An Adaptive Consensus Reaching Process Dealing with Comparative Linguistic Expressions in Large-scale Group Decision Making

Nowadays, society often faces decision problems under uncertainty that can hardly be managed by a single expert or a few of them because of their complexity. Under these conditions, large-scale group decision making (LS-GDM) problems are becoming more and more common. The decisions made on these types of problems might affect directly lots of people in which the consensual decisions are better accepted and consensus reaching processes (CRPs) support reaching such consensus. LS-GDM under uncertainty has been solved by using linguistic information but considering only single linguistic terms to represent experts’ opinions. Especially in large-scale, this is an important drawback, since the complexity of the problems causes the apparition of experts’ hesitancy, which cannot be modeled by single linguistic terms. Concretely, comparative linguistic expression (CLEs) based on hesitant fuzzy linguistic term sets have provided remarkable results in hesitancy modeling. Therefore, this contribution aims at defining a novel adaptive CRP for LS-GDM in which experts’ preferences are modeled by CLEs.

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