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A frequent misconception about unwanted variability in judgments is that it doesn’t matter, because random errors supposedly cancel one another out. Certainly, positive and negative errors in a judgment about the same case will tend to cancel one another out, and we will discuss in detail how this property can be used to reduce noise. But noisy systems do not make multiple judgments of the same case. They make noisy judgments of different cases. If one insurance policy is overpriced and another is underpriced, pricing may on average look right, but the insurance company has made two costly errors. If two felons who both should be sentenced to five years in prison receive sentences of three years and seven years, justice has not, on average, been done. In noisy systems, errors do not cancel out. They add up.
Noise
Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein
The "What Stays the Same" Razor
It's difficult to predict the future.
Jeff Bezos famously said that investing in what might change is risky, but investing in what will remain constant is safe.
When building for the future, focus on the constants—focus on what stays the same.
“Razors” Are Rules of Th...
@SahilBloom on Twitter
Cet échange montrait bien que le système de bonus dans son ensemble repose sur un présupposé : que l’on peut prédire l’avenir de façon fiable, donc fixer un objectif qui restera valable à tout moment. Chez Netflix, nous devons pouvoir nous adapter rapidement en réaction à des changements au rythme parfois soutenu, alors quel sens cela aurait-il de voir nos employés récompensés en décembre pour avoir atteint des objectifs fixés au mois de janvier précédent ? Le risque est de rester focalisé sur l’objectif préétabli, au détriment de ce qui serait bon au présent pour l’entreprise.
La Règle? Pas De Règles
Reed Hastings and Erin Meyer
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