Evolutionary game analysis of emergency rescuer dispatching under bounded rationality

In the initial stage of an emergency, rescue resources, such as rescuers, are often scarce, and victims at disaster sites under the condition of bounded rationality demonstrate competitive psychology in relation to the dispatch of rescue workers. Based on this, this study incorporates factors such as the bounded rationality of the disaster-stricken populations and their competitive psychology in terms of the number of rescuers into the analytical framework from the perspective of the disaster-stricken people and constructs an evolutionary game model with a multi-strategy set. The dynamic evolution process (EP) and the best rescuer dispatching scheme for each disaster site are shown through an example, and a disturbance analysis of the identified parameters is then carried out. The results reveal that the proposed rescuer dispatching model considers the multistage dynamic features of the emergency rescue process and the bounded rational game psychology among disaster sites, resulting in a more realistic dispatch scheme. For three different scenarios, such as the distance between the rescue point and the disaster site being relatively far and relatively close, and the subsequent rescue stage when the disaster situation is preliminarily alleviated, decision makers should make dispatch plans with timeliness, demand satisfaction, and comprehensive consideration of timeliness and demand satisfaction as the main decisions. • Emergencies and secondary disasters are considered in emergency rescuer dispatching. • Considering the multi-stage characteristics of rescue personnel scheduling in the early stage of emergency rescue. • Prospect theory, cumulative prospect theory and evolutionary game theory are applied to emergency rescuer dispatching. • A multi-strategy set evolutionary game model for rescuer dispatching under bounded rationality can obtain the stability strategy. • The three best dispatch schemes obtained from the preference coefficient analysis of decision makers can be applied to different rescue scenarios.