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.

中文翻译：

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考虑专家多重作用的具有社会信任信息的大规模群体决策的重叠图模型

社交网络分析是调查大型团体决策（LSGDM）中决策者之间关系的有效工具。现有的基于社交网络的LSGDM研究通常假定每个决策者具有单个角色或仅属于一个子组。很少考虑决策者具有多个角色或属于多个子组的假设。为此，本研究提出了一种重叠图模型（OGM），决策者可以以多种角色参与决策过程，以解决具有社会信任信息的LSGDM问题。在OGM中，决策者首先分为两种类型：多角色决策者和单角色决策者。由于决策者无法评估LSGDM问题中的所有其他问题，因此，然后，我们研究如何基于Agent机制构建完整的社会信任网络。为了减少决策者之间的差异，提出了一个两阶段的共识达成过程：第一阶段是针对子组中的单角色决策者，而第二阶段是针对Agent和多角色决策者。最后，提供了一个有关为COVID-19中的重症患者选择治疗计划的说明性示例，以测试所提出模型的适用性和合理性。