SUPERFORECASTING summary

Chapter 1: Forecasting has certain limitations,

Superforecasting by Philip Tetlock and Dan Gardner 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 superforecasts that are trimmed and realigned with each new piece of information and then analyzed and improved once the forecasted moment has passed. In this summary, we'll explore the complex and intriguing art of making the ultimate forecasts. In this summary, you'll find out what 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. Chapter 1.

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.

Chapter 2: Avoid using vague language

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% of the U. S. smartphone market, an undeniably significant number. But let's take a look at what he actually said.

He said that, indeed, the iPhone might generate a lot of money, however, it would never gain a significant market share in the global cell phone market; his prediction, between 2% and 3%. Rather, the software from his company, Microsoft, would come to dominate, and this prediction was, more or less, correct. According to Gartner IT data, iPhone's global share of mobile phone sales during the third quarter of 2013 sat at around 6%, which is clearly higher than what Ballmer predicted, but it's not way off. Meanwhile, Microsoft's software was being used in the majority of cell phones sold worldwide.

Forecasts should also avoid vague language and use numbers for increased precision. Vague words such as could, might, or likely are common in forecasting, but research shows that people attach different meanings to words like these. Forecasters should therefore talk about chance as accurately as possible, by using percentages, for instance. Consider how American intelligence organizations like the NSA and the CIA claimed that Saddam Hussein was hiding weapons of mass destruction, an allegation that turned out, catastrophically, to be untrue.

Had these intelligence agencies calculated with precision and used percentages, the United States might never have invaded Iraq. If they had calculated the chance of Iraq having WMDs at 60%, there would still be a 40% chance that Saddam had none—a rather shaky justification for going to war. Chapter 3. Keep score if you want to improve the accuracy of your forecasts.