Crude Linear Models Almost Always Outperform Human Judgment

Fascinating article – from 1979 – on how even a crudely-constructed linear model is almost always superior to human expert judgment on classification tasks:

Robyn M. Dawes, “The Robust Beauty of Improper Linear Models in Decision Making”

Is there still any role for people in decisionmaking?

But people are important. The statistical model may integrate the information in an optimal manner, but it is always the individual (judge, clinician, subjects) who chooses variables. Moreover, it is the human judge who knows the directional relationship between the predictor variables and the criterion of interest, or who can code the variables in such a way that they have clear directional relationships.

Now consider how good machine learning and AI algorithms have become at these parts of the task! Soon it might become optimal to delegate the whole process to machines.

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