Simple linear models, including improper ones(!!). In Chapter 21 of Thinking Fast and Slow, Kahneman writes about Meehl’s book Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review, which finds that simple algorithms made by getting some factors related to the final judgement and weighting them gives you surprisingly good results.
The number of studies reporting comparisons of clinical and statistical predictions has increased to roughly two hundred, but the score in the contest between humans and algorithms has not changed. About 60% of the studies have shown significantly better accuracy for the algorithms. The other comparisons scored a draw in accuracy [...]
If they are weighted optimally to predict the training set, they’re called proper linear models, and otherwise they’re called improper linear models. Kahneman says about Dawes’ The Robust Beauty of Improper Linear Models in Decision Making that
A formula that combines these predictors with equal weights is likely to be just as accurate in predicting new cases as the multiple-regression formula that was ptimal in the original sample. More recent research went further: formulas that assign equal weights to all the predictors are often superior, because they are not affected by accidents of sampling.
That is to say: to evaluate something, you can get very far just by coming up with a set of criteria that positively correlate with the overall result and with each other and then literally just adding them together.
Simple linear models, including improper ones(!!). In Chapter 21 of Thinking Fast and Slow, Kahneman writes about Meehl’s book Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review, which finds that simple algorithms made by getting some factors related to the final judgement and weighting them gives you surprisingly good results.
If they are weighted optimally to predict the training set, they’re called proper linear models, and otherwise they’re called improper linear models. Kahneman says about Dawes’ The Robust Beauty of Improper Linear Models in Decision Making that
That is to say: to evaluate something, you can get very far just by coming up with a set of criteria that positively correlate with the overall result and with each other and then literally just adding them together.