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

Nathan Brugnone (), Noam Benkler (), Peter Revay (), Rebecca Myhre (), Scott Friedman (), Sonja Schmer-Galunder (), Steven Gray () and James Gentile ()
Additional contact information
Nathan Brugnone: Michigan State University
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

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 8, 1327-1377

Abstract: 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.

Keywords: Data science; Clustering; Unsupervised machine learning; Wisdom of crowds; Values (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-024-00273-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:7:y:2024:i:2:d:10.1007_s42001-024-00273-8

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-024-00273-8

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().