Tuesday, 7 June 2016

"A new forecasting method for the Brexit referendum"

The following blog has been published first at the Oxford University Politics Blog. Here is just the first part explaining the logic behind our BAFS method. 

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

As the Brexit referendum date approaches, the uncertainty regarding its outcome is increasing. And, so are concerns about the precision of the polls. The forecasts are, once again, suggesting a very close result. Ever since the general election of May 2015, criticism against pollsters has been rampant. They have been accused of complacency, herding, of making sampling errors, and even of deliberate manipulation of their results

The UK is hardly the only country where pollsters are swiftly losing their reputation. 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 (the young, the better educated, the urban population, etc.). 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’[1]), 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, as game theory teaches us, 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 Facebook Survey, or BAFS for short. The BAFS method is first and foremost an Internet poll. It uses the social networks between friends on Facebook to conduct a survey among them. The survey asks the participants to express: 1) their vote preference (e.g. Leave or Remain); 2) how much do they think their preferred choice will get (in percentages); and 3) how likely they think other people will estimate that Leave or Remain will win the day.

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 the 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 – even Murr’s (2016) citizen forecasters  (although in relative terms the citizen forecaster model was the most precise). 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), BAFS formulates their predictive power and gives a higher weight to the better predictors (e.g. if the polls are predicting a 52%-48% outcome, a person estimating that one choice will get, say, 80% 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.



[1] See Murr, A.E. (2016) “The wisdom of crowds: What do citizens forecast for the 2015 British General Election?” Electoral Studies 41 (2016) 283-288.

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