A social network large-scale group decision-making method considering overconfidence behavior under incomplete hesitant fuzzy preference environment

With the proliferation of social media, social network large-scale group decision making (SNLSGDM), involving a vast number of experts, has emerged as a critical issue requiring urgent attention. In such contexts, the negative effects of overconfidence behavior must be carefully addressed. To tackle this challenge, this paper proposes an SNLSGDM method considering overconfidence behavior management under incomplete hesitant fuzzy preference environment. First, trust propagation and aggregation operators for hesitant fuzzy information are developed to obtain indirect trust values. Then, a two-stage estimation method is introduced to handle missing values in incomplete hesitant fuzzy preference relationship with self-confidence (HFPR-SC) is proposed. In the first stage, original estimates are determined based on reference degrees that integrate trust scores and similarity measures. In the second stage, a minimum distance adjustment model is constructed to update these original estimates. Next, an overconfidence behavior management mechanism is proposed for subgroups, based on the worst consistency index (WCI) and self-confidence levels. Meanwhile, a WCI achievement algorithm is developed using a minimum adjustment consistency improvement model to enhance subgroup consistency. Finally, the effectiveness and applicability of the proposed method are demonstrated through a numerical example.