Summary of Noise

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Noise book summary

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Professor Daniel Kahneman of Thinking, Fast and Slow brings his expertise in decision-making to bear on the phenomenon of noise. When you use your judgment to make evaluations or predictions, you are liable to make errors, without knowing how or why. For instance, people mistakenly believe that errors “cancel each other out” but they don’t. They add up. Examining medicine, the judicial system and insurance, Kahneman and co-authors Olivier Sibony and Cass R. Sunstein expose egregious, undetected errors that a “noise audit” could have diagnosed and avoided. By managing noise, they assert, you can solve problems instead of creating new ones. 

About the Authors

Princeton emeritus professor and 2002 Economic Sciences Nobelist Daniel Kahneman wrote Thinking, Fast and Slow. Former McKinsey senior partner Olivier Sibony teaches strategy at HEC Paris and Saïd Business School, Oxford, and wrote You’re About to Make a Terrible Mistake! Bestsellers by Cass R. Sunstein – Department of Homeland Security senior counselor in the Biden administration and Harvard professor – include How Change Happens, and Nudge, co-authored with Richard Thaler.

Summary

Physicians, judges, investors and many other professionals show a strikingly high level of disagreement in separate judgments of the same cases.

The human mind is a “measuring instrument,” and judgments are the measurements. Therefore, a judgment is a conclusion, not an argument. Making a good judgment is not the same as having good overall judgment. Judgment aims at determining “true value,” which is different for each person. Considerations about judgment include the expectation that people will experience “bounded disagreement.” After all, human beings are fallible – a reflection, in part, of how much judgment varies from person to person.

Judgments fall into two categories. Inconsistency is problematic in both of them, but for different reasons:

  1. Predictive judgment – Forecasters judge outcomes on the basis of probabilities. When two doctors or two weather forecasters come to vastly different conclusions using the same data, that indicates noise. Measuring the accuracy of predictive judgments after the fact is almost impossible, especially if the predictions are conditional...

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