Publication | Closed Access
Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions
238
Citations
47
References
2015
Year
Artificial IntelligenceEngineeringMachine LearningCognitionJudgmental ForecastingU.s. Intelligence CommunitySuperforecaster PerformanceSocial SciencesProbabilistic ForecastingData ScienceData MiningUncertainty QuantificationCollective IntelligenceBiasCognitive Bias MitigationStatisticsCognitive SciencePredictive AnalyticsKnowledge DiscoveryPredictive ModelingStrategyExperimental PsychologyImproving Probabilistic PredictionsHigh AccuracyPerformance StudiesHigh-performance Sport
Across a wide range of tasks, research has shown that people make poor probabilistic predictions of future events. Recently, the U.S. Intelligence Community sponsored a series of forecasting tournaments designed to explore the best strategies for generating accurate subjective probability estimates of geopolitical events. In this article, we describe the winning strategy: culling off top performers each year and assigning them into elite teams of superforecasters. Defying expectations of regression toward the mean 2 years in a row, superforecasters maintained high accuracy across hundreds of questions and a wide array of topics. We find support for four mutually reinforcing explanations of superforecaster performance: (a) cognitive abilities and styles, (b) task-specific skills, (c) motivation and commitment, and (d) enriched environments. These findings suggest that superforecasters are partly discovered and partly created-and that the high-performance incentives of tournaments highlight aspects of human judgment that would not come to light in laboratory paradigms focused on typical performance.
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