A Bayesian Hierarchical Model of Crowd Wisdom Based on Predicting Opinions of Others

John McCoy () and Drazen Prelec ()
Additional contact information
John McCoy: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Drazen Prelec: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Management Science, 2024, vol. 70, issue 9, 5931-5948

Abstract: In many domains, it is necessary to combine opinions or forecasts from multiple individuals. However, the average or modal judgment is often incorrect, shared information across respondents can result in correlated errors, and weighting judgments by confidence does not guarantee accuracy. We develop a Bayesian hierarchical model of crowd wisdom that incorporates predictions about others to address these aggregation challenges. The proposed model can be applied to single questions, and it can also estimate respondent expertise given multiple questions. Unlike existing Bayesian hierarchical models for aggregation, the model does not link the correct answer to consensus or privilege majority opinion. The model extends the “surprisingly popular algorithm” to enable statistical inference and in doing so, overcomes several of its limitations. We assess performance on empirical data and compare the results with other aggregation methods, including leading Bayesian hierarchical models.

Keywords: wisdom of crowds; expertise; Bayesian hierarchical model; surprisingly popular answer (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2023.4955 (application/pdf)

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:inm:ormnsc:v:70:y:2024:i:9:p:5931-5948

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().