An overlap graph model for large‐scale group decision making with social trust information considering the multiple roles of experts. Issue 3 (23rd December 2020)

• Record Type: Journal Article
• Authors: Liao, Huchang; Tan, Runzhi; Tang, Ming
• Is Part Of: Expert systems. Volume 38:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-23
• Subjects: consensus reaching process -- COVID‐19 -- group decision making -- overlap graph model -- social network analysis; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12659
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Social network analysis is an efficient tool to investigate the relationships of decision‐makers in large‐scale group decision making (LSGDM). Existing social network‐based LSGDM studies generally assumed that each decision‐maker has a single role or belongs to only one subgroup. The assumption that a decision‐maker has multiple roles or belongs to multiple subgroups is rarely taken into consideration. In this regard, this study proposes an overlap graph model (OGM) in which decision‐makers can participate in the decision‐making process in multiple roles to solve LSGDM problems with social trust information. In the OGM, decision‐makers are firstly divided into two types: multiple‐role decision‐makers and single‐role decision‐makers. Since it is unpractical for a decision‐maker to evaluate all others in a LSGDM problem, we then investigate how to construct a complete social trust network based on an Agent mechanism. A two‐stage consensus reaching process is proposed to reduce the discrepancies among decision‐makers: The first stage is for single‐role decision‐makers within a subgroup while the second stage is for Agents and multiple‐role decision‐makers. Finally, an illustrative example regarding selecting treatment plans for critical patients in COVID‐19 is provided to test the applicability and rationality of the proposed model.