This document describes an agent-based model that simulates active shooter scenarios to analyze how unarmed resistance, in the form of a small percentage of individuals confronting the shooter, might impact casualties. The model represents a landscape with randomly located shooters and agents that flee or fight. Results show that fighters can potentially save lives but at increased risk to themselves, and that their impact depends on factors like the number confronting the shooter and proximity. The authors conclude the model provides initial insights but requires further development, calibration and validation.

1. Active Shooter: An
Agent-Based Model of
Unarmed Resistance
Tom Briggs | William G. Kennedy
Dec 14, 2016 | Winter Simulation Conference 2016

2. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

3. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

4. 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?

5. Research
question
To what degree might the rapid
action of a few individuals who
physically confront a shooter
limit casualties in mass
shooting scenarios?

6. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

7. 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

8. Precedent

9. Precedent
Source: Blair, J. P., Martaindale, M. H., & Nichols, T. (2014). Active shooter events from 2000 to 2012.
FBI Law Enforcement Bulletin.

10. 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

11. 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

12. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

13. 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

14. Parameters control shooter armament & accuracy;
percentage of fighters and win probabilities.

15. Assumptions
Shooter fires one round
per second (likely
overestimate)
Hit likelihood linear
function of range and
distance
Rounds keep traveling

16. 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

17. Control / baseline condition: No fighters.

18. Control / baseline condition: Model run ends after 3m40s. Median LEO response time: 3 min (FBI).

19. 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

20. Parameters
Parameter Values Notes
population 500 1000 5000
7500
Agent population
%-who-fight 0.001 0.003
0.005 0.010
Percentage of agent population who
are “fighters” rather than “fleers”
chance-of-overcoming-
shooter
0.01 0.05 0.10 Per-tick probability of a fighter
overcoming the shooter in a hand-
to-hand struggle
shooters 1 Number of shooters
shooter-magazine-capacity 10 Rounds that can be fired before a
magazine reload (shooters have
unlimited magazines)
firearm-effective-range 30m 50m 70m Range at which a 100% accurate
shooter will hit target 50% of the
time; 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 of
each shot
field-of-view 180 degrees shooter’s field of view (see section
3.2)
shooter-chance-of-
overcoming-fighter
0.5 Per-tick probability of shooter
overcoming a fighter in a hand-to-
hand struggle

21. Verification
and Validation
Challenging
Hayes & Hayes (2014)
compared Aurora model to
actual casualties
Possibility of online
games, maybe VR, but
these lack situational
realism

22. “All models are wrong. Some are useful.”
-George Box

23. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

24. Sample run of experimental condition: ½ of 1% fight. Shooter overcome in 30 sec; 19 casualties.

25. Overall
results
0
20
40
60
0 100 200 300
Time (seconds)
NumberofCasualties
Control (No Fighters) Shooter not subdued Shooter subdued
Mean casualties: 30
Mean time: 100s
Mean casualties: 63
Mean casualties: 57
Mean time: 255s

26. 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

27. Agenda
Motivation
Background
Model
Results
Conclusions and Future Work

28. 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

29. 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

30. Tom Briggs
tbriggs@gmu.edu
Twitter: @twbriggs
www.twbriggs.com
Thank you
Project details:
http://bit.ly/shooterABM

31. 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.
References