Every forecaster has wanted to know what the most important factor for improving forecasting accuracy is, but for a long time the answer was not clear. Thanks to a chance overlap of co-authors Ville Satopää and Marat Salikhov at INSEAD, however, a new paper was published alongside forecasting pioneers Philip Tetlock and Barbara Mellers that does a great job of providing a solution.

Their paper, “Bias, Information, Noise: The BIN Model of Forecasting,” deconstructs the forecasting process into its component parts of: Information (the inputs you use to move your forecast away from the base rate), Bias (systematic error across a number of forecasts from a single forecaster), and Noise (non-information that is registered as information in a forecast). From there they test which of these parts is most critical to the accuracy of a forecast, and posit methods to improve in these areas.

In this episode we are lucky enough to sit down with Ville and Marat to discuss the origins of this paper, its findings, and the implications for the future of forecasting. We talk about possible avenues for further research based on the exciting results from Ville and Marat’s research, and even speculate on potential applications of the research in new and interesting environments.

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