IDEAS home Printed from https://ideas.repec.org/a/inm/ordeca/v20y2023i2p133-150.html
   My bibliography  Save this article

Group Structure and Information Distribution on the Emergence of Collective Intelligence

Author

Listed:
  • Ming Tang

    (Business School, Sichuan University, Chengdu 610064, China)

  • Huchang Liao

    (Business School, Sichuan University, Chengdu 610064, China)

Abstract

More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems.

Suggested Citation

  • Ming Tang & Huchang Liao, 2023. "Group Structure and Information Distribution on the Emergence of Collective Intelligence," Decision Analysis, INFORMS, vol. 20(2), pages 133-150, June.
  • Handle: RePEc:inm:ordeca:v:20:y:2023:i:2:p:133-150
    DOI: 10.1287/deca.2022.0466
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/deca.2022.0466
    Download Restriction: no

    File URL: https://libkey.io/10.1287/deca.2022.0466?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
    ---><---

    References listed on IDEAS

    as
    1. Giuseppe Carbone & Ilaria Giannoccaro, 2015. "Model of human collective decision-making in complex environments," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(12), pages 1-10, December.
    2. David V. Budescu & Eva Chen, 2015. "Identifying Expertise to Extract the Wisdom of Crowds," Management Science, INFORMS, vol. 61(2), pages 267-280, February.
    3. Diane L. Rulke & Joseph Galaskiewicz, 2000. "Distribution of Knowledge, Group Network Structure, and Group Performance," Management Science, INFORMS, vol. 46(5), pages 612-625, May.
    4. Frenken, Koen, 2006. "A fitness landscape approach to technological complexity, modularity, and vertical disintegration," Structural Change and Economic Dynamics, Elsevier, vol. 17(3), pages 288-305, September.
    5. 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.
    6. Massari, Giovanni Francesco & Giannoccaro, Ilaria, 2021. "Investigating the effect of horizontal coopetition on supply chain resilience in complex and turbulent environments," International Journal of Production Economics, Elsevier, vol. 237(C).
    7. Nihal Berktaş & Hande Yaman, 2021. "A Branch-and-Bound Algorithm for Team Formation on Social Networks," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1162-1176, July.
    8. 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).
    9. Arthur Carvalho, 2016. "An Overview of Applications of Proper Scoring Rules," Decision Analysis, INFORMS, vol. 13(4), pages 223-242, December.
    10. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao & Xiaoxin Mao, 2021. "Data-Driven Preference Learning Methods for Value-Driven Multiple Criteria Sorting with Interacting Criteria," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 586-606, May.
    11. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    12. John P. Lightle & John H. Kagel & Hal R. Arkes, 2009. "Information Exchange in Group Decision Making: The Hidden Profile Problem Reconsidered," Management Science, INFORMS, vol. 55(4), pages 568-581, April.
    13. Asa B. Palley & Jack B. Soll, 2019. "Extracting the Wisdom of Crowds When Information Is Shared," Management Science, INFORMS, vol. 67(5), pages 2291-2309, May.
    14. Argote, Linda & Turner, Marlene E. & Fichman, Mark, 1989. "To centralize or not to centralize: The effects of uncertainty and threat on group structure and performance," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(1), pages 58-74, February.
    15. Gruenfeld, Deborah H & Mannix, Elizabeth A. & Williams, Katherine Y. & Neale, Margaret A., 1996. "Group Composition and Decision Making: How Member Familiarity and Information Distribution Affect Process and Performance," Organizational Behavior and Human Decision Processes, Elsevier, vol. 67(1), pages 1-15, July.
    16. Clintin P. Davis-Stober & David V. Budescu & Stephen B. Broomell & Jason Dana, 2015. "The Composition of Optimally Wise Crowds," Decision Analysis, INFORMS, vol. 12(3), pages 130-143.
    17. Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe, 2019. "Are distrust relationships beneficial for group performance? The influence of the scope of distrust on the emergence of collective intelligence," International Journal of Production Economics, Elsevier, vol. 208(C), pages 343-355.
    18. Paul Varella & Mansour Javidan & David A. Waldman, 2012. "A Model of Instrumental Networks: The Roles of Socialized Charismatic Leadership and Group Behavior," Organization Science, INFORMS, vol. 23(2), pages 582-595, April.
