Integrating Actual Human Behavior into an Agent-Based School Shooting Simulation

With the ever-growing threat of school shootings, modeling these tragedies is crucial to mitigate or reduce casualties in future events. However, agent-based models traditionally instruct agents to act based on theoretical behavior rather than actual human behavior. We present the results of 81,000 simulations of a school shooting where agent behavior is modeled after actual human behavior from a similar virtual scenario. Agents' reaction time and movement speed are drawn from probability distributions based on human data. The pathways assigned to agents are based on the human behavior exhibited in response to three social influence conditions: where the non-player characters all ran, all hid, or both ran and hid. Additionally, we manipulate law enforcement dispatch time, shooter accuracy, and magazine capacity. Results show mixed agent behavior and lower dispatch times had the largest influence on casualties. The methodology demonstrates the power of empirically defining agent behavior in ABMs.