IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i3p385-d1576419.html
   My bibliography  Save this article

Accelerating Consensus Reaching Through Top Persuaders: A Social Persuasion Model in Social Network Group Decision Making

Author

Listed:
  • Bin Pan

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Jingti Han

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
    Shanghai Engineering Research Center of Finance Intelligence, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Bo Tian

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
    MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Yunhan Liu

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

  • Shenbao Liang

    (School of Law, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

In traditional group decision-making models, it is commonly assumed that all decision makers exert equal influence on one another. However, in real-world social networks, such as Twitter and Facebook, certain individuals—known as top persuaders—hold a disproportionately large influence over others. This study formulates the consensus-reaching problem in social network group decision making by introducing a novel framework for predicting top persuaders. Building on social network theories, we develop a social persuasion model that integrates social influence and social status to quantify individuals’ persuasive power more comprehensively. Subsequently, we propose a new CRP that leverages the influence of top persuaders. Our simulations and comparative analyses demonstrate that: (1) increasing the number of top persuaders substantially reduces the iterations required to achieve consensus; (2) establishing trust relationships between top persuaders and other individuals accelerates the consensus process; and (3) top persuaders retain a high and stable level of influence throughout the entire CRP rounds. Our research provides practical insights into identifying and strategically guiding top persuaders to enhance the efficiency in consensus reaching and reduce social management costs within social networked environments.

Suggested Citation

  • Bin Pan & Jingti Han & Bo Tian & Yunhan Liu & Shenbao Liang, 2025. "Accelerating Consensus Reaching Through Top Persuaders: A Social Persuasion Model in Social Network Group Decision Making," Mathematics, MDPI, vol. 13(3), pages 1-26, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:385-:d:1576419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/3/385/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/3/385/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kacprzyk, Janusz & Fedrizzi, Mario, 1988. "A `soft' measure of consensus in the setting of partial (fuzzy) preferences," European Journal of Operational Research, Elsevier, vol. 34(3), pages 316-325, March.
    2. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    3. Herrera, F. & Herrera-Viedma, E., 2000. "Choice functions and mechanisms for linguistic preference relations," European Journal of Operational Research, Elsevier, vol. 120(1), pages 144-161, January.
    4. Wang, Fang & Zhang, Hengjie & Wang, Jigan, 2025. "Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management," European Journal of Operational Research, Elsevier, vol. 321(3), pages 907-924.
    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. Harrison Hong & Terence Lim & Jeremy C. Stein, 2000. "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies," Journal of Finance, American Finance Association, vol. 55(1), pages 265-295, February.
    2. Liu, Hong & Qi, Lina & Li, Zaili, 2019. "Insider trading, representativeness heuristic insider, and market regulation," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 48-64.
    3. Seungwook Bahng, 2003. "Do Psychological Barriers Exist in the Stock Price Indices? Evidence from Asia's Emerging Markets," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 6(1), pages 35-52, March.
    4. Hou, Yang & Meng, Jiayin, 2018. "The momentum effect in the Chinese market and its relationship with the simultaneous and the lagged investor sentiment," MPRA Paper 94838, University Library of Munich, Germany.
    5. Keval Amin & Erica Harris, 2022. "The Effect of Investor Sentiment on Nonprofit Donations," Journal of Business Ethics, Springer, vol. 175(2), pages 427-450, January.
    6. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    7. Abdul Hamid Habbe, 2017. "Estimation Error of Earnings Information: A Test of Representativeness and Anchoring-adjustment Heuristic," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 224-233.
    8. Nicholas Apergis & Vasilios Plakandaras & Ioannis Pragidis, 2022. "Industry momentum and reversals in stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3093-3138, July.
    9. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    10. Gric, Zuzana & Ehrenbergerova, Dominika & Hodula, Martin, 2022. "The power of sentiment: Irrational beliefs of households and consumer loan dynamics," Journal of Financial Stability, Elsevier, vol. 59(C).
    11. Wu, Chen-Hui & Wu, Chin-Shun & Liu, Victor W., 2009. "The conservatism bias in an emerging stock market: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 17(4), pages 494-505, September.
    12. Brav, Alon & Geczy, Christopher & Gompers, Paul A., 2000. "Is the abnormal return following equity issuances anomalous?," Journal of Financial Economics, Elsevier, vol. 56(2), pages 209-249, May.
    13. Meng Fanyong & Tan Chunqiao, 2017. "Distance Measures for Hesitant Intuitionistic Fuzzy Linguistic Sets," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 207-224.
    14. Mark Grinblatt & Tobias J. Moskowitz, 2002. "What Do We Really Know About the Cross-Sectional Relation Between Past and Expected Returns?," NBER Working Papers 8744, National Bureau of Economic Research, Inc.
    15. L. Lin & M. Schatz & D. Sornette, 2019. "A simple mechanism for financial bubbles: time-varying momentum horizon," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 937-959, June.
    16. Li, Jinfang, 2014. "Multi-period sentiment asset pricing model with information," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 118-130.
    17. Jorgensen, Bjorn & Li, Jing & Sadka, Gil, 2012. "Earnings dispersion and aggregate stock returns," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 1-20.
    18. Blackburn, Douglas W. & Cakici, Nusret, 2017. "Overreaction and the cross-section of returns: International evidence," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 1-14.
    19. Tiziana Assenza & Te Bao & Cars Hommes & Domenico Massaro, 2014. "Experiments on Expectations in Macroeconomics and Finance," Research in Experimental Economics, in: Experiments in Macroeconomics, volume 17, pages 11-70, Emerald Group Publishing Limited.
    20. Leite, Brian J. & Uysal, Vahap B., 2023. "Does ESG matter to investors? ESG scores and the stock price response to new information," Global Finance Journal, Elsevier, vol. 57(C).

    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:13:y:2025:i:3:p:385-:d:1576419. 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.