Developing shooter movement models from virtual reality simulation

This research uses virtual reality (VR) to immerse human subjects in an active school shooting scenario in order to generate ecologically valid models of school shooter movement and behavior. Historically, data recovered from US school shootings has lacked the fidelity needed to model shooter movement; consequently, simulations of school shootings have had to rely on significant and unsupported assumptions about the movements of the shooter and/or victims. We asked human subjects to act as school shooters in a VR simulation. We then recorded their movements, observations, and actions. Our results show that participant shooters are statistically equivalent to historical incidents with respect to aggregate engagement metrics (shot rate, victim rate, and accuracy) across most scenarios. Moreover, empirical models trained on participant data reduced prediction error by at least 15.5% compared to heuristic baselines and 16.7% compared to models trained on pedestrian data. Overall, this work provides a reproducible framework for data-driven modeling of shooter movement, supporting controlled simulation-based evaluation of response strategies.