Active shooter simulations: An agent-based model of civilian response strategy

Abstract

<p>Active shooter situations are becoming increasingly more common in the United States. From 2014 to 2015, there were 40 active shooter incidents, with a total of 231 casualties. Because of this increase in the frequency of incidents, it is important for law enforcement officers to be able to quickly respond to these situations. Quicker response times by law enforcement can result in fewer casualties. It is also important for civilians to be properly trained on how to react if they are in an active shooter situation themselves; all civilians must be able to assess the situation and know the most appropriate response strategy.</p> <p>This thesis describes an agent-based simulation model of an active shooter situation in a classroom environment. The simulation focuses on three parameters: law enforcement response time, civilian response strategy, and cognitive delay of the civilians. The model attempts to quantify the effect that these three parameters have on the number of casualties in an active shooter situation. The setting for the simulation model is a school building hallway with six individual classrooms.</p> <p>The work completed in this research will increase the awareness of the benefits of building agent-based models for active shooter situations. Simulation models can help law enforcement officers better prepare for a wide variety of scenarios that could occur in an active shooting. The results of the model indicate that a slower police response time and having all civilians hide can result in an increased number of civilian casualties. Increasing the cognitive delay of civilians (i.e., increasing the time until civilians react to a shooter situation) can also increase the number of casualties although the effect of this parameter is less than that of the other two parameters. These results can help guide both law enforcement and civilian training.</p>