论文标题
通过顽固节点的选民模型预测选举结果
Forecasting elections results via the voter model with stubborn nodes
论文作者
论文摘要
在本文中，我们提出了一种新的方法，以预测仅使用以前的官方结果的选举结果。它基于具有顽固节点的选民模型，并使用了我们先前的工作中发展的理论结果。我们查看英国保守党和劳工方以及美国的共和党和民主党的普选投票份额。我们能够对模型参数进行随时间不断发展的估计，并使用这些估计来预测任何选举中每一方的投票股份。我们获得4.74 \％的平均绝对误差。作为附带产品，我们的参数估计提供了对政治格局的有意义的见解，向我们告知我们，这些选民比例是每个被考虑的当事方的强有力支持者。
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.
