Comparing Face-to-Face Meetings, Nominal Groups, Delphi and Prediction Markets on an Estimation Task

We conducted laboratory experiments to analyze the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) compared to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) that had to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While, overall, we did not find statistically significant differences in accuracy between the four methods, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets were unable to outperform FTF for any of the ten questions but were inferior for three questions. The relative performance of nominal groups and FTF was mixed and differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value compared to a simple average of forecast. In addition, we examined participants’ perceptions of the group and the group process. Participants rated personal communication more favorable than computer-mediated interaction. Group interaction in FTF and nominal groups was perceived as highly cooperative and effective. Prediction markets were rated least favorable. Prediction market participants were least satisfied with the group process and perceived their method as most difficult. Disciplines Business Comments Suggested Citation: Graefe, A. and Armstrong, J.S. (2011). Comparing Face-to-Face Meetings, Nominal Groups, Delphi and Prediction Markets on an Estimation Task. International Journal of Forecasting. Vol. 27(1). p. 183-195. Publisher URL: http://dx.doi.org/10.1016/j.ijforecast.2010.05.004 This journal article is available at ScholarlyCommons: http://repository.upenn.edu/marketing_papers/176