Expectations: Point-Estimates, Probability Distributions, Confidence, and Forecasts

In this article I test a new graphical, interactive interface that captures both " best estimate " point‐estimates and probability distributions from non‐experts. As in the previous literature, respondents are overconfident. My innovation is to show that in contrast to the standard method of directly asking respondents to state their confidence, using my method, which induces the respondents to reveal confidence, there is a sizable and statically significant positive relationship between the respondents' confidence and the accuracy of their individual‐ level expectations. This positive correlation between confidence and accuracy can be utilized to create confidence‐weighted aggregated forecasts that are more accurate than the standard " consensus forecasts ". The payment of financial incentives does not affect these findings. and the two anonymous reviewers for the Workshop on Social Computing and User Generated Content. Further thanks to the Wharton Risk Center's Ackoff grant program that helped me fund this project.