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Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions

机译:识别和培养超级预报员作为改进概率预测的方法

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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 createdand 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.
机译:在各种各样的任务中,研究表明,人们对未来事件的概率预测很差。最近,美国情报共同体赞助了一系列预测比赛,旨在探索产生精确的主观概率估计地缘政治事件的最佳策略。在本文中,我们描述了制胜战略:每年淘汰表现最好的人,并将他们分配给超级预测员的精英团队。超级预测员连续两年均无法回归预期,因此在数百个问题和广泛主题中保持了很高的准确性。我们发现支持有关超级预报员表现的四个相互补充的解释:(a)认知能力和风格,(b)特定于任务的技能,(c)动机和承诺,以及(d)丰富的环境。这些发现表明,超级预报员的部分发现和部分创造,以及锦标赛的高性能激励机制凸显了人类判断的各个方面,而在专注于典型性能的实验室范式中这是无法发现的。

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