Evolutionary game of emergency logistics path selection under bounded rationality

The occurrence of emergencies and secondary disasters causes varying degrees of obstruction on roads used by actors in an emergency rescue logistics network, and the bounded rationality of rescuers in the face of road risks considerably affects the choice of emergency rescue paths. In this regard, this study considered the traffic obstruction caused by emergencies and secondary disasters and the bounded rationality of rescue workers using a framework that combines cumulative prospect theory (CPT) and evolutionary game (EG) theory. The concept of a replicator was used to dynamically describe the game learning behaviors reflected in rescuers' path selection (PS) decisions, and an EG model was constructed to represent the multi-strategy set of limited rational rescuers. An example is presented to illustrate the dynamic evolution of PS and conduct a sensitivity analysis of parameters. The results showed that the EG model could determine the optimal path (stability strategy) on the basis of road conditions and the number of rescue vehicles traveling along a road network. Factors such as the type and severity of a secondary disaster, the time-related risks faced by rescuers, and the perception of road conditions tremendously affect the PS strategies of rescuers.