IDEAS home Printed from https://ideas.repec.org/a/spr/grdene/v31y2022i2d10.1007_s10726-022-09775-0.html
   My bibliography  Save this article

Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information

Author

Listed:
  • Xiao Tan

    (Nanjing University of Aeronautics and Astronautics)

  • Jianjun Zhu

    (Nanjing University of Aeronautics and Astronautics)

  • Tong Wu

    (Nanjing University of Aeronautics and Astronautics)

Abstract

We present a consensus improvement mechanism based on prospect theory and quantum probability theory (QPT) that enables the manifestation of irrational and uncertain behaviors of decision makers (DMs) in linguistic distribution group decision making. In this framework, the DMs pursue the possibility of working with different partial agreements on prospect values. Considering that the reference information should be comprehensive and accurate as it guides information modification and affects consensus efficiency, objective and subjective information is integrated to obtain the information. Several studies have verified that the interference effect will occur when the brain beliefs flow towards the different decision classification paths. To address this problem, QPT is introduced into the information integration and the optimized value of the interference term can be acquired by the designed multi-objective programming model based on the maximum individual utility. Finally, as the reference point changes during the preference adjustment process, a dynamic reference point-oriented consensus model is constructed to obtain the optimized modification. A case study is performed on the emergency plan for the selection of designated hospitals, and comparative analyses are performed to demonstrate the feasibility and advantages of the proposed model. Several important insights are offered to simulate the most likely possibility of consciousness flowing into different decision classifications for DMs and moderators.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:grdene:v:31:y:2022:i:2:d:10.1007_s10726-022-09775-0
    DOI: 10.1007/s10726-022-09775-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10726-022-09775-0
    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/s10726-022-09775-0?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. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Aurélien Baillon & Han Bleichrodt & Vitalie Spinu, 2020. "Searching for the Reference Point," Management Science, INFORMS, vol. 66(1), pages 93-112, January.
    3. Álvaro Labella & Rosa M. Rodríguez & Ahmad A. Alzahrani & Luis Martínez, 2020. "A Consensus Model for Extended Comparative Linguistic Expressions with Symbolic Translation," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    4. Jérôme Busemeyer & Ariane Lambert-Mogiliansky & Zheng Wang, 2009. "Empirical Comparison of Markov and Quantum models of decision-making," PSE-Ecole d'économie de Paris (Postprint) halshs-00754332, HAL.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Gong, Zaiwu & Zhang, Huanhuan & Forrest, Jeffrey & Li, Lianshui & Xu, Xiaoxia, 2015. "Two consensus models based on the minimum cost and maximum return regarding either all individuals or one individual," European Journal of Operational Research, Elsevier, vol. 240(1), pages 183-192.
    7. 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.
    8. Liu, Bingsheng & Zhou, Qi & Ding, Ru-Xi & Palomares, Iván & Herrera, Francisco, 2019. "Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination," European Journal of Operational Research, Elsevier, vol. 275(2), pages 737-754.
    9. Aurélien Baillon & Han Bleichrodt & Vitalie Spinu, 2020. "Searching for the Reference Point," Management Science, INFORMS, vol. 66(1), pages 93-112, January.
    10. Jérôme Busemeyer & Ariane Lambert-Mogiliansky & Zheng Wang, 2009. "Empirical Comparison of Markov and Quantum models of decision-making," Post-Print halshs-00754332, HAL.
    11. Bowen Zhang & Yucheng Dong & Enrique Herrera-Viedma, 2019. "Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching," Group Decision and Negotiation, Springer, vol. 28(3), pages 585-617, June.
    12. Weijun Xu & Xin Chen & Yucheng Dong & Francisco Chiclana, 2021. "Impact of Decision Rules and Non-cooperative Behaviors on Minimum Consensus Cost in Group Decision Making," Group Decision and Negotiation, Springer, vol. 30(6), pages 1239-1260, December.
    13. Yong Liu & Ting Zhou & Jeffrey Yi-Lin Forrest, 2020. "A Multivariate Minimum Cost Consensus Model for Negotiations of Holdout Demolition," Group Decision and Negotiation, Springer, vol. 29(5), pages 871-899, October.
    Full references (including those not matched with items on IDEAS)

