Algorithm Aversion: On Models and The Donald

Whatever your politics, one can’t help but be fascinated by the Trump phenomenon. One interesting aspect is how pollsters got Trump wrong. We can get insight into that mistake from Nate Silver at fivethirtyeight. To his immense credit, Silver admits he got Trump all wrong, and went back to have a look at the why and the how…. it boils down to a case of algorithmic aversion (see here and here).

Silver’s approach to forecasting, which has been extremely successful across a wide range of subjects, is based on aggregating data from multiple polls to build as big a sample as possible. It is a mechanical, quantitative, model based, statistical method. The model spits out an answer, and that is forecast. But in the Trump case, he over-rode the model. He had a bias, and in this instance he applied it to his forecast, and voila, a bad outcome. Even Silver, a devoted algorithmist if there ever was one, fell into the trap. He did not trust the model, even though it had been right so many times before.

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