Forecasting elections results via the voter model with stubborn nodes

TL;DR

This paper introduces a novel election forecasting method based on the voter model with stubborn nodes, utilizing historical official results to predict future vote shares with high accuracy and political insight.

Contribution

It develops a new forecasting approach using the voter model with stubborn nodes and provides a method to estimate model parameters over time from official election results.

Findings

Achieved a mean absolute error of 4.74% in vote share predictions.

Provided meaningful insights into voter support strength for major parties.

Demonstrated the model's applicability to UK and US elections.

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.