A hierarchical consensus reaching process for group decision making with noncooperative behaviors

•We propose a hierarchical consensus reaching process model.•We introduce a weight determination method for subgroups.•We give a noncooperative behavior detection and management mechanism.•We provide three preference search strategies.•We validate the method with new metro route building in Chengdu....

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Bibliographic Details
Published in:European journal of operational research Vol. 293; no. 2; pp. 632 - 642
Main Authors: Tang, Ming, Liao, Huchang, Mi, Xiaomei, Lev, Benjamin, Pedrycz, Witold
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2021
Subjects:
Decision support systems
Group decision making
Hierarchical consensus
Minimum spanning tree
Noncooperative behaviors
Decision support systems
Group decision making
Noncooperative behaviors
Hierarchical consensus
Minimum spanning tree
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:•We propose a hierarchical consensus reaching process model.•We introduce a weight determination method for subgroups.•We give a noncooperative behavior detection and management mechanism.•We provide three preference search strategies.•We validate the method with new metro route building in Chengdu. With the development of technological and societal paradigms, we witness a trend where a large number of experts participate in decision-making processes, and large-scale group decision making has become a much researched topic. A large-scale group decision-making problem usually involves many experts with various backgrounds and experiences. In these cases, an effective consensus reaching process is essential to guarantee the support of all experts, especially in large-scale group decision-making settings. This study proposes a hierarchical consensus model that allows the number of adjusted opinions to vary depending on the specific level of consensus in each iterative round. Furthermore, this study also introduces a method to detect and manage noncooperative behaviors by means of the hierarchical consensus model. The minimum spanning tree clustering algorithm is used to classify experts. A weight determination method combining the size of the subgroup and the sum of squared errors is developed for subgroups. Finally, an illustrative example is provided to demonstrate the practicality of the proposed model.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2020.12.028