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Name: Superforecasting
Rating: 4 (127 reviews)
Author: Philip E. Tetlock & Dan Gardner

Philip E. Tetlock Dan Gardner

The Art and Science of Prediction

4 (127 ratings)

16 mins

Brief summary

Superforecasting by Philip E. Tetlock and Dan Gardner showcases how to improve prediction skills using scientific methods and team collaboration. It emphasizes the need for continuous learning, rigorous testing, and mental flexibility.

Topics

Sales Investments Mathematics

Table of Contents

Superforecasting
Summary of 7 key ideas

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Key idea 1 of 7

What’s in it for me? Learn how to make excellent forecasts.

The weather, the stock market, next year’s budget or who will win this weekend’s football game – all have one thing in common: predictions and forecasts are made about each. But these aren’t the only things we make predictions about. Our fixation on forecasting has permeated most areas of our life, and we get irritated when events don’t transpire as we thought they would. So can forecasts be better than they are today?

They can. We’ll soon be making superforcasts that are trimmed and realigned with each new piece of information and then analyzed and improved once the forecasted moment has passed. In these blinks, we’ll explore the complex and intriguing art of making the ultimate forecasts.

In these blinks, you’ll find out

why the former CEO of Microsoft predicted about the iPhone's market share;
how a forecaster predicted Yasser Arafat’s autopsy; and
why groups of forecasters are more successful than individual forecasters.

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.

Avoid using vague language and be as precise as possible.

Measuring forecasts might seem like a no-brainer: collect the forecasts, judge their accuracy, do the calculations, et voilà! But it isn’t that easy at all.

To ascertain the accuracy of a forecast, you must first understand the meaning of the original forecast. For example, in April, 2007, media outlets reported that Microsoft’s CEO, Steve Ballmer, had made a prediction: the iPhone would fail to win a significant market share. Considering Apple’s size, Ballmer’s forecast seemed ridiculous, and people literally laughed at him. Others highlighted the fact that Apple controlled 42 percent of the US smartphone market, an undeniably significant number.

Keep score if you want to improve the accuracy of your forecasts.

So how do we avoid horrendous errors like what happened with the WMDs? Clearly, more accuracy is needed in our forecasts. Let’s look at some ways of attaining this.

The best way is to keep score.

Superforecasters break down problems into smaller units to start their analysis.

Are all superforecasters gifted thinkers with access to top secret information? Not really. So how do they make such accurate predictions about the future?

A superforecaster tackles a question by breaking down seemingly impossible problems into bite-sized sub-problems. This is known as Fermi-style thinking.

Start from the outside, then turn to the inside view for an accurate forecast.

As every situation is unique, you should avoid jumping the gun and judging a case too quickly. The best way to approach any question is to take an outside view, which means finding out what the base rate is. But what exactly does that mean?

To offer an example, picture an Italian family who lives in a modest house in the United States. The father works as a bookkeeper and the mother has a part-time position at a daycare center. Their household consists of them, their child and the child’s grandmother.

Stay up-to-date even after your initial conclusion and adjust your forecasts with new information.

We’ve seen how superforecasters get the process going, but once you’ve made your initial forecast, you can’t just wait and see if you were right. You’ve got to update and modify your judgment based on any new piece of information.

Remember Bill Flack? After he forecasted a 60-percent chance of polonium being detected in Yasser Arafat’s corpse, he kept an eye on the news and updated his forecast whenever he deemed fit.

Working in teams can be helpful in forecasting, but only if you do it right.

Perhaps you’ve heard of the term groupthink. Psychologist Irving Janis, who coined the term, hypothesized that people in small groups build team spirit by unconsciously creating shared illusions that interfere with critical thinking. This interference comes from people shying away from confrontation and simply agreeing with one another.

But not conforming is a source of real value. Independent expression and thought are tremendous assets in any team.

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What is Superforecasting about?

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.

Superforecasting Review

Superforecasting (2015) offers invaluable insights into the art and science of prediction, making it a worthwhile read for anyone interested in understanding how to make better forecasts. Here's why this book stands out:

Packed with compelling case studies and examples, it demonstrates how ordinary people with little-to-no expertise can outperform professional forecasters.
By dissecting the factors that contribute to accurate forecasting, the book provides practical strategies that readers can apply to their own decision-making processes.
With its engaging storytelling and relatable examples, the book manages to make a subject as complex as forecasting accessible and surprisingly entertaining.

Best quote from Superforecasting

When the facts change, I change my mind - John Maynard Keynes

—Philip E. Tetlock & Dan Gardner

Who should read Superforecasting?

Those curious about how forecasting works
Critical thinkers
Businesspeople wanting to improve their predictions

About the Author

Philip E. Tetlock, the Annenberg University Professor at the University of Pennsylvania, specializes in political science and psychology. The leader of the forecasting study Good Judgment Project, he has published over 200 articles in peer-reviewed journals.

Dan Gardner is a journalist, author and lecturer. Author of the influential books Risk: The Science and Politics of Fear and Future Babble, Gardner has also lectured internationally at government events and for corporations such as Google and Siemens.

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