Abstract Consensus reaching processes (CRPs) have been required to assure the consensus in large scale group decision making (LSGDM). Opinion reliability detection has been demanded to ensure the trustworthiness of the original information and different information modeling approaches have facilitated it in which two dimensional linguistic (TDL) information has an outstanding place. The reliability degree of original opinions elicited by TDL expressions is often given in advance as subjective evaluation, and after adjustment during CRP, the reliability of the adjusted opinions is often neglected especially for automatic CRP, which may lead to unreliable decisions. In real decision making, considering the interest of decision makers (DMs) themselves, the self-assessment of the DMs on the reliability of the given opinions could be easily manipulated by DMs. To reduce the subjectivity of the decision making, we propose a method to obtain objectively the reliability of the adjusted opinions through a two-stage minimum cost consensus model based on 2-tuple linguistic additive preference relations. Firstly, a support degree (SD)-based clustering method will be developed for classifying DMs into several subgroups to make more manageable the large number of DMs. Subsequently, a two-stage minimum adjustment consensus model will be presented to improve the consensus level (CL) gradually. Eventually, the adjusted opinions will be presented as two-dimension 2-tuple linguistic (TD2L) information. A comparative performance analysis of this CRP based LSGDM approach will be provided to show its effectiveness.

中文翻译：

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基于二维二元语言信息建模可靠性的大规模决策两阶段最小调整共识模型

摘要 共识达成过程 (CRP) 需要确保大规模群体决策 (LSGDM) 中的共识。意见可靠性检测被要求确保原始信息的可信度，不同的信息建模方法促进了它的发展，其中二维语言 (TDL) 信息具有突出的地位。TDL表达式引出的原始意见的可信度往往作为主观评价预先给出，在CRP进行调整后，尤其是自动CRP，往往忽略了调整后意见的可靠性，可能导致决策不可靠。在实际决策中，考虑决策者 (DM) 自身的利益，DM 对给定意见的可靠性的自我评估很容易被 DM 操纵。为了减少决策的主观性，我们提出了一种方法，通过基于二元语言加性偏好关系的两阶段最小成本共识模型客观地获得调整意见的可靠性。首先，将开发基于支持度 (SD) 的聚类方法，用于将 DM 分类为多个子组，以使大量 DM 更易于管理。随后，将提出一个两阶段最小调整共识模型，以逐步提高共识水平（CL）。最终，调整后的意见将呈现为二维二元语言（TD2L）信息。