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Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making

Author

Listed:
  • Cong-Cong Li

    (Southwest Jiaotong University)

  • Yuan Gao

    (Sichuan University)

  • Yucheng Dong

    (Sichuan University)

Abstract

Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal.

Suggested Citation

  • Cong-Cong Li & Yuan Gao & Yucheng Dong, 2021. "Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making," Group Decision and Negotiation, Springer, vol. 30(1), pages 97-118, February.
    • Handle: RePEc:spr:grdene:v:30:y:2021:i:1:d:10.1007_s10726-020-09708-9
    DOI: 10.1007/s10726-020-09708-9
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    References listed on IDEAS

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    1. Wen-Tao Guo & Van-Nam Huynh & Yoshiteru Nakamori, 2015. "An Interval Linguistic Distribution Model for Multiple Attribute Decision Making Problems with Incomplete Linguistic Information," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 6(4), pages 16-34, October.
    2. Feifei Jin & Zhiwei Ni & Reza Langari & Huayou Chen, 2020. "Consistency Improvement-Driven Decision-Making Methods with Probabilistic Multiplicative Preference Relations," Group Decision and Negotiation, Springer, vol. 29(2), pages 371-397, April.
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    Citations

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    Cited by:

    1. Wenqi Liu & Hengjie Zhang & Haiming Liang & Cong-cong Li & Yucheng Dong, 2022. "Managing Consistency and Consensus Issues in Group Decision-Making with Self-Confident Additive Preference Relations and Without Feedback: A Nonlinear Optimization Method," Group Decision and Negotiation, Springer, vol. 31(1), pages 213-240, February.
    2. Jian Wu & Jun Chen & Wei Liu & Yujia Liu & Changyong Liang & Mingshuo Cao, 2022. "A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    3. Hengjie Zhang & Wenfeng Zhu & Xin Chen & Yuzhu Wu & Haiming Liang & Cong-Cong Li & Yucheng Dong, 2024. "Managing flexible linguistic expression and ordinal classification-based consensus in large-scale multi-attribute group decision making," Annals of Operations Research, Springer, vol. 341(1), pages 95-148, October.
    4. Cong-Cong Li & Yuan Gao & Yucheng Dong, 2023. "Managing Missing Preference Values Through Consistency and Consensus in Distributed Linguistic Preference Relations: A Two-stage Method Based on Personalized Individual Semantics," Group Decision and Negotiation, Springer, vol. 32(1), pages 125-146, February.
    5. Xiao Tan & Jianjun Zhu & Tong Wu, 2022. "Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information," Group Decision and Negotiation, Springer, vol. 31(2), pages 491-528, April.
    6. Li, Cong-Cong & Dong, Yucheng & Liang, Haiming & Pedrycz, Witold & Herrera, Francisco, 2022. "Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making," Omega, Elsevier, vol. 111(C).

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