Multi-agent Modeling of Crowd Dynamics under Mass Shooting Cases
Terrorist attacks lead to huge social cost, and, especially, mass shooting cases result in massive deaths and injuries worldwide. Hence, it is necessary to explore the accurate and dynamic processes of mass shooting cases. Taking the Cinema Shooting case in 2012 as the real target case, the agent-based model is applied to explore the dynamics of human behaviors under common risk of shooting. Parameter traverse and repeated simulations can be used to obtain robust outcomes. According to real target case, the optimal solution with the highest matchiness can be obtained, which supports the validity and robustness of our agent-based model. Under this optimal solution, the same numbers of deaths and injuries can be achieved, and the dynamic process can be also back-calculated. Besides, we can infer counterfactual outcomes, which strengthens explanatory capability of our model. Hence, the multiple outcomes under different situations (settings or strategies) can be obtained, and optimal perception range R* can be solved as well. Combining agent-based model and counterfactual simulations, emergency responses, public governance, and facility designs will be improved.