IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v30y2021i4d10.1007_s10726-021-09736-z.html
   My bibliography  Save this article

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

Author

Listed:
  • Feifei Jin

    (Anhui University)

  • Jinpei Liu

    (Anhui University)

  • Ligang Zhou

    (Anhui University)

  • Luis Martínez

    (University of Jaén)

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.

Suggested Citation

  • Feifei Jin & Jinpei Liu & Ligang Zhou & Luis Martínez, 2021. "Consensus-Based Linguistic Distribution Large-Scale Group Decision Making Using Statistical Inference and Regret Theory," Group Decision and Negotiation, Springer, vol. 30(4), pages 813-845, August.
  • Handle: RePEc:spr:grdene:v:30:y:2021:i:4:d:10.1007_s10726-021-09736-z
    DOI: 10.1007/s10726-021-09736-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-021-09736-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10726-021-09736-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pei Wang & Xuanhua Xu & Shuai Huang, 2019. "An Improved Consensus-Based Model for Large Group Decision Making Problems Considering Experts with Linguistic Weighted Information," Group Decision and Negotiation, Springer, vol. 28(3), pages 619-640, June.
    2. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    3. Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).
    4. Tang, Ming & Liao, Huchang & Xu, Jiuping & Streimikiene, Dalia & Zheng, Xiaosong, 2020. "Adaptive consensus reaching process with hybrid strategies for large-scale group decision making," European Journal of Operational Research, Elsevier, vol. 282(3), pages 957-971.
    5. Qifeng Wan & Xuanhua Xu & Xiaohong Chen & Jun Zhuang, 2020. "A Two-Stage Optimization Model for Large-Scale Group Decision-Making in Disaster Management: Minimizing Group Conflict and Maximizing Individual Satisfaction," Group Decision and Negotiation, Springer, vol. 29(5), pages 901-921, October.
    6. David E. Bell, 1982. "Regret in Decision Making under Uncertainty," Operations Research, INFORMS, vol. 30(5), pages 961-981, October.
    7. Bain, Kimberly & Hansen, Anne Steen, 2020. "Strengthening implementation success using large-scale consensus decision-making - A new approach to creating medical practice guidelines," Evaluation and Program Planning, Elsevier, vol. 79(C).
    8. Zhang, Hengjie & Dong, Yucheng & Chiclana, Francisco & Yu, Shui, 2019. "Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design," European Journal of Operational Research, Elsevier, vol. 275(2), pages 580-598.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feifei Jin & Chang Li & Jinpei Liu & Ligang Zhou, 2021. "Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis," Mathematics, MDPI, vol. 9(19), pages 1-19, October.
    2. Shaw, Lipika & Das, Soumen Kumar & Roy, Sankar Kumar, 2022. "Location-allocation problem for resource distribution under uncertainty in disaster relief operations," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    3. Tong, Huagang & Zhu, Jianjun, 2023. "A parallel approach with the strategy-proof mechanism for large-scale group decision making: An application in industrial internet," European Journal of Operational Research, Elsevier, vol. 311(1), pages 173-195.
    4. Lu Chen & Ayad Hendalianpour & Mohammad Reza Feylizadeh & Haiyan Xu, 2023. "Factors Affecting the Use of Blockchain Technology in Humanitarian Supply Chain: A Novel Fuzzy Large-Scale Group-DEMATEL," Group Decision and Negotiation, Springer, vol. 32(2), pages 359-394, April.
    5. Fangqing Wei & Yanan Fu & Feng Yang & Chun Sun & Sheng Ang, 2023. "Closest target setting with minimum improvement costs considering demand and resource mismatches," Operational Research, Springer, vol. 23(3), pages 1-29, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georgia Perakis & Guillaume Roels, 2008. "Regret in the Newsvendor Model with Partial Information," Operations Research, INFORMS, vol. 56(1), pages 188-203, February.
    2. Jinyi Hu, 2023. "Linguistic Multiple-Attribute Decision Making Based on Regret Theory and Minimax-DEA," Mathematics, MDPI, vol. 11(20), pages 1-14, October.
    3. Martín Egozcue & Xu Guo & Wing-Keung Wong, 2015. "Optimal output for the regret-averse competitive firm under price uncertainty," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 5(2), pages 279-295, December.
    4. Jhunjhunwala, Tanushree, 2021. "Searching to avoid regret: An experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 298-319.
    5. van Dijk, Wilco W. & van der Pligt, Joop, 1997. "The Impact of Probability and Magnitude of Outcome on Disappointment and Elation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(3), pages 277-284, March.
    6. Enrico G. De Giorgi & Thierry Post, 2011. "Loss Aversion with a State-Dependent Reference Point," Management Science, INFORMS, vol. 57(6), pages 1094-1110, June.
    7. van Dijk, W.W. & Zeelenberg, M. & van der Pligt, J., 1999. "Not having what you want versus having what you don't want : The impact of the type of negative outcome on the experience of disappointment and related emotions," Other publications TiSEM 5d1661b1-db82-4773-8ac4-5, Tilburg University, School of Economics and Management.
    8. Olivier Chanel & Graciela Chichilnisky, 2009. "The influence of fear in decisions: Experimental evidence," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 271-298, December.
    9. Soora Rasouli & Harry Timmermans, 2017. "Specification of regret-based models of choice behaviour: formal analyses and experimental design based evidence," Transportation, Springer, vol. 44(6), pages 1555-1576, November.
    10. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2013. "Salience and Consumer Choice," Journal of Political Economy, University of Chicago Press, vol. 121(5), pages 803-843.
    11. Raquel M. Gaspar & Paulo M. Silva, 2023. "Investors’ perspective on portfolio insurance," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 49-79, January.
    12. Yuval Rottenstreich & Alex Markle & Johannes Müller-Trede, 2023. "Risky Sure Things," Management Science, INFORMS, vol. 69(8), pages 4707-4720, August.
    13. Ulrich Schmidt & Stefan Traub, 2009. "An Experimental Investigation of the Disparity Between WTA and WTP for Lotteries," Theory and Decision, Springer, vol. 66(3), pages 229-262, March.
    14. Herweg, Fabian, 2013. "The expectation-based loss-averse newsvendor," Economics Letters, Elsevier, vol. 120(3), pages 429-432.
    15. Meimei Xia & Jian Chen & Juliang Zhang, 2015. "Multi-criteria decision making based on relative measures," Annals of Operations Research, Springer, vol. 229(1), pages 791-811, June.
    16. Gang Chen & Mark S. Daskin & Zuo‐Jun Max Shen & Stanislav Uryasev, 2006. "The α‐reliable mean‐excess regret model for stochastic facility location modeling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(7), pages 617-626, October.
    17. Broll, Udo & Welzel, Peter & Wong, Kit Pong, 2014. "Multinational firm, exchange rate risk and the impact of regret on trade," Dresden Discussion Paper Series in Economics 04/14, Technische Universität Dresden, Faculty of Business and Economics, Department of Economics.
    18. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    19. Peng Jing & Mengxuan Zhao & Meiling He & Long Chen, 2018. "Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    20. Fershtman, Chaim, 1996. "On the value of incumbency managerial reference points and loss aversion," Journal of Economic Psychology, Elsevier, vol. 17(2), pages 245-257, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:grdene:v:30:y:2021:i:4:d:10.1007_s10726-021-09736-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.