Improving Judgmental Forecasts with Judgmental Bootstrapping and Task Feedback Support

摘要

This study examines the utility of two widely advocated methods for supporting judgmental forecasts—providing task feedback and providing judgmental bootstrapping support. In a simulated laboratory based experiment that focused on producing composite sales forecasts from three individual components, we compared the effectiveness of these two methods in improving final judgmental forecasts. In the presence of cognitive feedback task, feedback led to better forecasts than providing judgmental bootstrap forecasts. Simply providing bootstrap forecasts was of no additional benefit over a control condition. This was true in terms of the Brunswik Lens model measures of achievement, knowledge, and consistency, and in terms of forecast accuracy. This occurred both in stable environments and when special events (unusual one-time events requiring adjustments to the forecasts) arose.