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Superforecasting

The Art and Science of Prediction

By Philip E. Tetlock and Dan Gardner
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  • Contains 8 key ideas
Superforecasting: The Art and Science of Prediction by Philip E. Tetlock and Dan Gardner
Synopsis

Based on decades of research and the results of a massive, government-funded forecasting tournament, Superforecasting (2015) describes how to make your predictions more accurate, whether you’re trying to anticipate changes in the stock market, politics or daily life.

Key idea 1 of 8

Forecasting has certain limitations, but that’s no reason to dismiss it.   

Forecasting is something we do all the time, whether we’re mapping our next career move or choosing an investment. Essentially, our forecasts reflect our expectations about what the future holds.

Forecasting is limited, though, since minor events can lead to unforeseen consequences.

We live in a complex world where a single person can instigate huge events. Consider the Arab Spring. It all started when one Tunisian street vendor, Mohamed Bouazizi, set himself on fire after being humiliated by corrupt police officers.

There is a theoretical explanation of why it’s difficult to predict such events. It’s called chaos theory (also known as the butterfly effect), and American meteorologist Edward Lorenz explains it thus: in nonlinear systems like the Earth’s atmosphere, even minute changes can have a considerable impact. If the trajectory of the wind shifts by less than a fraction of a degree, the long-term weather patterns can change drastically. Dramatically put: the flap of a butterfly’s wing in Brazil can cause a tornado in Texas.

But we shouldn’t scrap forecasting altogether just because it has its limitations. Take Edward Lorenz’s field, meteorology. Weather forecasts are relatively reliable when made a few days in advance. Why? Because weather forecasters analyze the accuracy of their forecasts after the fact. By comparing their forecast with the actual weather, they improve their understanding of how the weather works.

The problem is, people in other fields usually do not measure the accuracy of their forecasts!

To improve our forecasting, then, we need to work on accuracy and get serious about comparing what we thought would happen with what actually ends up taking place. And that means getting serious about measuring.

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