# Noise  ## Metadata - Author:: [[Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein]] - Full Title:: Noise - Category: #books ## Highlights > Bias and noise—systematic deviation and random scatter—are different components of error. The targets illustrate the difference. ([Location 89](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=89)) > A general property of noise is that you can recognize and measure it while knowing nothing about the target or bias. ([Location 98](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=98)) > To understand error in judgment, we must understand both bias and noise. ([Location 106](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=106)) > Wherever you look at human judgments, you are likely to find noise. To improve the quality of our judgments, we need to overcome noise as well as bias. ([Location 137](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=137)) > Occasion noise is the variability in judgments of the same case by the same person or group on different occasions. A surprising amount of occasion noise arises in group discussion because of seemingly irrelevant factors, such as who speaks first. ([Location 145](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=145)) > wherever there is judgment, there is noise—and more of it than you think. ([Location 194](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=194)) > The price of reducing noise was to make decisions unacceptably mechanical. ([Location 298](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=298)) > For any risk, there is a Goldilocks price that is just right— ([Location 353](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=353)) > Judgment can therefore be described as measurement in which the instrument is a human mind. ([Location 568](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=568)) > People who are affected by evaluative judgments expect the values these judgments reflect to be those of the system, not of the individual judges. ([Location 755](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=755)) > System noise is inconsistency, and inconsistency damages the credibility of the system. ([Location 758](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=758)) > The different errors add up; they do not cancel out. ([Location 779](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=779)) > a goal that is in principle as important as the reduction of statistical bias. (We should emphasize that statistical bias is not a synonym for social discrimination; it is simply the average error in a set of judgments.) ([Location 928](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=928)) > And this result certainly provides a rationale for the age-old advice to decision makers: “Sleep on it, and think again in the morning.” ([Location 1181](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1181)) > gambler’s fallacy: we tend to underestimate the likelihood that streaks will occur by chance. ([Location 1275](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1275)) > Or to put it differently, you are not always the same person, and you are less consistent over time than you think. But somewhat reassuringly, you are more similar to yourself yesterday than you are to another person today. ([Location 1286](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1286)) > There are “wise crowds,” whose mean judgment is close to the correct answer, but there are also crowds that follow tyrants, that fuel market bubbles, that believe in magic, or that are under the sway of a shared illusion. ([Location 1328](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1328)) > Under favorable circumstances, in which people share what they know, deliberating groups can indeed do well. But independence is a prerequisite for the wisdom of crowds. If people are not making their own judgments and are relying instead on what other people think, crowds might not be so wise after all. ([Location 1401](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1401)) > To use Dawes’s phrase, which has become a meme among students of judgment, there is a “robust beauty” in equal weights. ([Location 1791](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1791)) > If you have decisive information that the model could not take into consideration, there is a true broken leg, ([Location 1829](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1829)) > Because of this intuitive expectation, however, people are likely to distrust algorithms and keep using their judgment, even when this choice produces demonstrably inferior results. ([Location 1920](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1920)) > The internal signal is a self-administered reward, one people work hard (or sometimes not so hard) to achieve when they reach closure on a judgment. ([Location 1946](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1946)) > Both intractable uncertainty (what cannot possibly be known) and imperfect information (what could be known but isn’t) make perfect prediction impossible. ([Location 1976](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1976)) > Overconfidence is one of the best-documented cognitive biases. ([Location 1983](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1983)) > “The average expert was roughly as accurate as a dart-throwing chimpanzee.” ([Location 1993](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1993)) > experts who make a living “commenting or offering advice on political and economic trends” were not “better than journalists or attentive readers of the New York Times in ‘reading’ emerging situations.” ([Location 1994](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1994)) > Pundits blessed with clear theories about how the world works were the most confident and the least accurate. ([Location 2002](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2002)) > The team discovered that short-term forecasting is difficult but not impossible, and that some people, whom Tetlock and Mellers called superforecasters, are consistently better at it than most others, including professionals in the intelligence community. ([Location 2011](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2011)) > “significant” may be the worst example of this. When a finding is described as “significant,” we should not conclude that the effect it describes is a strong one. It simply means that the finding is unlikely to be the product of chance alone. ([Location 2137](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2137)) > Causal thinking helps us make sense of a world that is far less predictable than we think. It also explains why we view the world as far more predictable than it really is. ([Location 2237](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2237)) # Noise  ## Metadata - Author:: [[Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein]] - Full Title:: Noise - Category: #books ## Highlights > Bias and noise—systematic deviation and random scatter—are different components of error. The targets illustrate the difference. ([Location 89](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=89)) > A general property of noise is that you can recognize and measure it while knowing nothing about the target or bias. ([Location 98](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=98)) > To understand error in judgment, we must understand both bias and noise. ([Location 106](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=106)) > Wherever you look at human judgments, you are likely to find noise. To improve the quality of our judgments, we need to overcome noise as well as bias. ([Location 137](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=137)) > Occasion noise is the variability in judgments of the same case by the same person or group on different occasions. A surprising amount of occasion noise arises in group discussion because of seemingly irrelevant factors, such as who speaks first. ([Location 145](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=145)) > wherever there is judgment, there is noise—and more of it than you think. ([Location 194](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=194)) > The price of reducing noise was to make decisions unacceptably mechanical. ([Location 298](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=298)) > For any risk, there is a Goldilocks price that is just right— ([Location 353](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=353)) > Judgment can therefore be described as measurement in which the instrument is a human mind. ([Location 568](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=568)) > People who are affected by evaluative judgments expect the values these judgments reflect to be those of the system, not of the individual judges. ([Location 755](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=755)) > System noise is inconsistency, and inconsistency damages the credibility of the system. ([Location 758](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=758)) > The different errors add up; they do not cancel out. ([Location 779](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=779)) > a goal that is in principle as important as the reduction of statistical bias. (We should emphasize that statistical bias is not a synonym for social discrimination; it is simply the average error in a set of judgments.) ([Location 928](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=928)) > And this result certainly provides a rationale for the age-old advice to decision makers: “Sleep on it, and think again in the morning.” ([Location 1181](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1181)) > gambler’s fallacy: we tend to underestimate the likelihood that streaks will occur by chance. ([Location 1275](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1275)) > Or to put it differently, you are not always the same person, and you are less consistent over time than you think. But somewhat reassuringly, you are more similar to yourself yesterday than you are to another person today. ([Location 1286](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1286)) > There are “wise crowds,” whose mean judgment is close to the correct answer, but there are also crowds that follow tyrants, that fuel market bubbles, that believe in magic, or that are under the sway of a shared illusion. ([Location 1328](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1328)) > Under favorable circumstances, in which people share what they know, deliberating groups can indeed do well. But independence is a prerequisite for the wisdom of crowds. If people are not making their own judgments and are relying instead on what other people think, crowds might not be so wise after all. ([Location 1401](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1401)) > To use Dawes’s phrase, which has become a meme among students of judgment, there is a “robust beauty” in equal weights. ([Location 1791](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1791)) > If you have decisive information that the model could not take into consideration, there is a true broken leg, ([Location 1829](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1829)) > Because of this intuitive expectation, however, people are likely to distrust algorithms and keep using their judgment, even when this choice produces demonstrably inferior results. ([Location 1920](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1920)) > The internal signal is a self-administered reward, one people work hard (or sometimes not so hard) to achieve when they reach closure on a judgment. ([Location 1946](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1946)) > Both intractable uncertainty (what cannot possibly be known) and imperfect information (what could be known but isn’t) make perfect prediction impossible. ([Location 1976](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1976)) > Overconfidence is one of the best-documented cognitive biases. ([Location 1983](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1983)) > “The average expert was roughly as accurate as a dart-throwing chimpanzee.” ([Location 1993](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1993)) > experts who make a living “commenting or offering advice on political and economic trends” were not “better than journalists or attentive readers of the New York Times in ‘reading’ emerging situations.” ([Location 1994](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=1994)) > Pundits blessed with clear theories about how the world works were the most confident and the least accurate. ([Location 2002](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2002)) > The team discovered that short-term forecasting is difficult but not impossible, and that some people, whom Tetlock and Mellers called superforecasters, are consistently better at it than most others, including professionals in the intelligence community. ([Location 2011](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2011)) > “significant” may be the worst example of this. When a finding is described as “significant,” we should not conclude that the effect it describes is a strong one. It simply means that the finding is unlikely to be the product of chance alone. ([Location 2137](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2137)) > Causal thinking helps us make sense of a world that is far less predictable than we think. It also explains why we view the world as far more predictable than it really is. ([Location 2237](https://readwise.io/to_kindle?action=open&asin=B08KQ2FKBX&location=2237))