Consensus reaching for large-scale group decision making: A gain-loss analysis perspective

Large-scale group decision making (LSGDM) is increasingly prevalent in practical scenarios, with consensus reaching being a crucial aspect that concerns the effectiveness and efficiency of the decision-making process. This paper proposes an innovative consensus reaching method for LSGDM, adopting a novel perspective that focuses on gains and losses. First, the gains and losses of experts during the clustering process are computed using recognition increment and representativeness decrement, which are combined to determine their utility. By ensuring that experts receive a high level of utility, a clustering method is proposed to categorize a large number of experts into distinct clusters. Then, an optimization model is presented to determine the weights of clusters, with the objective of maximizing the utility of clusters. Next, a feedback mechanism is developed, grounded in the concept of gains and losses, to enhance consensus levels. During the feedback adjustment process, the gains and losses of clusters are assessed based on changes in consensus levels and the adjustment costs incurred when clusters modify their information. These gains and losses are combined to determine the utility of clusters, serving as the foundation for designing the feedback mechanism. Finally, an application example of blockchain platform selection is presented, along with comparative analyses to validate the proposed method.