Global Guessing Podcasts

Global Guessing Podcasts Trailer Bonus Episode 20 Season 1

Satopää and Salikhov on Bias, Information, and Noise Model of Forecasting (GGWP 16)

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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.

Show Notes

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.

What is Global Guessing Podcasts?

Home of the Global Guessing Weekly Podcast (GGWP) and The Right Side of Maybe. GGWP is a weekly podcast about geopolitics and the science of forecasting hosted by the co-founders of globalguessing.com, Clay Graubard and Andrew Eaddy. Andrew and Clay also host the guest-focused, The Right Side of Maybe: A new podcast where we learn from and about elite forecasters.