Bias, Information, Noise: The BIN Model of Forecasting
基于美国情报界四年主观概率预测锦标赛数据，提出贝叶斯BIN模型，分离偏差、信息和噪声对预测准确性的影响，发现降噪是提升预测效果的关键，并提供了R包BINtools。
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 first author’s personal website. This paper was accepted by Manel Baucells, decision analysis.