Why we can't use machine learning to predict presidential elections

Off The Broiler

  The UNIVAC 1 is a machine with important historical significance to the computer industry because it was the first mass-produced computer to be sold to corporations. It's also the computer that has the distinction of being the one that Walter Cronkite used to correctly predict the outcome of the 1952 Presidential Election. 

By Chris Wilson/

Slate ran an article outlining why you can’t build a model to predict presidential elections. The logic there still holds: You can predict any outcome you like depending which data you choose to use. One of the most powerful aspects of the algorithms we studied in this class was their ability to compute tremendous amounts of data and scale it down to the most important factors. Researchers also have ways of preventing their models from “overfitting” the data. In other words, one could probably create an algorithm that predicted past elections with great accuracy, but it might be so finely tuned to the quirks and turns of that historical data that it would be meaningless in 2012. It may seem preposterous to think that any mountainous pile of polls, unemployment rates, approval ratings, the stock markets, and a million other factors could, if correctly parsed by a smart machine, predict human behavior. But by and large, people behave rationally, and machines, if we learned anything, can model rational systems very well. And they’re getting better every day.  

Wired.com

Añadir leyendaRemington Rand's Univac computer was big and expensive. But it built its reputation quickly as a predictor of presidential elections.
Photo: U.S. Army

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