Does information sharing affect the regulation of street vending: An analysis based on the dynamic tripartite evolutionary game in social networks

Abstract

Street vending enormously contributes to the economies in the global south, but its high probability of illegal operation, also has generated many spin-off problems. Moreover, both the government's attitude to street vending regulation and the willingness of public supervision oscillates. How these stakeholders interact in the street vending remain controversial and is worth further researching. We verify the conditions required in the model construction process through text and correlation analysis, and use them to construct a dynamic evolutionary model in social network. The strategic interactions between three groups: governors, street vendors and citizens are studied, where governors can adjust their regulation strength, vendors select their operating strategies, citizens choose the supervision strategies, and they can move and learn freely in networks. Research results show that it is difficult to achieve the optimal equilibrium of strict regulation by governors, high-quality production by street vendors, and active supervision by citizens in situations of complete information. This ultimately hinders the high-quality development of street vending. In situations with partial information, the behavior of all agents in regulation is more proactive, reflecting the promoting effect of asymmetric information in the group on the regulation of street vending. This work provides decision-making support for the municipal government.