Abstract
•We discuss the consensus method which considers overconfidence behaviors in LSGDM.•A clustering method considering expert's self-confidence is presented.•A consensus measure considering expert's self-confidence is proposed.•The detection and management of overconfidence behaviors in LSGDM is analyzed.
With the development of intelligent decision-making, one kind of decision modes involves a large number of decision-makers (DMs), which is called large scale group decision making (LSGDM). In LSGDM, overconfidence is one of the common behaviors because of many DMs’ participation and the bounded rationality of human decision. Overconfidence usually has a negative impact on LSGDM and can even lead to failure in the final decision(s). To achieve consensus is very important for LSGDM. Different consensus models of LSGDM have been proposed, while the DMs’ overconfidence behaviors in the consensus have not been concerned. Hence, the purpose of this paper is to propose a consensus model which considers overconfidence behaviors, and the paper mainly focuses on LSGDM based on fuzzy preference relations with self-confidence (FPRs-SC). In the proposed model, a DM clustering method, which combines fuzzy preference values similarity and self-confidence similarity, is used to classify the DMs with similar opinions into a subgroup. A group consensus index which considers both the fuzzy preference values and self-confidence is presented to measure the consensus level among DMs. An overconfidence measurement is given to detect the DMs’ overconfidence behaviors in the consensus. Subsequently, the detailed overconfidence behavior analysis is presented involving two aspects: fuzzy preference values consensus and self-confidence consensus. A dynamic weight punishment mechanism is implemented for overconfident DMs to improve the consensus efficiently. The effectiveness and advantages of the presented consensus model are validated by a numerical example and comparative analysis.