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

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. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03061 .