Group Discussions Improve Competence Calibration: Making Self-Perceived Competence Valuable When Harnessing the Wisdom of Crowds

• 成果类型: Article
• 署名作者: Goedde-Menke, Michael; Diecidue, Enrico; Jacobs, Andreas; Langer, Thomas
• 署名单位: University of Munster; INSEAD Business School
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03061
• 发表日期: 2026
• 关键词: estimation accuracy; wisdom of the crowd; calibration; competence weighting; prediction markets

摘要

This paper experimentally demonstrates that group discussions can serve as an instrument to improve competence calibration, which in turn allows getting more wisdom out of the crowd through competence weighting. While the alignment of individuals' estimation accuracy and self-perceived competence is typically poor and competence-weighted aggregates do not even match the accuracy of simple averaging, we find that preceding group discussions on unrelated judgment problems enhance competence calibration. Consequently, the subsequent performance of competence-weighted aggregation schemes rises to and beyond prediction market levels, suggesting an easy-to-implement approach for effectively exploiting crowd wisdom.