CHALLENGES FOR IMPROVING CONSENSUS REACHING PROCESS IN COLLECTIVE DECISIONS

2007 ◽  
Vol 03 (02) ◽  
pp. 203-217 ◽  
Author(s):  
L. MARTÍNEZ ◽  
J. MONTERO

The majority rule is frequently presented as a cornerstone of any democratic society, guiding many group decision-making processes where final decision requires the agreement of more than half the people involved. But sometimes, some key decisions require a higher level of agreement. In such cases, an added value would be to reach some consensus about the decision-making problem. Decision making under consensus drives to decisions which are better accepted and appreciated. But it also implies a greater complexity and time consuming process to reach a final decision, and it may even lead to a deadlock or unsuccessful results, whenever the searched agreement is not achieved. Meanwhile, these problems arise because the requirements to achieve the consensus are too strong, and different processes have softened their requirements. In particular, soft consensus is one of the most widespread consensus reaching processes that uses fuzzy logic to soften the consensus requirements. However, several problems still persist despite the softening of the requirements. In this paper, we are going to make a brief revision of the different concepts about consensus and about different consensus reaching processes, both in the crisp and fuzzy environment. We shall then analyze how to overcome their lacks, indicating the challenges facing these processes in order to obtain successful results in those group decision problems in which they are required to make a decision under consensus.

Author(s):  
Bo Peng ◽  
Chunming Ye ◽  
Shouzhen Zeng

The ordered weighted distance (OWD) measure developed by Xu and Chen having been proved suitable to deal with the situation where the input arguments are represented in exact numerical values. In this paper, we develop some new geometric distance measures with intuitionistic fuzzy information, which are the generalization of some widely used distance measures, including the intuitionistic fuzzy weighted geometric distance (IFWGD) measure, the intuitionistic fuzzy ordered weighted geometric distance (IFOWGD) measure, the intuitionistic fuzzy ordered weighted geometric Hamming distance (IFOWGHD) measure, the intuitionistic fuzzy ordered weighted geometric Euclidean distance (IFOWGED) measure, the intuitionistic fuzzy hybrid weighted geometric distance (IFHWGD) measure. These developed weighted geometric distance measures are very suitable to deal with the situation where the input arguments are represented in intuitionistic fuzzy values. And then, we present a consensus reaching process based on the developed distance measures with intuitionistic fuzzy preference information for group decision making. Finally, we apply the developed approach with a numerical example to group decision making under intuitionistic fuzzy environment.


2020 ◽  
Vol 282 (3) ◽  
pp. 957-971 ◽  
Author(s):  
Ming Tang ◽  
Huchang Liao ◽  
Jiuping Xu ◽  
Dalia Streimikiene ◽  
Xiaosong Zheng

Author(s):  
Xiangrui Chao ◽  
Yucheng Dong ◽  
Gang Kou ◽  
Yi Peng

AbstractIn the past 10 years, a large number of consensus-reaching approaches for group decision making (GDM) have been proposed. While these methods either focus on the cost of the consensus reaching or the convergency of the consensus process, the consensus efficiency has long been ignored. Meanwhile, the measurements of consensus threshold are often determined by some subjective and intuitive judgements, such as management experience and estimations for the degree of satisfaction, which lack a theoretical foundation. In management applications, how to measure consensus and how to evaluate a consensus reaching method are also ambiguous. To tackle these questions, we introduce efficiency measures into the consensus reaching process of GDM and achieve a comprehensive evaluation of current consensus methods through an efficiency analysis of consensus costs and consensus improvement. From the perspective of efficiency, we propose a benchmark in consensus reaching by data envelopment analysis without explicit input benchmark models, and then present an objective method for consensus threshold determination in GDM. Finally, we use numerical examples to illustrate the usability of our method.


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