Sequential Game Model for Urban Emergency Human–Machine Collaborative Decision-Making

Decision-making algorithms based on big data, artificial intelligence and other technologies are increasingly being applied to urban emergency decision-making, and urban smart emergency response is gradually appearing to be transformed from traditional empirical decision-making to human–machine collaborative decision-making. This paper explores the motivations for cooperative decision-making between leaders (human) and followers (machines) in urban emergency management in the presence of science and technology input spillovers. It focuses on the impact of human–machine cooperative decision-making on urban emergency response capacity, science and technology inputs and total urban emergency response benefits and discusses how to maximize the total benefits of urban emergency response under different levels of spillovers. In this paper, a three-stage dynamic game model is constructed: leaders and followers decide whether to establish a cooperative decision in the first stage; decide the level of science and technology inputs in the second stage; and compete for sequential decisions in the third stage. It was found that, firstly, unlike the case of static games, in sequential games, leaders and followers develop a willingness to cooperate in decision-making only when the spillover coefficients are in the lower range. Second, cooperative human–machine decision-making may diminish the importance of human experience in urban emergency management. Finally, the effectiveness of collaborative human–machine decision-making in urban emergencies deserves further research. The research in this paper provides recommendations for smart urban emergency management.