Bias, Information, Noise: The BIN Model of Forecasting

• 成果类型: Article
• 署名作者: Satopaa, Ville A.; Salikhov, Marat; Tetlock, Philip E.; Mellers, Barbara
• 署名单位: INSEAD Business School; Yale University; University of Pennsylvania
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3882
• 发表日期: 2021
• 页码: 7599-7618
• 关键词: Bayesian statistics; judgmental forecasting; Partial information; Shapley value; Wisdom of crowds

摘要

A four-year series of subjective probability forecasting tournaments sponsored by the U.S. intelligence community revealed a host of replicable drivers of predictive accuracy, including experimental interventions such as training in probabilistic reasoning, anti-groupthink teaming, and tracking of talent. Drawing on these data, we propose a Bayesian BIN model (Bias, Information, Noise) for disentangling the underlying processes that enable forecasters and forecasting methods to improve-either by tamping down bias and noise in judgment or by ramping up the efficient extraction of valid information from the environment. The BIN model reveals that noise reduction plays a surprisingly consistent role across all three methods of enhancing performance. We see the BIN method as useful in focusing managerial interventions on what works when and why in a wide range of domains. An R-package called BINtools implements our method and is available on the