Information entropy risk measure applied to large group decision-making method

Based on specially significant emergencies, as Tianjin Port, 812 large fire and explosion accidents and other practical cases, we have a scientific modeling for decision-making process and identify the risk that complex large group decision-makers' preference differences lead to in the process. We propose a new risk measure combined with information entropy theory to measure risk in clustering. Risk due to the complexity of appetite caused in the decision-making process is quantificationally computed, and the size of the risk helps determine the results of the final preference information assembly, whereby the program order. Finally, numerical example illustrates the effectiveness of the method.