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Active Shooter: An Agent-Based Model of Unarmed Resistance

Thomas W Briggs, William G Kennedy
Submitted By: twbriggs
Submitted: Dec 17, 2016
Last Updated: Apr 4, 2017
67 Downloads (17 Downloads in the last 3 months)
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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.

This model is associated with a publication:

Briggs, T. W., & Kennedy, W. G. (2016). Active shooter: An agent-based model of unarmed resistance. In Proceedings of the 2016 Winter Simulation Conference (WSC), 3521–3531.

Cite This Model:
Briggs, Thomas W, Kennedy, William G (2016, December 29). "Active Shooter: An Agent-Based Model of Unarmed Resistance" (Version 1). CoMSES Computational Model Library. Retrieved from:

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Model Version: 1
Version Notes:

Platform: NetLogo 5.3.1
Programming Language: Logo (variant)
Operating System: Platform Independent
Licensed Under: Apache License, Version 2.0
Instructions on Running This Model:

Available Model Versions
Version Number Submitted
1 12/29/2016
2 04/4/2017
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