Abstract
In this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours. We look at popular vote shares for the Conservative and Labour parties in the UK and the Republican and Democrat parties in the US. We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a mean absolute error of 4.74%. As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the proportion of voters that are strong supporters of each of the considered parties.
Highlights
Modern democratic societies have been polling populations to try and track the popularity of elections candidates and members of governments
Note that because of the twoparty system in place in the United States, both Republicans and Democrats see their share of popular votes fluctuate around the 50% mark
Conclusion and future work In this paper we proposed a new method for the forecast of elections results
Summary
Modern democratic societies have been polling populations to try and track the popularity of elections candidates and members of governments Those are often conducted by means of phone, online or even in person surveys, which can be very time-consuming and usually suffer from limited sample sizes and bias—e.g. respondants with controversial views might be reluctant of sharing them. The quality of predictions spans a rather wide range and numerous voices have expressed concerns over these methods, arguing that there are multiple factors at play that may alter their reliability (Gayo-Avello 2012; Jungherr et al 2017) This is why in this work we propose a novel method that does not rely on data analysis but rather uses the authentic and official results of previous elections to perform estimation for future ones
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
