Developing a Discrete-Event Simulator of School Shooter Behavior from VR Data

TL;DR

This paper introduces a data-driven discrete-event simulator based on VR data to evaluate school shooter interventions, enabling scalable testing of strategies that are impractical to assess with real participants.

Contribution

It presents a novel VR-derived stochastic simulator for modeling shooter behavior, facilitating scalable evaluation of security interventions without extensive human trials.

Findings

Simulator reproduces key empirical patterns

Enables scalable evaluation of intervention strategies

Supports development of autonomous security measures

Abstract

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.