With the rapid increase of experts, groups or organizations involved in decision making, the problem of large-scale group decision making (LSGDM) has attracted increasing attention in the whole research field. Behavioral management and heterogeneous preference representation structures are two fundamental aspects of LSGDM problems. However, psychological functioning has been less considered in existing consensus models to deal with the different behavioral styles of decision-makers. Therefore, this study proposes a novel consensus reaching framework to detect and manage multiple styles of behavior in LSGDM based on heterogeneous preferences with self-confidence. Specifically, an optimization-based selection process is introduced to obtain the individual and collective preference vectors. Next, a self-confidence driven consensus approach is proposed, which includes consensus measure, clustering, detection of multiple styles of behavior, and hybrid feedback adjustment mechanism. According to the consensus level and the self-confidence level, the proposed detection of multiple styles of behavior method identifies four different behavioral subgroup types: collaborating, accommodating, competing, and avoiding. The hybrid feedback adjustment mechanism generates different feedback adjustment opinions for the four identified behavioral type subgroups. The effectiveness and characteristics of the proposed consensus approach is demonstrated with an emergency management case study and the reporting of comprehensive simulation experiments.

中文翻译：

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在大规模群体决策中自信地管理异质偏好和多种共识行为

随着参与决策的专家、团体或组织的迅速增加，大规模群体决策（LSGDM）问题越来越引起整个研究领域的关注。行为管理和异构偏好表示结构是 LSGDM 问题的两个基本方面。然而，在现有的共识模型中，很少考虑心理功能来处理决策者的不同行为风格。因此，本研究提出了一种新颖的共识框架，用于基于异质偏好和自信来检测和管理 LSGDM 中的多种行为风格。具体来说，引入基于优化的选择过程来获得个人和集体偏好向量。接下来，提出了一种自信驱动的共识方法，包括共识度量、聚类、多种行为风格检测和混合反馈调整机制。根据共识水平和自信心水平，所提出的多种行为风格检测方法识别了四种不同的行为子组类型：合作、适应、竞争和回避。混合反馈调整机制针对四个已识别的行为类型子组生成不同的反馈调整意见。通过应急管理案例研究和综合模拟实验报告证明了所提出的共识方法的有效性和特点。