Psychometric Measurement of Forecasters Using the Wisdom of Crowds

Forecasting skill is often measured by the average error between forecasters’ predictions and the ground truth, but the inherent uncertainty in event outcomes adds an additional layer of measurement error onto skill estimation. Intersubjective measures offer an alternative approach to skill measurement: comparing forecasters’ predictions to those of their peers, typically to aggregated forecasts from groups of peers. A key advantage of this approach is that forecasts can be scored in real time, without having to wait for the ground truth outcome to be realized. However, it has another more subtle advantage as well: aggregate predictions can be less noisy than ground truth outcomes, leading to a potential reduction in this additionalmeasurement error. In a simulation study, we demonstrate conditions under which crowd aggregates provide a more reliable indicator of the optimal forecast for a given event than a single realized ground truth outcome, leading to skill measurement with reduced measurement error. We also demonstrate the effectiveness of intersubjective methods in a real-world forecasting study in which 894 participants made both forecasts and metapredictions, i.e., predictions of what others might forecast. As in our simulation, intersubjective measures capture forecasting ability more efficiently than do ground-truth scores, demonstrating their usefulness for identifying high-performing forecasters.