Understanding the individual within the crowd: An analysis of how individual forecasters contribute to ideal group forecasts
Abstract: We investigate optimal group member configurations for producing a maximally accurate group forecast. Our approach accounts for group members that may be biased in their forecasts and/or have errors that correlate with the criterion values being forecast. We show that for large forecasting groups, the diversity of individual forecasts linearly trades off with individual forecaster accuracy when determining optimal group composition. We develop a statistical model that estimates features of individual forecasters when making aggregate forecasts. We use this model to better understand the conditions under which researchers should pursue optimal group weighting versus simply averaging the individual forecasts.