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

“Our biological instincts are not always very well adapted to the information-rich modern world. Unless we work actively to become aware of the biases we introduce, the returns to additional information may be minimal - or diminishing.”
“Sports and games, because they follow well-defined rules, represent good laboratories for testing our predictive skills. They help us to a better understanding of randomness and uncertainty and provide insight about how we might forge information into knowledge.”
“Leverage, or investments financed by debt.”
“The word objective is sometimes taken to be synonymous with quantitative, but it isn’t. Instead it means seeing beyond our personal biases and prejudices and toward the truth of a problem.”
“Good innovators typically think very big and they think very small.”
“And yet while the notion that aggregate forecasts beat individual ones is an important empirical regularity, it is sometimes used as a cop-out when forecasts might be improved. The aggregate forecast is made up of individual forecasts; if those improve, so will the group’s performance. Moreover, even the aggregate economics forecasts have been quite poor in any real-world sense, so there is plenty of room for progress.”
“The key is in remembering that a model is a tool to help us understand the complexities of the universe, and never a substitute for the universe itself.”
“The bayesian viewpoint, instead, regards rationality as a probabilistic matter. In essence, Bayes and Price are telling Hume, don’t blame nature because you are too daft to understand it: if you step out of your skeptical shell and make some predictions about its behavior, perhaps you will get a little closer to the truth.”
“This does not imply that all prior beliefs are equally correct or equally valid. But I’m of the view that we can never achieve perfect objectivity, rationality, or accuracy in our beliefs. Instead, we can strive to be less subjective, less irrational, and less wrong. Making predictions based on our beliefs is the best (and perhaps even the only) way to test ourselves.”
“Elite chess players tend to be good at metacognition - thinking about the way they think - and correcting themselves if they don’t seem to be striking the right balance.”
“Blitzing your opponent with a deluge of possibilities is the best way to complicate his probability calculations.”
“But if you’re approaching prediction as more of a business proposition, you’re usually better off finding someplace where you can be the big fish in a small pond.”
“Control. When we play poker, we control our decision making process but not how the cards come down. If you correctly detect an opponent’s bluff, but he gets a lucky card and wins the hand anyway, you should be pleased rather than angry, because you played the hand as well as you could. The irony is that by being less focused on your results, you may achieve better ones.”
“The more eagerly we commit to scrutinizing and testing our theories, the more readily we accept that our knowledge of the world is uncertain, the more willingly we acknowledge that perfect prediction is impossible, the less we will live in fear of our failures, and the more liberty we will have to let our minds flow freely. By knowing more about what we don’t know, we may get a few more predictions right.”
“In many walks of life, expressions of uncertainty are mistake for admissions of weakness.”
“Bayes’s theorem requires us to state - explicitly - how likely we believe an event is to occur before we begin to weigh the evidence. It calls this estimate a prior belief.”
“Information becomes knowledge only when it’s placed in context. Without it, we have no way to differentiate the signal from the noise, and our search for the truth might be swamped by false positives.”
“In the real world, they rarely come when you are standing in place. Nor do the “big” ideas necessarily start out that way. It’s more often with small, incremental, and sometimes even accidental steps that we make progress.”