“Noise” by Daniel Kahneman, Olivier Sibony, Cass R Sunstein, Jonathan Todd Ross

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  • Bias (variance from the target) and noise (variance from others) appear is most organizations
  • System noise compounds rather than balance (cancel) out
  • Most people are content with maintaining a single world view
  • Focusing on common language allows us to avoid developing common standards
    • Conflict avoidance helps support this
  • (Feedback is really important to making consistent decisions)
  • Verifiability does not affect our estimates
    • (Just because something can be verified does not mean we are better at estimating it)
  • Decisions require predictive and evaluative judgements
  • Bias and noise are independent errors
  • Avoid mixing your values and facts
  • Waiting a few weeks to make a second guess helps improve personal guess
  • To get most accurate average estimates
    • Get input from other people
    • Ask yourself a second time, weeks later
    • Ask yourself to guess again, assuming your first guess is wrong
  • Summing up votes can have a huge boost in accuracy
  • Crowds are only wise if people register views independently from each other
  • Discussing ideas with others often intensifies original beliefs
  • “We do not need more accurate weights than our measures.”
  • “Frugal rules” (algorithms with few inputs) are often better than complex algorithms but not as good as ML
  • People are often willing to trust an algorithm until it makes a bad decision
  • Most things happen in “the Valley of the Norm” where it is easy to explain after the fact
  • Using comparisons instead of labels for more accurate judgements at scale
    • 7 is the magical number of categories
  • We like casual stories of explanation, even if they are not rational
  • Intelligence has a strong correlation with financial success
  • Open minded is better than strong confidence
  • Documenting each step helps to reduce bias due to information exposure

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