Analysis of collaborative urban public crisis governance in complex system: A multi-agent stochastic evolutionary game approach

Abstract

As the world economy is leaping forward, countries have been exposed to the constant threat of public crises. Accelerated urbanization has currently led to a proliferation of urban public crisis events. Conventional governance methods become ineffective under the effect of the highly uncertain and dynamic nature of urban public crisis events. Accordingly, this study primarily aims to facilitate the construction of a collaborative multi-agent alliance in urban public crisis governance. Thus, evolutionary game theory is combined with system dynamics, Gaussian white noise stochastic disturbance terms are introduced to model the complex characteristics of urban public crisis events, and evolutionary game models and stochastic evolutionary game models are built for three types of groups (i.e., local governments, enterprises, and the public) to study the evolutionary process of group strategy choice behavior with or without central government supervision. The result of this study suggests that under the effect of the complexity factor, the central government's dynamic reward and punishment mechanism plays a role of in reaching a facilitative and stable alliance for collaborative urban public crisis governance. Furthermore, contributions, unresolved issues and vital challenges are identified. In this study, a solid basis is laid for future research and widespread use of collaborative urban public crisis governance, which provides a reference for stakeholders in the field of urban public crisis governance.