IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2023i1p4-d1302949.html
   My bibliography  Save this article

A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments

Author

Listed:
  • József Dombi

    (Institute of Informatics, University of Szeged, 6720 Szeged, Hungary
    HUN-REN SZTE Research Group on Artificial Intelligence, 6720 Szeged, Hungary)

  • Jenő Fáró

    (Faculty of Economics, ELTE Eötvös Loránd University, 1088 Budapest, Hungary)

  • Tamás Jónás

    (Faculty of Economics, ELTE Eötvös Loránd University, 1088 Budapest, Hungary)

Abstract

This study presents a novel, fuzzy entropy-based approach to the measurement of consensus in group decision making. Here, the basic assumption is that the decision inputs are the ‘yes’ or ‘no’ votes of group members on a financial investment that has a particular expected rate of return. In this paper, using a class of fuzzy entropies, a novel consensus measure satisfying reasonable requirements is introduced for a case where the decision inputs are dichotomous variables. It is also shown here that some existing consensus measures are just special cases of the proposed fuzzy entropy-based consensus measure when the input variables are dichotomous. Next, the so-called group consensus map for financial investments is presented. It is demonstrated that this construction can be used to characterize the level of consensus among the members of a group concerning financial investments as a function of the expected rate of return. Moreover, it is described how a consensus map can be constructed from empirical data and how this map is connected with behavioral economics.

Suggested Citation

  • József Dombi & Jenő Fáró & Tamás Jónás, 2023. "A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments," Mathematics, MDPI, vol. 12(1), pages 1-18, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:4-:d:1302949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/1/4/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/1/4/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gong, Zaiwu & Guo, Weiwei & Herrera-Viedma, Enrique & Gong, Zejun & Wei, Guo, 2020. "Consistency and consensus modeling of linear uncertain preference relations," European Journal of Operational Research, Elsevier, vol. 283(1), pages 290-307.
    2. Eklund, Patrik & Rusinowska, Agnieszka & De Swart, Harrie, 2007. "Consensus reaching in committees," European Journal of Operational Research, Elsevier, vol. 178(1), pages 185-193, April.
    3. J. C. R. Alcantud & R. Andrés Calle & J. M. Cascón, 2015. "Pairwise Dichotomous Cohesiveness Measures," Group Decision and Negotiation, Springer, vol. 24(5), pages 833-854, September.
    4. Guo, Weiwei & Gong, Zaiwu & Zhang, Wei-Guo & Xu, Yanxin, 2023. "Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1279-1295.
    5. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).
    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. Dombi, József & Jónás, Tamás, 2024. "Consensus measures based on a fuzzy concept," European Journal of Operational Research, Elsevier, vol. 315(2), pages 642-653.
    2. Meng, Fan-Yong & Zhao, Deng-Yu & Gong, Zai-Wu & Chu, Jun-Fei & Pedrycz, Witold & Yuan, Zhe, 2024. "Consensus adjustment for multi-attribute group decision making based on cross-allocation," European Journal of Operational Research, Elsevier, vol. 318(1), pages 200-216.
    3. Guo, Weiwei & Gong, Zaiwu & Zhang, Wei-Guo & Xu, Yanxin, 2023. "Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1279-1295.
    4. González-Arteaga, T. & Alcantud, J.C.R. & de Andrés Calle, R., 2016. "A cardinal dissensus measure based on the Mahalanobis distance," European Journal of Operational Research, Elsevier, vol. 251(2), pages 575-585.
    5. Meng, Fan-Yong & Gong, Zai-Wu & Pedrycz, Witold & Chu, Jun-Fei, 2023. "Selfish-dilemma consensus analysis for group decision making in the perspective of cooperative game theory," European Journal of Operational Research, Elsevier, vol. 308(1), pages 290-305.
    6. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).
    7. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    8. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    9. Juan Carlos Leyva-López, 2024. "A consistency and consensus model for group decision support based on the outranking approach," Operational Research, Springer, vol. 24(2), pages 1-29, June.
    10. Jana Goers & Graham Horton, 2024. "On the Combinatorial Acceptability Entropy Consensus Metric for Multi-Criteria Group Decisions," Group Decision and Negotiation, Springer, vol. 33(5), pages 1247-1268, October.
    11. Xuyuan Zhang & Hailin Liang & Shaojian Qu, 2024. "Robust Consensus Modeling: Concerning Consensus Fairness and Efficiency with Uncertain Costs," Mathematics, MDPI, vol. 12(8), pages 1-31, April.
    12. Hong Huo & Ruinan Sun & Hao He & Zongwei Ren, 2024. "A Large-Scale Group Decision-Making Model Considering Expert Authority Degree and Relationship Evolution Under Social Network," Group Decision and Negotiation, Springer, vol. 33(4), pages 839-881, August.
    13. Powdthavee, Nattavudh & Riyanto, Yohanes E. & Knetsch, Jack L., 2018. "Lower-rated publications do lower academics’ judgments of publication lists: Evidence from a survey experiment of economists," Journal of Economic Psychology, Elsevier, vol. 66(C), pages 33-44.
    14. José Carlos R. Alcantud & María José M. Torrecillas, 2017. "Consensus measures for various informational bases. Three new proposals and two case studies from political science," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 285-306, January.
    15. 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).
    16. Sen Liu & Wei Yu & Ling Liu & Yanan Hu, 2019. "Variable weights theory and its application to multi-attribute group decision making with intuitionistic fuzzy numbers on determining decision maker’s weights," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    17. Irion, Kristina & Helberger, Natali, 2017. "Smart TV and the online media sector: User privacy in view of changing market realities," Telecommunications Policy, Elsevier, vol. 41(3), pages 170-184.
    18. Gong, Zaiwu & Xu, Xiaoxia & Zhang, Huanhuan & Aytun Ozturk, U. & Herrera-Viedma, Enrique & Xu, Chao, 2015. "The consensus models with interval preference opinions and their economic interpretation," Omega, Elsevier, vol. 55(C), pages 81-90.
    19. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    20. Zhibin Wu & Rong Yuan & Jiancheng Tu, 2021. "Group Decision Making with Transitive Preferences Under Ordinal and Cardinal Consistencies: An Optimization Approach," Group Decision and Negotiation, Springer, vol. 30(1), pages 221-250, February.

    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:gam:jmathe:v:12:y:2023:i:1:p:4-:d:1302949. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.