Five university-based research groups competed to recruit forecasters, elicit their predictions, and aggregate those predictions to assign the most accurate probabilities to events in a 2-year geopolitical forecasting tournament. Our group tested and found support for three psychological drivers of accuracy: training, teaming, and tracking. Probability training corrected cognitive biases, encouraged forecasters to use reference classes, and provided forecasters with heuristics, such as averaging when multiple estimates were available. Teaming allowed forecasters to share information and discuss the rationales behind their beliefs. Tracking placed the highest performers (top 2% from Year 1) in elite teams that worked together. Results showed that probability training, team collaboration, and tracking improved both calibration and resolution. Forecasting is often viewed as a statistical problem, but forecasts can be improved with behavioral interventions. Training, teaming, and tracking are psychological interventions that dramatically increased the accuracy of forecasts. Statistical algorithms (reported elsewhere) improved the accuracy of the aggregation. Putting both statistics and psychology to work produced the best forecasts 2 years in a row.

We report the results of the first large-scale, long-term, experimental test between two crowdsourcing methods: prediction markets and prediction polls. More than 2,400 participants made forecasts on 261 events over two seasons of a geopolitical prediction tournament. Forecasters were randomly assigned to either prediction markets (continuous double auction markets) in which they were ranked based on earnings, or prediction polls in which they submitted probability judgments, independently or in teams, and were ranked based on Brier scores. In both seasons of the tournament, prices from the prediction market were more accurate than the simple mean of forecasts from prediction polls. However, team prediction polls outperformed prediction markets when forecasts were statistically aggregated using temporal decay, differential weighting based on past performance, and recalibration. The biggest advantage of prediction polls was at the beginning of long-duration questions. Results suggest that prediction polls with proper scoring feedback, collaboration features, and statistical aggregation are an attractive alternative to prediction markets for distilling the wisdom of crowds. This paper was accepted by Uri Gneezy, behavioral economics.

This research examines the development of confidence and accuracy over time in the context of forecasting. Although overconfidence has been studied in many contexts, little research examines its progression over long periods of time or in consequential policy domains. This study employs a unique data set from a geopolitical forecasting tournament spanning three years in which thousands of forecasters predicted the outcomes of hundreds of events. We sought to apply insights from research to structure the questions, interactions, and elicitations to improve forecasts. Indeed, forecasters’ confidence roughly matched their accuracy. As information came in, accuracy increased. Confidence increased at approximately the same rate as accuracy, and good calibration persisted. Nevertheless, there was evidence of a small amount of overconfidence (3%), especially on the most confident forecasts. Training helped reduce overconfidence, and team collaboration improved forecast accuracy. Together, teams and training reduced overconfidence to 1%. Our results provide reason for tempered optimism regarding confidence calibration and its development over time in consequential field contexts. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

Performance (such as a course grade) is a joint function of an individual’s ability (such as intelligence) and the situation (such as the instructor’s grading leniency). Prior research has documented a human bias toward dispositional inference, which ascribes performance to individual ability, even when it is better explained through situational influences on performance. It is hypothesized here that this tendency leads admissions decisions to favor students coming from institutions with lenient grading because those students have their high grades mistaken for evidence of high ability. Three experiments show that those who obtain high scores simply because of lenient grading are favored in selection. These results have implications for research on attribution because they provide a more stringent test of the correspondence bias and allow for a more precise measure of its size. Implications for university admissions and personnel selection decisions are also discussed.

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