IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v7y2024i2d10.1007_s42001-024-00273-8.html
   My bibliography  Save this article

Is from ought? A comparison of unsupervised methods for structuring values-based wisdom-of-crowds estimates

Author

Listed:
  • Nathan Brugnone

    (Michigan State University
    Michigan State University
    Two Six Technologies)

  • Noam Benkler

    (Smart Information Flow Technologies)

  • Peter Revay

    (Two Six Technologies)

  • Rebecca Myhre

    (Two Six Technologies)

  • Scott Friedman

    (Smart Information Flow Technologies)

  • Sonja Schmer-Galunder

    (Smart Information Flow Technologies)

  • Steven Gray

    (Michigan State University)

  • James Gentile

    (Two Six Technologies)

Abstract

Many social and ecological problems require us to consider objectively verifiable phenomena as well as subjective states of knowledge and associated value systems. When approximating the facts of reality, the wisdom of crowds phenomenon demonstrates that many pooled estimates can be more accurate than individual or expert estimates. For complex and social systems, wisdom of crowd approaches are improved by aggregating knowledge over subpopulations. In this paper we consider subpopulations defined by different sets of shared values. We first discuss two approaches to qualitatively understanding differences in value sets held by individuals and groups, which in turn motivate our discussion of three unsupervised methods for identifying subpopulations based upon value-laden statements in narrative data from hyperlocal maternal and child health (MCH) contexts in Gombe State, Nigeria. We employ data science techniques and compare methods to assess the stability of inferences. We find the hypothesized groups to be method dependent and discuss implications for wisdom-of-crowd estimates in sustainable development contexts.

Suggested Citation

  • Nathan Brugnone & Noam Benkler & Peter Revay & Rebecca Myhre & Scott Friedman & Sonja Schmer-Galunder & Steven Gray & James Gentile, 2024. "Is from ought? A comparison of unsupervised methods for structuring values-based wisdom-of-crowds estimates," Journal of Computational Social Science, Springer, vol. 7(2), pages 1327-1377, October.
  • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:2:d:10.1007_s42001-024-00273-8
    DOI: 10.1007/s42001-024-00273-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00273-8
    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/s42001-024-00273-8?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. Payam Aminpour & Steven A. Gray & Antonie J. Jetter & Joshua E. Introne & Alison Singer & Robert Arlinghaus, 2020. "Wisdom of stakeholder crowds in complex social–ecological systems," Nature Sustainability, Nature, vol. 3(3), pages 191-199, March.
    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. Joshua Aaron Becker & Douglas Guilbeault & Edward Bishop Smith, 2022. "The Crowd Classification Problem: Social Dynamics of Binary-Choice Accuracy," Management Science, INFORMS, vol. 68(5), pages 3949-3965, May.
    2. Payam Aminpour & Heike Schwermer & Steven Gray, 2021. "Do social identity and cognitive diversity correlate in environmental stakeholders? A novel approach to measuring cognitive distance within and between groups," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-18, November.
    3. Zare, Fateme & Jakeman, Anthony J. & Elsawah, Sondoss & Guillaume, Joseph H.A., 2024. "Bridging practice and science in socio-environmental systems research and modelling: A design science approach," Ecological Modelling, Elsevier, vol. 492(C).
    4. Kelly F. Robinson & Mark R. DuFour & Jason L. Fischer & Seth J. Herbst & Michael L. Jones & Lucas R. Nathan & Tammy J. Newcomb, 2023. "Lessons Learned in Applying Decision Analysis to Natural Resource Management for High-Stakes Issues Surrounded by Uncertainty," Decision Analysis, INFORMS, vol. 20(4), pages 326-342, December.
    5. Ghani, Latifah Abdul & Mahmood, Noor Zalina, 2023. "Modeling domestic wastewater pathways on household system using the socio-MFA techniques," Ecological Modelling, Elsevier, vol. 480(C).
    6. Tong, De & Sun, Yiyu & Tang, Junqing & Luo, Zhenying & Lu, Jinfeng & Liu, Xuan, 2023. "Modeling the interaction of internal and external systems of rural settlements: The case of Guangdong, China," Land Use Policy, Elsevier, vol. 132(C).
    7. Haeussler, Carolin & Vieth, Sabrina, 2022. "A question worth a million: The expert, the crowd, or myself? An investigation of problem solving," Research Policy, Elsevier, vol. 51(3).
    8. Sumaiya Haque & Hesam Mahmoudi & Navid Ghaffarzadegan & Konstantinos Triantis, 2023. "Mental models, cognitive maps, and the challenge of quantitative analysis of their network representations," System Dynamics Review, System Dynamics Society, vol. 39(2), pages 152-170, 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:jcsosc:v:7:y:2024:i:2:d:10.1007_s42001-024-00273-8. 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.