“The Signal and the Noise: by Nate Silver

Another great book. Sometimes people are so easily influenced by the information in front of them and often take that information at face value without any further thought. I personally have to challenge myself to me skeptical of such information. The saying, “Trust, but verify” comes to mind.

  1. We can never make perfectly objective predictions. They will always be tainted by our subjective point of view.
  2. A belief in the objective truth—and a commitment to pursing it is the first prerequisite of making better predictions. The forecaster’s next commitment is to realize that she perceives it so imperfectly.
  3. Baye’s theorem is nominally a mathematical formula. But it is really much more than that. It implies that we must think differently about our ideas and how to test them. We must become more comfortable with probability and uncertainty. We must think more carefully about the assumptions and beliefs that we bring to a problem.
  4. We perceive it selectively, subjectively, and without much self-regard for the distortions that this causes. We think we want information when we really want knowledge.
  5. The most calamitous failures of prediction usually have a lot in common. We focus on signals that tell a story about the world as we would like it to be, not how it really is. We ignore the roles that are hardest to measure, even when they pose the greatest threats to our well being. We make approximations and assumptions about the world that are much cruder than we realize. We abhor uncertainty, even when it is an irreducible part of the problem we are trying to solve.
  6. If you’re in a market and someone’s trying to sell you something you don’t understand, you should think that they are selling you a lemon.
  7. Financial crises—and most other failures of prediction—stem from this false sense of confidence. Precise forecasts masquerade as accurate ones, and some of us get fooled and double-down our bets.
  8. “The fox knows many little things, but the hedgehog knows one big thing.”
  9. Hedgehogs are Type A personalities who believe in big ideas—in governing principles about the world that behave as though they were physical laws and undergird virtually all interactions in society. Think Karl Marx and class struggle, or Sigmund Freud and the unconscious, or Malcolm Gladwell and “the tipping point.”
  10. Foxes, on the other hand, are scrappy creatures who believe in a plethora of little ideas and in taking a multitude of approaches toward a problem. They tend to be more tolerant of nuance, uncertainty, complexity, and dissenting opinion. If hedgehogs are hunters, always looking out for the big kill, foxes are gatherers.
  11. If you have a reason to think that yesterday’s forecast was wrong, there is no glory in sticking to it. “When the facts change, I change my mind,” John Maynard Keynes famously said, “What do you do, sir?”
  12. Wherever there is human judgment, there is the potential for bias.
  13. It’s hard to have an idea that nobody else has thought of. It’s even harder to have a good idea—and when you do, it will soon be duplicated.
  14. 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. And they are sometimes found when you are doing your most abstract and philosophical thinking, considering why they world is the way that it is and whether there might be an alternative to the dominant paradigm.
  15. Laplace’s Demon: We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula, the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future, just like the past, would be present before its eyes.
  16. Probabalism was, at first, mostly an epistemological paradigm; it avowed that there were limits on man’s ability to come to grips with the universe.
  17. Perfect predictions are impossible if the universe itself is random.
  18. When catastrophe strikes, we look for a signal in the noise—anything that might explain the chaos that we see all around us and bring order to the world again.
  19. A prediction is a definitive and specific statement about when and where an earthquake will strike. Whereas a forecast is a probabilistic statement, usually over a long time scale.
  20. When we are evaluating the success of a forecasting method, it is crucial to keep “retrodictions” and predications separate; predicting the past is an oxymoron and obviously should not be counted among successes.
  21. “Correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other.
  22. The observer effect (often mistaken for a related concept, the Heisenberg Uncertainty Principle): once we begin to measure something, its behavior starts to change.
  23. In many cases involving predictions about human activity, the very act of prediction can alter the way people behave.
  24. How there could be suffering and evil in the world if God was truly benevolent. Baye’s answer, in essence, was that we should not mistake our human imperfections for imperfections on the part of God, whose designs for the universe we might not fully understand.
  25. We can think of these simplifications as “models”, but heuristics is the preferred term in the study of computer programming and human decision-making. It comes from the same Greek root word from we derive Eureka. A heuristic approach to problem solving consists of employing rules of thumb when a deterministic solution to a problem is beyond our practical capabilities.
  26. Sometimes the tactical loss is outweighed by the strategic gain.
  27. It can require a lot of extra effort to beat the competition. You will find that you soon encounter diminishing returns. The extra experience that you gain, the further wrinkles that you add to your strategy, and the additional variables that you put into your forecasting model—these will only make a marginal difference.
  28. It is often possible to make a profit by being pretty good at prediction in fields where the competition succumbs to poor incentives, bad habits, or blind adherence to tradition.
  29. Increase their level of self-awareness, encouraging them to develop a better sense for which things are and are not within their control.
  30. Just as in a good poker game, good players need some fish at the table to make the game profitable to play in. in the financial literature, these irrational traders are known as the “noise traders”.
  31. Investors needs to learn how to do exactly the reverse of what their fight-or-flight mechanism is telling them to do. When the market crashes, that is the time to get excited and put your money into it. It’s not the time to get scared and pull money out. What you see instead is the more the market drops, the more money comes out of it. Normal investors are obliterated, because they continuously do exactly the wrong thing.
  32. Agreement among forecasters is not related to accuracy.
  33. The more complex you make the model the worse the forecast gets.
  34. There is almost certainly some value in the idea that different members of a group can learn from one another’s expertise. But this introduces the possibility of groupthink and herding. Some members of a group may be more influential because of their charisma or status and not necessarily because they have the better idea.
  35. There 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 no know we don’t know.
  36. We tend to overrate the likelihood of events that are nearer to us in time and space and underpredict the ones that aren’t.
  37. Whatever range of abilities we have acquired, there will always be tasks sitting right at the edge of them. If we judge ourselves by what is hardest for us, we may take for granted those things that we do easily and routinely.
  38. Our brains process information by means of approximation. This is less an existential fact than a biological necessity; we perceive far more inputs than we can consciously consider, and we handle this problem by breaking them down into regularities and patterns.
  39. Efficient-market hypothesis and whether an individual investor can beat the stock market. Each statement is an approximation, but each build on the last one to become slightly more accurate:
    1. No investor can beat the stock market.
    2. No investor can beat the stock market over the long run.
    3. No investor can beat the stock market over the long run relative to his level of risk.
    4. No investor can beat the stock market over the long run relative to his level of risk and accounting for his transaction costs.
    5. No investor can beat the stock market over the long run relative to his level of risk and accounting for his transaction costs unless he has insider information.
    6. No investor can beat the stock market over the long run relative to his level of risk and accounting for his transaction costs unless he has insider information few investors beat the stock market.
    7. It is hard to tell how many investors beat the stock market over the long run, because the data is very noisy… you are problem better off investing in an index fund.

The Signal and the Noise


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