A Maximum Consensus Improvement Method for Group Decision Making Under Social Network with Probabilistic Linguistic Information

Zhen Hua*, Huifeng Xue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Group decision-making (GDM) requires consensus building, because an outcome from a consensual decision is indispensable to implement a highly acceptable solution. This paper proposes a novel consensus reaching method for GDM with Probabilistic Linguistic Term Set (PLTS) under a social network environment. First, the preferences and trust evaluations of decision-makers (DMs) are collected using PLTS. Then, two types of centralities are utilized to obtain the significance of DMs, and these centralities are used to derive the group evaluation. Then, a consensus measure is employed to quantify the degree of agreement within the group. To promote further consensus, a novel feedback mechanism that combines the Identification and Direction Rule-based method with an optimization-based approach is developed to achieve maximum consensus improvement in each round of modification. Moreover, DM’s bounded rationality is factored into the GDM process for a more reliable result. Finally, illustrative examples and comparison analyses are conducted to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)437-465
Number of pages29
JournalNeural Processing Letters
Volume54
Issue number1
DOIs
Publication statusPublished - Feb 2022
Externally publishedYes

Keywords

  • Consensus reaching process (CRP)
  • Group decision-making (GDM)
  • Probabilistic linguistic term set (PLTS)

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