تقرير
Forecasting elections results via the voter model with stubborn nodes
العنوان: | Forecasting elections results via the voter model with stubborn nodes |
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المؤلفون: | Vendeville, Antoine, Guedj, Benjamin, Zhou, Shi |
المصدر: | Applied Network Science 6, 1 (2021) |
سنة النشر: | 2020 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Social and Information Networks, Computer Science - Machine Learning |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
DOI: | 10.1007/s41109-020-00342-7 |
URL الوصول: | http://arxiv.org/abs/2009.10627 |
رقم الأكسشن: | edsarx.2009.10627 |
قاعدة البيانات: | arXiv |
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