Prospect theory driven-minimum cost ordinal consensus model for large-scale group decision-making

In large-scale group decision-making, resources and time invested in decision-making are typically constrained. Consequently, the focus of research in large-scale group decision-making lies in efficiently and cost-effectively obtaining decision results that are widely accepted by the majority of decision-makers. In this paper, we focus on addressing the large-scale group decision-making problem that considers the tolerance of decision-makers and propose a new minimum cost ordinal consensus model driven by prospect theory. Firstly, a new method for calculating comprehensive ordinal similarity among decision-makers is introduced. The hierarchical clustering algorithm is employed to cluster decision-makers to enhance decision-making efficiency, depending on the comprehensive ordinal similarity and trust relationships among them. Subsequently, a novel ordinal consensus measure method is presented based on the comprehensive ordinal similarity. By introducing prospect theory into the consensus reaching process, a minimum cost-maximum prospect value ordinal consensus model is developed. The model aims to minimize the consensus reaching costs while preserving the original opinions of decision-makers. Moreover, an objective method for determining the unit adjustment cost of decision-makers through social networks is provided in the model. Lastly, the feasibility of the proposed model is validated through an example. Comparative and simulation analyses highlight the effectiveness and advantages of the model.