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.

This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and openmindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs. A t APA, we understand how challenging it can be for you to stay abreast of the latest and best academic research on psychology. Disciplines Finance and Financial Management | Social and Behavioral Sciences

The CAD triad hypothesis (Rozin, Lowery, Imada, & Haidt, 1999) stipulates that, cross-culturally, people feel anger for violations of autonomy, contempt for violations of community, and disgust for violations of divinity. Although the disgust-divinity link has received some measure of empirical support, the results have been difficult to interpret in light of several conceptual and design flaws. Taking a revised methodological approach, including use of newly validated (Study 1), pathogen-free violations of the divinity code, we found (Study 2) little evidence of disgust-related phenomenology (nausea, gagging, loss of appetite) or action tendency (desire to move away), but much evidence of anger-linked desire to retaliate, as a major component of individuals' projected response to "pure" (pathogen-free) violations of the divinity code. Study 3 replicated these results using faces in lieu of words as a dependent measure. Concordant findings emerged from an archival study (Study 4) examining the aftermath of a real-life sacred violation-the burning of Korans by U.S. military personnel. Study 5 further corroborated these results using continuous measures based on everyday emotion terms and new variants of the divinity-pure scenarios featuring sacrilegious acts committed by a theologically irreverent member of one's own group rather than an ideologically opposed member of another group. Finally, a supplemental study found the anger-dominant attribution pattern to remain intact when the impious act being judged was the judge's own. Based on these and related results, we posit anger to be the principal emotional response to moral transgressions irrespective of the normative content involved.

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.

A growing body of research indicates that forecasting skill is a unique and stable trait: forecasters with a track record of high accuracy tend to maintain this record. But how does one identify skilled forecasters effectively? We address this question using data collected during two seasons of a longitudinal geopolitical forecasting tournament. Our first analysis, which compares psychometric traits assessed prior to forecasting, indicates intelligence consistently predicts accuracy. Next, using methods adapted from classical test theory and item response theory, we model latent forecasting skill based on the forecasters’ past accuracy, while accounting for the timing of their forecasts relative to question resolution. Our results suggest these methods perform better at assessing forecasting skill than simpler methods employed by many previous studies. By parsing the data at different time points during the competitions, we assess the relative importance of each information source over time. When past performance information is limited, psychometric traits are useful predictors of future performance, but, as more information becomes available, past performance becomes the stronger predictor of future accuracy. Finally, we demonstrate the predictive validity of these results on out-of-sample data, and their utility in producing performance weights for wisdom-of-crowds aggregations.