The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

As I’ve said, when you gain experience with a difficult task, it can sometimes transition from System 2 to System 1.

the fact that the few theories we can test have produced quite poor results suggests that many of the ideas we haven’t tested are very wrong as well. We are undoubtedly living with many delusions that we do not even realize.

Lorenz realized that it could. The most basic tenet of chaos theory is that a small change in initial conditions—a butterfly flapping its wings in Brazil—can produce a large and unexpected divergence in outcomes—a tornado in Texas. This does not mean that the behavior of the system is random, as the term “chaos” might seem to imply. Nor is chaos th

“All models are wrong, but some models are useful.”90 What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, “The best model of a cat is a cat.”91 Everything else is leaving out some sort of detail. How pertinent that detail might be will depend on…

Some highlights ha

The science of weather forecasting is a success story despite the challenges posed by the intricacies of the weather system. As you’ll find throughout this book, cases like these are more the exception than the rule when it comes to making forecasts.

[T]here are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—there are things we do not know we don’t know.—Donald Rumsfeld

statheads can have their biases too. One of the most pernicious ones is to assume that if something cannot easily be quantified, it does not matter.

Bayes’s theorem says we should update our forecasts any time we are presented with new information. A less literal version of this idea is simply trial and error. Companies that really “get” Big Data, like Google, aren’t spending a lot of time in model land.* They’re running thousands of experiments every year and testing their ideas on real custom

Uncertainty, on the other hand, is risk that is hard to measure. You might have some vague awareness of the demons lurking out there. You might even be acutely concerned about them. But you have no real idea how many of them there are or when they might strike. Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor of 1,0