COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Active Shooter: An Agent-based Model of Unarmed Resistance – Thomas Briggs
COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR
Active Shooter: An Agent-Based Model of Unarmed Resistance
Thomas Briggs, CSS PhD Student
Department of Computational and Data Sciences
George Mason University
Friday, November 11, 2016, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall
Mass shootings unfold quickly and are rarely foreseen by victims. Increasingly, training is provided to increase chances of surviving active shooter scenarios, usually emphasizing, “Run, Hide, Fight.” Evidence from prior mass shootings suggests that casualties may be limited should the shooter encounter unarmed resistance prior to the arrival of law enforcement officers (LEOs). An agent-based model (ABM) explored the potential for limiting casualties should a small proportion of potential victims swarm a gunman, as occurred on a train from Amsterdam to Paris in 2015. Results suggest that even with a miniscule probability of overcoming a shooter, fighters may save lives but put themselves at increased risk. While not intended to prescribe a course of action, the model suggests the potential for a reduction in casualties in active shooter scenarios.