There is the average of error, which is bias, and there is the variability of error, and that’s noise. What is it about noise that it was able to capture and hold your attention?ĭaniel Kahneman: In the mathematics of accuracy, there are two types of error which are equivalent.
What noise is and how it differs from biasĮvan Nesterak: At this stage in your career, after all you’ve studied, you could focus on anything you wanted. We covered a lot of ground in our hour-long conversation, which I’ve distilled below and organized in three sections: what noise is and how it differs from bias, how we can measure and deal with noise, and some of noise’s nuances. Kahneman and I had the chance to discuss noise over a Zoom call. And, in that example, it’s not hard to see how noise isn’t simply a decision-making quirk but a feature of the decision-making systems we’ve set up, and one with serious consequences. The case of judicial sentencing is an example that features in the book. The field’s recent attention to bias has overshadowed noise it’s like we’re fighting systemic error with one hand tied behind our back. And that’s because reducing noise in a system can help reduce error, just like reducing bias does. Kahneman and co argue that it’s time we pay more attention to noise. That’s what we’d have if judges are too varied in their sentencing and consistently dole out too harsh of a sentence.
In a biased system, judges might consistently give sentences that are too high for certain types of crimes.
We’d expect similar punishments for the same crime. For instance, if a group of judges gives vastly different sentences to defendants who committed the same crime-some judges give a one-month sentence, others one-year, others seven years, and others somewhere in between-then one could call the system noisy.