“Thinking Fast and Slow” by Daniel Kahneman: Part Two

Second installment of my notes from this book. There are a lot of notes but it really goes to show how influential it has been on my other readings. Here is Part One in case you missed it.

  1. However, policy is ultimately about people, what they want and what is best for them. Every policy question involves assumptions about human nature, in particular about the choices that people may make and the consequences of their choices for themselves and for society.
  2. The logic of how people should change their mind in the light of evidence. Bayes’ rule specifies how prior beliefs should be combined with the diagnosticity of the evidence, the degree to which it favors the hypothesis over the alternative.
  3. The most coherent stories are not necessarily the most probable, but they are plausible, and the notions of coherence, plausibility, and probability are easily confused by the unwary. Adding detail to the scenarios makes them more persuasive, but less likely to come true.
  4. It is useful to remember, however, that neglecting valid stereotypes inevitably results in suboptimal judgments.
  5. I had stumbled onto a significant fact of the human condition: the feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.
  6. If the topic of regression comes up in a criminal or civil trial, the side that must explain regression to the jury will lose the case. Why is it so hard? The main reason for the difficulty is a recurrent theme of this book: our mind is strongly biased toward causal explanations and does not deal well with “mere statistics.”
  7. This is perhaps the best evidence we have for the role of substitution. People are asked for a prediction by they substitute an evaluation of the evidence, without noticing that the question they answer is not the one they were asked.
  8. Taleb introduced the notion of a narrative fallacy to describe how flawed stories of the past shape our views of the world and our expectations for the future. Narrative fallacies arise inevitably from our continuous attempt to make sense of the world.
  9. Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.
  10. This outcome bias makes it almost impossible to evaluate a decision properly- in terms of the beliefs that were reasonable when the decision was made.
  11. Because adherence to standard operating procedures is difficult to second-guess, decision makers who expect to have their decisions scrutinized with hindsight are driven to bureaucratic solutions- and to an extreme reluctance to take risks.
  12. The Sense-making machinery of System 1 makes us see the world as more tidy, simple, predictable, and coherent than it really is. The illusion that one has understood the past feeds the further illusion that once can predict and control the future.
  13. The diagnostic for the existence of any skill is the consistency of individual differences in achievement. The logic is simple: if individual difference in any one year are due entirely to luck, the ranking of investors and funds will vary erratically and the year-to-year correlation will be zero. Where there is a skill, however, the rankings will be more stable.
  14. Facts that challenge such basic assumptions- and thereby threaten people’s livelihood and self-esteem- are simply not absorbed. The mind does not digest them. This is particularly true of statistical studies of performance, which provide base-rate information that people generally ignore when it clashes with their personal impressions from experience.
  15. Our tendency to construct and believe coherent narratives of the past makes it difficult for us to accept the limits of our forecasting ability. Everything makes sense in hindsight, a fact that financial pundits exploit every evening as they offer convincing accounts of the day’s events.
  16. Those who know more forecast very slightly better than those who know less. But those with the most knowledge are often less reliable. The reason is that the person who acquires more knowledge develops an enhanced illusion of her skill and becomes unrealistically confident.
  17. Hedgehogs “know one big thing” and have a theory about the world; they account for particular events within a coherent framework, bristle with impatience toward those who don’t see things their way, and are confident in their forecasts. They are also especially reluctant to admit error. For hedgehogs, a failed prediction is almost always “off only on timing” or “very nearly right”. They are opinionated and clear, which is exactly what television producers love to see on programs. Two hedgehogs on different sides of an issue, each attacking the idiotic ideas of the adversary, make for a good show.
  18. Foxes, by contrast, are complex thinkers. They don’t believe that one big thing drives the march of history (for example, they are unlikely to accept the view that Ronald Reagan single-handedly ended the cold war by standing tall against the Soviet Union). Instead the foxes recognize that reality emerges from the interactions of many different agents and forces, including blind luck, often producing large and unpredictable outcomes.
  19. Why are experts inferior to algorithms? One reason, which Meehl suspected, is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity. Simple combinations of features are better. Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula.
  20. Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent in making summary judgments of complex information. When asked to evaluate the same information twice, they frequently give different answers.
  21. Because you have little direct knowledge of what goes on in your mind, you will never know that you might have made a different decision under very slightly different circumstances. Formulas do not suffer from such problems. Given the same input, they always return the same answer. When predictability is poor- which is in most of the studies reviews by Meehl and his followers- inconsistency is destructive of any predictive validity.
  22. Complex statistical algorithm adds little or no value. One can do just as well by selecting a set of scores that have some validity for predicting the outcome and adjusting the values to make them comparable (by using standard scores or ranks). A formula that combines these predictors with equal weights is likely to be just as accurate in predicting new cases as the multiple-regression formula that was optimal in the original sample. More recent research went further: formulas that assign equal weights to all the predictors are often superior, because they are not affected by accidents of sampling.
  23. Their rational argument is compelling, but it runs against a stubborn psychological reality: for most people, the cause of the mistake matters. The story of a child dying because an algorithm made a mistake is more poignant than the story of the same tragedy occurring as a result of human error, and the different in emotional intensity is readily translated into a moral preference.
  24. We are increasingly exposed to guidelines that have the form of simple algorithms, such as the ratio of good and bad cholesterol levels we should strive to attain. The public is now well aware that formulas may do better than humans in some critical decision the world of sports.
  25. The acquisition of expertise in complex tasks such as high-level chess, professional basketball, or firefighting is intricate and slow because expertise in a domain is not a single skill but rather a large collection of miniskills.
  26. When do they display an illusion of validity? The answer comes from the two basic conditions for acquiring a skill: 1) an environment that is sufficiently regular to be predictable. 2) An opportunity to learn these regularities through prolonged practice. When both of these conditions are satisfied, intuitions are likely to be skilled.
  27. In a less regular, or low validity, environment, the heuristics of judgment are invoked. System 1 is often able to produce quick answers to difficult questions by substitution, creating coherence where there is none. The question that is answered is not the one that was intended by the answer is produced quickly and may be sufficiently plausible to pass the lax and lenient review of System 2.
  28. The optimism of planners and decision makers is not the only cause of overruns. Contractors of kitchen renovations and of weapon systems readily admit (though not to their clients) that they routinely make most of their profit on additions to the original plan. The failures of forecasting in these cases reflect the customers’ inability to imagine how much their wishes will escalate over time. They end up paying more than they would if they had made a realistic plan and stuck to it.
  29. Most of us view the world as more benign than it really is, our own attributes as more favorable than they truly are, and the goals we adopt as more achievable than they are likely to be.
  30. I have yet to meet a successful scientist who lacks the ability to exaggerate the importance of what he or she is doing, and I believe that someone who lacks a delusional sense of significance will wilt in the face of repeated experiences of multiple small failures and rare successes, the fate of most researchers.

Thinking, Fast and Slow

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