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Active Shooter: An Agent-Based Model of Unarmed Resistance
Tom Briggs | William G. KennedyDec 14, 2016 | Winter Simulation Conference 2016
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Motivation
Mass shootings rare but increasing
Mass shooting difficult to study; difficult to predict or prevent
Increased active shooter training: Run, Hide, Fight
Systems thinking / complexity science perspective?
Research question
To what degree might the rapid action of a few individuals who physically confront a shooter limit casualties in mass shooting scenarios?
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Active shooters & mass
shootings
2000 – 2013: U.S. FBI reported 160 active shooter incidents; 486 killed and 557 wounded
Difficult to predict when or where they occur – shooters generally have informational advantage and element of surprise
Median LEO response time: 3 min
Precedent
Precedent
Source: Blair, J. P., Martaindale, M. H., & Nichols, T. (2014). Active shooter events from 2000 to 2012. FBI Law Enforcement Bulletin.
Prior model: Hayes &
Hayes (2014)
Constructed ABMs investigating details of Senator Feinstein’s proposed weapons bill
Reproduced 2012 Aurora, CO movie theater shooting
Variable that matters most in “number shot” is firearm rate of fire
Prior model: Anklam et al
(2015)
Armed school LEOs and/or staff carrying concealed firearms present
On entering room with CCW staff or LEO, active shooter neutralized
Neutralization assumption may be overly optimistic in light of studies of shooting performance (Lewinski et al., 2015)
No distinction between LEOs and civilians; no possibility of intercept by unarmed individuals
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Overall model
Open landscape (concert or outdoor rally)
Randomly-located shooter begins firing
Parsimony: fired shot can hit only one victim and no lethality determination is made
Most agents flee; small proportion of “fighters” attempt to tackle shooter
Parameters control shooter armament & accuracy; percentage of fighters and win probabilities.
Assumptions
Shooter fires one round per second (likely overestimate)
Hit likelihood linear function of range and distance
Rounds keep traveling
Round hit likelihood(accuracy)
Three factors:
-Distance between shooter and target
-Shooter accuracy – human component of shooting performance (user can set at 1.0, if desired)
-Firearm effective range – range at which 100% accurate shooter hits target 50% of the time
Control / baseline condition: No fighters.
Control / baseline condition: Model run ends after 3m40s. Median LEO response time: 3 min (FBI).
Fighters & shooter
Fighters within 1 sec range (running) attempt to tackle the shooter
On reaching shooter, struggle begins –shooter shifts attention from targeting victims to fighter
Likelihood of fighter overcoming shooter depends on multitude of factors, so user sets probabilities
Parameters
Parameter Values Notespopulation 500 1000 5000
7500Agent population
%-who-fight 0.001 0.003 0.005 0.010
Percentage of agent population whoare “fighters” rather than “fleers”
chance-of-overcoming-shooter
0.01 0.05 0.10 Per-tick probability of a fighterovercoming the shooter in a hand-to-hand struggle
shooters 1 Number of shootersshooter-magazine-capacity 10 Rounds that can be fired before a
magazine reload (shooters haveunlimited magazines)
firearm-effective-range 30m 50m 70m Range at which a 100% accurateshooter will hit target 50% of thetime; used in hit probability
shot-accuracy 0.5 0.8 1.0 Human factor in accuracy;combines with firearm-effective-range to determine hit probability ofeach shot
field-of-view 180 degrees shooter’s field of view (see section3.2)
shooter-chance-of-overcoming-fighter
0.5 Per-tick probability of shooterovercoming a fighter in a hand-to-hand struggle
Verification and Validation
Challenging
Hayes & Hayes (2014) compared Aurora model to actual casualties
Possibility of online games, maybe VR, but these lack situational realism
“All models are wrong. Some are useful.”
-George Box
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Sample run of experimental condition: ½ of 1% fight. Shooter overcome in 30 sec; 19 casualties.
Overall results
0
20
40
60
0 100 200 300Time (seconds)
Num
ber o
f Cas
ualti
es
Control (No Fighters) Shooter not subdued Shooter subdued
Mean casualties: 30Mean time: 100s
Mean casualties: 63
Mean casualties: 57Mean time: 255s
Results
Casualties concentrated at beginning due to distance and delay
Flee vs. fight – 0.1 vs. 0.4 vs. 0.8
Firearm effective range (30 vs. 50 vs. 70 m) had little effect on casualties
Fighters at a distance at severe disadvantage – implications for ambushing LEO entry teams
Agenda
Motivation
Background
Model
Results
Conclusions and Future Work
Conclusions
Fighters will potentially save lives but increase their own risk
Attention is a scarce commodity
Run / hide helps give LEO more time to arrive and sweep, but historical evidence (VA Tech, Sandy Hook) suggests hardened targets will be bypassed for softer targets
Future work
Model extension / criticism
Rapid collective action / swarm attack
Threshold model for fighters (i.e., only attack once certain number of others do)
Calibrate to SME input
Anklam, Charles, Adam Kirby, Filipo Sharevski, and J. Eric Dietz. 2015. “Mitigating Active Shooter Impact: Analysis for Policy Options Based on Agent/computer-Based Modeling.” Journal of Emergency Management 13 (3): 201–16. doi:10.5055/jem.2015.0234.Blair, John Peterson, M. Hunter Martaindale, and Terry Nichols. 2014. “Active Shooter Events from 2000 to 2012.” FBI Law Enforcement Bulletin. https://leb.fbi.gov/2014/january/active-shooter-events-from-2000-to-2012.Blair, John Peterson, and Katherine W. Schweit. 2013. “A Study of Active Shooter Incidents, 2000-2013.” https://hazdoc.colorado.edu/handle/10590/2712.Hayes, Roy, and Reginald Hayes. 2014. “Agent-Based Simulation of Mass Shootings: Determining How to Limit the Scale of a Tragedy.” Journal of Artificial Societies and Social Simulation 17 (2): 5.Lewinski, William J., Ron Avery, Jennifer Dysterheft, Nathan D. Dicks, and Jacob Bushey. 2015. “The Real Risks during Deadly Police Shootouts Accuracy of the Naïve Shooter.” International Journal of Police Science & Management, 117–27.Police Executive Research Forum. 2014. The Police Response to Active Shooter Incidents.Vickers, Joan N., and William Lewinski. 2012. “Performing under Pressure: Gaze Control, Decision Making and Shooting Performance of Elite and Rookie Police Officers.” Human Movement Science 31 (1): 101–17. doi:10.1016/j.humov.2011.04.004.Wilensky, Uri. 1999. NetLogo. Center for Connected Learning and Computer-Based Modeling. Evanston, IL: Northwestern University. http://ccl.northwestern.edu/netlogo.
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