    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. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    2. Wenhui Zhou & Dongmei Wang & Weixiang Huang & Pengfei Guo, 2021. "To Pool or Not to Pool? The Effect of Loss Aversion on Queue Configurations," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4258-4272, November.
    3. Bleichrodt, Han & L’Haridon, Olivier, 2023. "Prospect theory’s loss aversion is robust to stake size," Judgment and Decision Making, Cambridge University Press, vol. 18, pages 1-1, January.
    4. Zhihua Li & Songfa Zhong, 2023. "Reference Dependence in Intertemporal Preference," Management Science, INFORMS, vol. 69(1), pages 475-490, January.
    5. Kontosakos, Vasileios E. & Hwang, Soosung & Kallinterakis, Vasileios & Pantelous, Athanasios A., 2024. "Long-term dynamic asset allocation under asymmetric risk preferences," European Journal of Operational Research, Elsevier, vol. 312(2), pages 765-782.
    6. Xiangrui Chao & Yucheng Dong & Gang Kou & Yi Peng, 2022. "How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight," Annals of Operations Research, Springer, vol. 316(1), pages 143-177, September.
    7. Tao, Zhenmin & Moncada, Jorge Andres & Delarue, Erik, 2023. "Exploring the impact of boundedly rational power plant investment decision-making by applying prospect theory," Utilities Policy, Elsevier, vol. 82(C).
    8. Konstantinos Georgalos & Ivan Paya & David Peel, 2023. "Higher order risk attitudes: new model insights and heterogeneity of preferences," Experimental Economics, Springer;Economic Science Association, vol. 26(1), pages 145-192, March.
    9. Ulrich Schmidt & Horst Zank, 2022. "Chance theory: A separation of riskless and risky utility," Journal of Risk and Uncertainty, Springer, vol. 65(1), pages 1-32, August.
    10. Heiko Karle & Dirk Engelmann & Martin Peitz, 2022. "Student performance and loss aversion," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(2), pages 420-456, April.
    11. Konstantinos Georgalos & Nathan Nabil, 2023. "Heuristics Unveiled," Working Papers 400814162, Lancaster University Management School, Economics Department.
    12. Buchanan, Joy A., 2020. "My reference point, not yours," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 297-311.
    13. Brice Corgnet & Roberto Hernán González, 2023. "You Will not Regret it: On the Practice of Randomized Incentives," Working Papers 2314, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    14. Yuval Rottenstreich & Alex Markle & Johannes Müller-Trede, 2023. "Risky Sure Things," Management Science, INFORMS, vol. 69(8), pages 4707-4720, August.
    15. Miles S. Kimball & Collin B. Raymond & Jiannan Zhou & Junya Zhou & Fumio Ohtake & Yoshiro Tsutsui, 2024. "Happiness Dynamics, Reference Dependence, and Motivated Beliefs in U.S. Presidential Elections," NBER Working Papers 32078, National Bureau of Economic Research, Inc.
    16. Stephan Billinger & Kannan Srikanth & Nils Stieglitz & Terry R. Schumacher, 2021. "Exploration and exploitation in complex search tasks: How feedback influences whether and where human agents search," Strategic Management Journal, Wiley Blackwell, vol. 42(2), pages 361-385, February.
    17. Pascal Flurin Meier & Raphael Flepp & Egon Franck, 2022. "Are Expectations Misled by Chance? Quasi-Experimental Evidence from Financial Analysts," Working Papers 396, University of Zurich, Department of Business Administration (IBW).
    18. Konstantinos Georgalos & Nathan Nabil, 2023. "Testing Models of Complexity Aversion," Working Papers 400814269, Lancaster University Management School, Economics Department.
    19. Alam, Jessica & Georgalos, Konstantinos & Rolls, Harrison, 2022. "Risk preferences, gender effects and Bayesian econometrics," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 168-183.
    20. Dennis W. Jansen & Liqun Liu, 2022. "Portfolio choice in the model of expected utility with a safety-first component," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 187-207, June.

    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:31:y:2022:i:2:d:10.1007_s10726-022-09775-0. 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.