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Faculty & Research


Bias, Information, Noise: The BIN Model of Forecasting

Journal Article
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, the authors 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. The authors 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 their method and is available on the first author’s personal website.

Associate Professor of Technology and Operations Management