Parallel evolution and response decision method for public sentiment based on system dynamics

Governments face difficulties in policy making in many areas such as health, food safety, and large-scale projects where public perceptions can be misplaced. For example, the adoption of the MMR vaccine has been opposed due to the publicity indicating an erroneous link between the vaccine and autism. This research proposes the “Parallel Evolution and Response Decision Framework for Public Sentiments” as a real-time decision-making method to simulate and control the public sentiment evolution mechanisms. This framework is based on the theories of Parallel Control and Management (PCM) and System Dynamics (SD) and includes four iterative steps: namely, SD modelling, simulating, optimizing, and controlling. A concrete case of an anti-nuclear mass incident that sparked public sentiment in China is introduced as a study sample to test the effectiveness of the proposed method. In addition, the results indicate the effects by adjusting the key control variables of response strategies. These variables include response time, response capacity, and transparency of the government regarding public sentiment. Furthermore, the advantages and disadvantages of the proposed method will be analyzed to determine how it can be used by policy makers in predicting public opinion and offering effective response strategies.