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