Thinking, Fast and Slow

by Daniel Kahneman

The biases we find in predictions that are expressed on a scale, such as GPA or the revenue of a firm, are similar to the biases observed in judging the probabilities of outcomes. The corrective procedures are also similar: Both contain a baseline prediction, which you would make if you knew nothing about the case at hand. In the categorical case, it was the base rate. In the numerical case, it is the average outcome in the relevant category. Both contain an intuitive prediction, which expresses the number that comes to your mind, whether it is a probability or a GPA. In both cases, you aim for a prediction that is intermediate between the baseline and your intuitive response. In the default case of no useful evidence, you stay with the baseline. At the other extreme, you also stay with your initial prediction. This will happen, of course, only if you remain completely confident in your initial prediction after a critical review of the evidence that supports it. In most cases you will find some reason to doubt that the correlation between your intuitive judgment and the truth is perfect, and you will end up somewhere between the two poles.