How ML won the US Presidential Elections
2008 US Presidential Elections were won by the insertion of technology into the campaign (first into the Democratic primary elections and afterwards in the Presidential ones). The effect of Twitter and many other social media tools were exploited by the winner to reach all potential voters. A good communication policy and effectiveness when using these powerful massive tools did the rest.
2012 elections went well further into this technology exploitation. A Time Magazine story, published yesterday, details how every detail was taken into account when selectively choosing a celebrity to host an event, or detecting people’s mood about nearly anything in their lives. One can even say the candidate could actually hear everything we said outloud to be heard. It scares me, I have to admit. But it’s also the extreme position where the politician actually listens to the people.
I’ll talk in several posts about Big Data later on, but for now, if you haven’t heard about it, it’s time you know something about it. The term Big Data changes with time and computational capabilities, but let’s say it’s the use of massive information data in machine learning as the way to solve a problem. Algorithms have to be changed, and also the resources needed for this task, but the efficient use of this information provided Obama’s team a real-time map of the present state of the country in nearly any matter. Then, they could even obtain this map for a certain demographic, geographic or social division. Can you imagine the potential of it?
The fact that even last day polls failed almost completely to predict the final result (in terms of the number of representatives which finally lead to the election of a president). The image displayed in this post was taken from a well-known poll aggregator, RCP, the day before the elections (you can click here to go to the actual page). The final result provided a much wider advantage for the Democrats.
The question is, why? I can only guess, but the first reason that comes to mind is that all polls are somehow biased by the polling company owner interests, or by the primary client (usually a party or one of its ‘white brands’). A more technical approach to the problem is that people sometimes lie in these situations, therefore affecting final accuracy. But, if you ask me seriously what happened here, I would answer the polls didn’t reflect what people thought at that moment, only what they answered when they were asked about politics.
On the other hand, I am not saying regular polls don’t have information. Nate Silver has shown consecutively (2008 and 2012) that, by using polling history (and individual polling past accuracy), one can predict the final result almost certainly. In fact, he succeeded in 49/50 states analysed in 2008 (the so-called toss-up states), and in 50/50 states this week (Florida hasn’t finished vote count, but up to now – 98% votes counted – it’s also a success for Mr. Silver). It’s not that polls are entirely wrong, is it?
The conclusion here is not about politics. It is about machine learning, and how can be used in a real problem, in a very complex environment such as an entire society. And how to succeed when used correctly, competently.
Be careful what you wish for because it might come true