Agent-based modeling of mass shooting case with the counterforce of policemen

Abstract

Mass shooting cases have caused large casualties worldwide. The counterforce, such as the policemen, is of great significance to reducing casualties, which is the core issue of social safety governance. Therefore, we model both the killing force and counterforce, to explore the crowd dynamics under the shooting. Taking the “Borderline” shooting in 2018 as the target case, the agent-based modeling is applied to back-calculate this dynamic process and explore key behavior rules of individuals. The real death tolls of three classes of agents (civilians, policemen, & killers) are as the real function, based on which we calculate the gaps between real target case and simulations. Eventually, we obtain three optimal solutions, which achieve the least gap or highest matching degree. Besides, we make counterfactual inferences under the optimal solutions, to explore the strategic interactions between policemen and killers. For strategies of killers, we explore different sizes, positions, and moving patterns of the killers. The optimal size of policemen is four to five, for each one killer. For strategies of policemen, we explore the size, locations, and the response time. It indicates that optimal response time of policemen is thirty to forty shots of the killer, and the death of civilians and policemen can be minimized, and the death probability of the killer can be maximized. These findings help to improve public safety governance for our cities. To effectively deal with sudden shooting terrorist cases, patrol routes, reasonable settings, and swift dispatches of the police (stations) should be considered.