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.

翻译：虚拟现实（VR）已成为评估高风险场景（如校园枪击事件）中学校安全措施的有力工具，其具备实验可控性和高行为保真度。然而，在VR中评估新干预措施需要为每种实验条件招募新的参与者群体，这使得大规模或迭代评估变得困难。当尝试学习通常需要大量训练回合的有效干预策略时，这些限制尤为突出。为应对这一挑战，我们开发了一种数据驱动的离散事件模拟器（DES），该模型将枪手移动和区域内行动建模为从VR研究参与者行为中学习得到的随机过程。我们利用该模拟器研究了基于机器人的枪手干预策略的影响。一旦证明该DES能够复现关键经验模式，它便可实现干预策略的可扩展评估与学习，而这些策略直接通过人类受试者进行训练是不可行的。总体而言，本研究展示了一种高至中保真度的仿真工作流程，为开发和评估自主化校园安全干预措施提供了可扩展的替代方案。