Predicting the 2016 US Presidential election

Is it possible to have a more accurate prediction by asking people how confident they are that their preferred choice will win?

One consequence of this hectic election season has been that people have stopped trusting the polls as much as they did before. Which is surprising given that in the US, unlike the rest of Europe, pollsters and particularly polling aggregation sites (like FiveThirtyEight) have on aggregate been quite accurate in their predictions thus far. Still, one cannot escape the overall feeling that pollsters are losing their reputation, as they are often being accused of complacency, sampling errors, and even deliberate manipulations.

There are legitimate reasons for this however. With the rise of online polls, proper sampling can be extremely difficult. Online polls are based on self-selection of the respondents, making them non-random and hence biased towards a particular voter group (young, better educated, urban population, etc.), despite the efforts of those behind these polls to adjust them for various socio-demographic biases. On the other hand, the potential sample for traditional telephone (live interview) polls is in sharp decline, making them less and less reliable. Telephone interviews are usually done during the day biasing the results towards stay-at-home moms, retirees, and the unemployed, while most people, for some reason, do not respond to mobile phone surveys as eagerly as they once did to landline surveys. With all this uncertainty it is hard to gauge which poll(ster) should we trust and to judge the quality of different prediction methods.

However, what if the answer to ‘what is the best prediction method’ lies in asking people not only who they will vote for, but also who they think will win (as ‘citizen forecasters’) and more importantly, how they feel about who other people think will win? Sounds convoluted? It is actually quite simple.

There are a number of scientific methods out there that aim to uncover how people form opinions and make choices. Elections are just one of the many choices people make. When deciding who to vote for, people usually succumb to their standard ideological or otherwise embedded preferences. However, they also carry an internal signal which tells them how much chance their preferred choice has. In other words, they think about how other people will vote. This is why people tend to vote strategically and do not always pick their first choice, but opt for the second or third, only to prevent their least preferred option from winning.

When pollsters make surveys they are only interested in figuring out the present state of the people’s ideological preferences. They have no idea on why someone made the choice they made. And if the polling results are close, the standard saying is: “the undecided will decide the election”. What if we could figure out how the undecided will vote, even if we do not know their ideological preferences?

One such method, focused on uncovering how people think about elections, is the Bayesian Adjusted Social Network (BASON) Survey. The BASON method is first and foremost an Internet poll. It uses the social networks between friends on Facebook and followers and followees on Twitter to conduct a survey among them. The survey asks the participants to express: 1) their vote preference (e.g. Trump or Clinton); 2) how much do they think their preferred candidate will get (in percentages); and 3) how they think other people will estimate that Trump or Clinton will get.

BASON Survey for the 2016 US Presidential elections
(temporary results for states in which predictions have been made by our users)
Let’s clarify the logic behind this. Each individual holds some prior knowledge as to what he or she thinks the final outcome will be. This knowledge can be based on current polls, or drawn from the information held by their friends and people they find more informed about politics. Based on this it is possible to draw upon the wisdom of crowds where one searches for informed individuals thus bypassing the necessity of having to compile a representative sample.

However, what if the crowd is systematically biased? For example, many in the UK believed that the 2015 election would yield a hung parliament. In other words, information from the polls is creating a distorted perception of reality which is returned back to the crowd biasing their internal perception. To overcome this, we need to see how much individuals within the crowd are diverging from the opinion polls, but also from their internal networks of friends.

Depending on how well they estimate the prediction possibilities of their preferred choices (compared to what the polls are saying), the BASON formulates their predictive power and gives a higher weight to the better predictors. For example, if the polls are predicting a 52%-48% outcome in a given state, a person estimating that one candidate will get, say, 90% is given an insignificant weight. Group predictions can be completely wrong of course, as closed groups tend to suffer from confirmation bias. On the aggregate however, there is a way to get the most out of people’s individual opinions, no matter how internally biased they are. The Internet makes all of them easily accessible for these kinds of experiments, even if the sampling is non-random. 

Oraclum is currently conducting the survey across the United States. Forecasts are updated daily with the final one being shown on Election Day. 

So if you think you know politics, and that you do not live in a bubble where everyone around you thinks the same way, log into our app through Facebook or Twitter, give your prediction, and attain bragging rights among your friends on November 8th. Don’t forget to share and remember: if it’s not on Facebook or Twitter, it didn’t happen!


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