Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory

Feifei Jin, Jinpei Liu, Ligang Zhou and Luis Martínez ()
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Feifei Jin: Anhui University
Jinpei Liu: Anhui University
Ligang Zhou: Anhui University
Luis Martínez: University of Jaén

Group Decision and Negotiation, 2021, vol. 30, issue 4, No 4, 813-845

Abstract: Abstract Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs’ regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs’ regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

Keywords: Linguistic distribution information; Large-scale group decision making; Consensus reaching process; Statistical inference principle; Regret theory (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10726-021-09736-z

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