Noise: A Flaw in Human Judgment

by Daniel Kahneman

Speaking of Rules and Algorithms “When there is a lot of data, machine-learning algorithms will do better than humans and better than simple models. But even the simplest rules and algorithms have big advantages over human judges: they are free of noise, and they do not attempt to apply complex, usually invalid insights about the predictors.” “Since we lack data about the outcome we must predict, why don’t we use an equal-weight model? It will do almost as well as a proper model, and will surely do better than case-by-case human judgment.” “You disagree with the model’s forecast. I get it. But is there a broken leg here, or do you just dislike the prediction?” “The algorithm makes mistakes, of course. But if human judges make even more mistakes, whom should we trust?”