Preference similarity network structural equivalence clustering based consensus group decision making model
Preference similarity network structural equivalence clustering based consensus group decision making model
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DOI：
10.1016/j.asoc.2017.11.022
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发表时间：
2018-06-01
影响因子：
8.7
通讯作者：
Levesley, Jeremy
中科院分区：
文献类型：
--
作者：
Kamis, Nor Hanimah;Chiclana, Francisco;Levesley, Jeremy
社会网络分析（SNA）方法已被开发用于分析网络关系的社会结构和模式，尽管它们已被最少地探索和/或有目的地用于决策过程。在这项研究中，我们之间的差距，SNA和共识为基础的决策定义无向加权偏好网络的相似性，专家的偏好，使用“结构等价”的概念。结构上等价的专家采用具有完全连接函数的凝聚层次聚类算法表示，使得类内专家密度高，类间专家稀疏度高。我们得到的内部和外部凝聚力的基础上的集群共识，而群体共识是通过确定最高级别的共识，在最佳水平的聚类。因此，基于聚类的一致性度量方法有助于从整体上呈现专家偏好的同质性。在不充分的群体共识状态的情况下，我们构建了一个基于聚类的反馈机制过程，该过程包括三个主要阶段：（1）识别对共识贡献较小的专家;（2）识别网络中的领导者;（3）建议生成。我们利用SNA中的中心性概念来确定网络中最重要的人，他作为领导者在反馈过程中提供建议。证明了所提出的反馈机制的实施增加了共识，并且由于共识度量的有界条件，保证了收敛到充分的群体协议。中心性的概念也被应用在一个新的聚合算子的建设，即作为中心IOWA运营商，这是用来获得的集体偏好关系的共识解决方案的可行的替代方案，基于优势的概念，实现根据大多数的中心专家在网络中，这是在本文中表示的语言量词大多数。“为了验证的目的，现有的文献研究被用来进行比较分析，从中得出结论和解释。(C)2017作者由爱思唯尔公司出版
Social network analysis (SNA) methods have been developed to analyse social structures and patterns of network relationships, although they have been least explored and/or exploited purposely for decision making processes. In this study, we bridge a gap between SNA and consensus-based decision making by defining undirected weighted preference network from the similarity of expert preferences using the concept of 'structural equivalence'. Structurally equivalent experts are represented using the agglomerative hierarchical clustering algorithm with complete link function, thus intra-clusters' experts are high in density and inter-clusters' experts are rich in sparsity. We derive cluster consensus based on internal and external cohesions, while group consensus is obtained by identifying the highest level consensus at optimal level of clustering. Thus, the clustering based approach to consensus measure contributes to present homogeneity of experts preferences as a whole. In the event of insufficient group consensus state, we construct a feedback mechanism procedure based on clustering that consists of three main phases: (1) identification of experts that contribute less to consensus; (2) identification of a leader in the network; and (3) advice generation. We make use of the centrality concept in SNA as a way of determining the most important person in a network, who is presented as a leader to provide advices in the feedback process. It is proved that the implementation of the proposed feedback mechanism increases consensus and, because of the bounded condition of consensus measure, convergence to sufficient group agreement is guaranteed. The centrality concept is also applied in the construction of a new aggregation operator, namely as cent-IOWA operator, that is used to derive the collective preference relation from which the feasible alternative of consensus solution, based on the concept of dominance, is achieved according to a majority of the central experts in the network, which is represented in this paper by the linguistic quantifier most of.' For validation purposes, an existing literature study is used to perform a comparative analysis from which conclusions are drawn and explained. (C) 2017 The Authors. Published by Elsevier B.V.