Vendeville, A;
Guedj, B;
Zhou, S;
(2021)
Forecasting elections results via the voter model with stubborn nodes.
Applied Network Science
, 6
, Article 1.
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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.
| Type: | Article |
|---|---|
| Title: | Forecasting elections results via the voter model with stubborn nodes |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.1007/s41109-020-00342-7 |
| Language: | English |
| Additional information: | © 2021 BioMed Central Ltd. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Elections, Voter model, Opinion dynamics, Markov chains, Social networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10118774 |
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