    19. Hannu Nurmi, 1998. "Voting paradoxes and referenda," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 15(3), pages 333-350.
    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. Jaspersen, Johannes G., 2022. "Convex combinations in judgment aggregation," European Journal of Operational Research, Elsevier, vol. 299(2), pages 780-794.
    2. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    3. Brown, Alasdair & Reade, J. James, 2019. "The wisdom of amateur crowds: Evidence from an online community of sports tipsters," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1073-1081.
    4. Marcellin Martinie & Tom Wilkening & Piers D L Howe, 2020. "Using meta-predictions to identify experts in the crowd when past performance is unknown," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-11, April.
    5. Xiaoming He & Yaqun Yi & Zelong Wei, 2019. "New product development capabilities in China: the moderating role of TMT cooperative behavior," Asian Business & Management, Palgrave Macmillan, vol. 18(2), pages 73-97, April.
    6. Saul Estrin & Susanna Khavul & Mike Wright, 2022. "Soft and hard information in equity crowdfunding: network effects in the digitalization of entrepreneurial finance," Small Business Economics, Springer, vol. 58(4), pages 1761-1781, April.
    7. Tine Buyl & Christophe Boone & Walter Hendriks & Paul Matthyssens, 2011. "Top Management Team Functional Diversity and Firm Performance: The Moderating Role of CEO Characteristics," Journal of Management Studies, Wiley Blackwell, vol. 48(1), pages 151-177, January.
    8. Muye Chen & Michel Regenwetter & Clintin P. Davis-Stober, 2021. "Collective Choice May Tell Nothing About Anyone’s Individual Preferences," Decision Analysis, INFORMS, vol. 18(1), pages 1-24, March.
    9. Jon Atwell & Marlon Twyman II, 2023. "Metawisdom of the Crowd: How Choice Within Aided Decision Making Can Make Crowd Wisdom Robust," Papers 2308.15451, arXiv.org.
    10. Diane L. Rulke & Joseph Galaskiewicz, 2000. "Distribution of Knowledge, Group Network Structure, and Group Performance," Management Science, INFORMS, vol. 46(5), pages 612-625, May.
    11. Cem Peker, 2023. "Extracting the collective wisdom in probabilistic judgments," Theory and Decision, Springer, vol. 94(3), pages 467-501, April.
    12. repec:cup:judgdm:v:13:y:2018:i:2:p:185-201 is not listed on IDEAS
    13. Christophe Boone & Walter Hendriks, 2009. "Top Management Team Diversity and Firm Performance: Moderators of Functional-Background and Locus-of-Control Diversity," Management Science, INFORMS, vol. 55(2), pages 165-180, February.
    14. Satopää, Ville A. & Salikhov, Marat & Tetlock, Philip E. & Mellers, Barbara, 2023. "Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model," International Journal of Forecasting, Elsevier, vol. 39(1), pages 470-485.
    15. Robert L. Winkler & Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose, 2019. "Probability Forecasts and Their Combination: A Research Perspective," Decision Analysis, INFORMS, vol. 16(4), pages 239-260, December.
    16. Meng, Fanyong & Tang, Jie & An, Qingxian, 2023. "Cooperative game based two-stage consensus adjustment mechanism for large-scale group decision making," Omega, Elsevier, vol. 117(C).
    17. Edgar C. Merkle & Robert Hartman, 2018. "Weighted Brier score decompositions for topically heterogenous forecasting tournaments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(2), pages 185-201, March.
    18. 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.
    19. Tang, Ming & Liao, Huchang, 2021. "Multi-attribute large-scale group decision making with data mining and subgroup leaders: An application to the development of the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    20. Vitalii Antoshchuk & Volodymyr Filippov & Varvara Kuvaieva, 2021. "Development of methodological support for improving the quality of expert assessment of business processes," Technology audit and production reserves, Socionet;Technology audit and production reserves, vol. 1(4(57)), pages 22-27.
    21. Dan Zhu & Qingwei Wang & John Goddard, 2022. "A new hedging hypothesis regarding prediction interval formation in stock price forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 697-717, July.

    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:inm:ordeca:v:20:y:2023:i:2:p:133-150. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.