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Forecasting elections results via the voter model with stubborn nodes

Antoine Vendeville 1 Benjamin Guedj 2, 1, 3, 4 Shi Zhou 1
4 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
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 quantity of voters that are strongly pro and against the considered parties.
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https://hal.inria.fr/hal-02946434
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Submitted on : Wednesday, September 23, 2020 - 10:17:29 AM
Last modification on : Thursday, October 1, 2020 - 12:48:09 PM

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  • HAL Id : hal-02946434, version 1
  • ARXIV : 2009.10627

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Antoine Vendeville, Benjamin Guedj, Shi Zhou. Forecasting elections results via the voter model with stubborn nodes. 2020. ⟨hal-02946434⟩

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