EPFL's Predikon: predicting voting results with machine learning
On September 27 Switzerland votes for the first time since the COVID-19 pandemic began, including on a contentious initiative to end the free movement of workers with the European Union. Predikon will be predicting the final outcome within minutes of the release of the first partial municipal results from the Swiss Federal Statistical Office. In the past half-decade, many pre-vote polls and initial vote counting around the world have turned out to be unreliable. Perhaps the two most notorious recent examples are the Brexit 'yes' vote in the UK and the election of Donald Trump as president of the United States. In both cases, not only were the majority of pre-vote polls wrong but many of us went to bed with initial counting showing that the UK would remain in the EU and that Hillary Clinton would be the 45th U.S. president. The next morning's results were confounding. For the past six years a group of researchers at EPFL's Information and Network Dynamics Lab ( INDY ), part of the School of Computer and Communication Sciences , have been using probabilistic modelling, large-scale data analytics and machine learning to develop Predikon , in a bid to better predict final election and referendum results from partial, early ballot counts.
