A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making

By Ivan Palomares, Luis Martínez-López, Francisco Herrera. IEEE transactions on fuzzy systems, 2014

493 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0a2015fed470e75ff233941a1c3203f4c6575f64

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Citations in the corpus

Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making (2017, methodology): “In order to keep the maximum information elicited by decision makers in a group in the initial stages of the decision process, this paper proposes the use of linguistic distribution assessments [33], [34] to represent group’s linguistic information for linguistic LGDM problems.”
Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions (2018, methodology): “There are a lot of clustering algorithms for implementing the preference clustering (see [43], [45], [60], [61], and [67]).”
Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors (2016, background): “[40], thewkh is computed aswkh=min(wk,wh).”
Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions (2015, background): “In the decision-making process, DMswho are reluctant to adjust their own opinions are referred to as non-cooperative members [28].”
Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance (2021, background): “LSDM research provides appropriate insights to determine the best solution to solve practical problems and overcome the noncooperative behaviour of key decision-makers (Palomares, Martinez, & Herrera, 2013; ).”