to accurately model the human decision-making process in real time, enabling the creation of proactive decision support systems and improving overall human-agent teaming.