Large-scale multiple criteria group decision-making with information emendation based on unsupervised opinion evolutions

Large-scale multiple criteria group decision-making (MCGDM) is prevalent in diverse decision-making scenarios, involving numerous decision makers (DMs), the set of alternatives and criteria, and continuous temporal cycles. Opinions from DMs dynamically evolve through iterative interaction, leading to dynamic opinion evolutions. However, traditional MCGDM methodology usually establish the opinion formation on a static time point throughout information aggregation, which will lead to information distortion. This study develops a novel large-scale MCGDM method with information emendation based on an unsupervised opinion dynamics (UOD) model, combining with the intuitionistic fuzzy set (IFS) and the technique for order preference by similarity to an ideal solution (TOPSIS). The IFS is utilized to quantify opinions since it can effectively achieve a tradeoff between information retention and convenience of evaluation. Simultaneously, in the proposed UOD model, the weight updating mechanism is further considered to improve the interaction adequacy, and the unsupervised mechanism for interaction threshold helps to decrease the influences of subjectivity from DMs. Moreover, numerical simulations validate the UOD model’s feasibility. Finally, a school site selection problem is carried out to elaborate the effectiveness of the proposed method. This study will provide a methodological reference for solving large-scale MCGDM problems, facilitating rapid convergence of opinions within large-scale groups, and enrich the research on opinion dynamics in the field of decision-making.