There is a tendency in our planning to confuse the unfamiliar with the improbable. The contingency we have not considered seriously looks strange; what looks strange is thought improbable; what is improbable need not be considered seriously.
Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.
The forecasters later told researchers that they were afraid the public might lose confidence in the forecast if they had conveyed any uncertainty in the outlook.
The key to making a good forecast, as we observed in chapter 2, is not in limiting yourself to quantitative information. Rather, it’s having a good process for weighing the information appropriately.
A forecaster who says he doesn’t care about the science is like the cook who says he doesn’t care about food.
The best model of a cat is a cat.”91 Everything else is leaving out some sort of detail.
Technology is beneficial as a labor-saving device, but we should not expect machines to do our thinking for us.
In complex systems, however, mistakes are not measured in degrees but in whole orders of magnitude.
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.
What makes them so tragic is the gap between what his characters might like to accomplish and what fate provides to them. The idea of controlling one’s fate seemed to have become part of the human consciousness by Shakespeare’s time – but not yet the competencies to achieve that end.
The fundamental dilemma faced by climatologists is that global warming is a long-term problem that might require a near-term solution.
I lived the poker dream for a while, and then it died. I learned that poker sits at the muddy confluence of the signal and the noise. My years in the game taught me a great deal about the role that chance plays in our lives and the delusions it can produce when we seek to understand the world and predict its course.
The way to become more objective is to recognize the influence that our assumptions play in our forecasts and to question ourselves about them.
Scientific progress is harder to measure than economic progress.32 But one mark of it is the number of patents produced, especially relative to the investment in research and development. If it has become cheaper to produce a new invention, this suggests that we are using our information wisely and are forging it into knowledge.
Good innovators typically think very big and they think very small. New ideas are sometimes found in the most granular details of a problem where few others bother to look.
In 2011, he said that Donald Trump would run for the Republican nomination – and had a “damn good” chance of winning it.19.
The Weather Service was initially organized under the Department of War by President Ulysses S. Grant, who authorized it in 1870. This was partly because President Grant was convinced that only a culture of military discipline could produce the requisite accuracy in forecasting25 and partly because the whole enterprise was so hopeless that it was only worth bothering with during wartime when you would try almost anything to get an edge.
Most of our strengths and weaknesses as a nation – our ingenuity and our industriousness, our arrogance and our impatience – stem from our unshakable belief in the idea that we choose our own course.
The Industrial Revolution largely began in Protestant countries and largely in those with a free press, where both religious and scientific ideas could flow without fear of censorship.25.
This is an example of an out-of-sample problem. As easy as it might seem to avoid this sort of problem, the ratings agencies made just this mistake. Moody’s estimated the extent to which mortgage defaults were correlated with one another by building a model from past data – specifically, they looked at American housing data going back to about the 1980s.
In any contentious debate, some people will find it advantageous to align themselves with the crowd, while a smaller number will come to see themselves as persecuted outsiders. This may especially hold in a field like climate science, where the data is noisy and the predictions are hard to experience in a visceral way. And it may be especially common in the United States, which is admirably independent-minded.