Eliciting and Aggregating Forecasts When Information is Shared

Using the wisdom of crowds -- combining many individual forecasts to obtain an aggregate estimate -- can be an effective technique for improving forecast accuracy. However, correlated forecast errors greatly limit the ability of the wisdom of crowds to recover the truth. In practice, this dependence often emerges because information is shared. To address this problem, we propose an elicitation procedure in which each respondent is asked to provide both their own best forecast and a guess of the average forecast that will be given by all other respondents. We develop an aggregation method, called pivoting, which separates individual forecasts into shared and private information and then recombines these results in the optimal manner. In several studies, we investigate the method and examine the accuracy of the aggregate forecasts. Overall, the empirical data suggest that the pivoting method provides an effective forecast aggregation procedure that can significantly outperform the simple crowd average.