The OWA-based consensus operator under linguistic representation models using position indexes

By Yucheng Dong, Yinfeng Xu, Hongyi Li, Bo Feng. European Journal of Operational Research, 2010

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1cbfb5f799366b9c1b4b8160262028155ccb7a78

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Citations in the corpus

Consensus reaching in social network group decision making: Research paradigms and challenges (2018, background): “5 (b) Optimization-based consensus rule [46-50], which are utilized to minimize the number of adjusted decision makers, alternatives, preference values, and distance between the original and adjusted preferences.”
A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust (2018, background): “al [14, 15], which produces the reaccommodated values to the satisfied consensus level.”
Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors (2016, background): “Based on a “soft” consensus level, different types of CRPs have been proposed: (i) CRPs under different preference representation formats [10,13,15,17,28,35, 55]; (ii) CRPs with minimum adjustments or cost [5,6,12,16,22,23,66, 68,69]; (iii) CRPs based on consistency and consensus measures [18, 20,25”
Consensus models for AHP group decision making under row geometric mean prioritization method (2010, background): “The consensus model is an important aspect in group decision making [5,6,9,10,13,16,27,29,30,44].”
Trust based consensus model for social network in an incomplete linguistic information context (2015, methodology): “The interactive consensus model is regarded as an effective method to reach satisfactory consensus level because it implements a feedback mechanism to advice experts on how to change their preferences [5] [19] [23] [24] [53] [56] [63].”
A statistical comparative study of different similarity measures of consensus in group decision making (2013, background): “com), which has led to the definition and use of a new concept of consensus degree referred to as ‘soft’ consensus degree [16, 18, 26, 30, 31, 33, 40, 50].”