Learning Event-Based Shooter Models from Virtual Reality Experiments
This work addresses the problem of scalable evaluation of school security measures for researchers and policymakers, though it is incremental by applying existing simulation methods to a new domain.
The paper tackled the challenge of evaluating school security interventions in VR by developing a data-driven discrete-event simulator that models shooter behavior from VR experiments, enabling scalable assessment of strategies like robot-based interventions without requiring new human participants for each condition.
Virtual reality (VR) has emerged as a powerful tool for evaluating school security measures in high-risk scenarios such as school shootings, offering experimental control and high behavioral fidelity. However, assessing new interventions in VR requires recruiting new participant cohorts for each condition, making large-scale or iterative evaluation difficult. These limitations are especially restrictive when attempting to learn effective intervention strategies, which typically require many training episodes. To address this challenge, we develop a data-driven discrete-event simulator (DES) that models shooter movement and in-region actions as stochastic processes learned from participant behavior in VR studies. We use the simulator to examine the impact of a robot-based shooter intervention strategy. Once shown to reproduce key empirical patterns, the DES enables scalable evaluation and learning of intervention strategies that are infeasible to train directly with human subjects. Overall, this work demonstrates a high-to-mid fidelity simulation workflow that provides a scalable surrogate for developing and evaluating autonomous school-security interventions.