Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

Abstract

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic.