Forecasting elections results via the voter model with stubborn nodes

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.

On Aggregation and Dynamics of Opinions in Complex Networks

“…As the name implies, opinion dynamics is all about the change of attitudes over time via interactions in a network. Knowledge about opinion formation has a broad range of applications in, for example, election forecasting [23][24][25], finance [26], marketing [27,28], the study of fear of crime [29], classical physics [30], statistical physics [31,32], hydrodynamics [33], optimization [34,35], probability theory [36], biology [37], and many more. As the above list of applications suggests, the word "opinion" in opinion dynamics should not always be taken literally 1 , nor should one always assume that such models are concerned with social networks involving humans.…”

“…As noted in step (3), the voter model leads to consensus [31], and much of the literature on the voter model and its many variations studies the average time it takes to reach consensus [112][113][114]. Perhaps due to its simplicity, the voter model has attained a lot of attention from a wide range of research fields, including probability theory [115], statistical physics [116], hydrodynamics [33], election forecasting [25], ecology and evolutionary biology [37].…”

Time scales in the dynamics of political opinions and the voter model

“…However, it cannot be applied to long time scales and it may not easily be generalised to the whole society level. On the other hand, data from political polls or elections [23][24][25] have a poor time resolution and no information on the individual agents, the only measurable observable being the share n + (t) of voters who support the proposition at each time step. Concurrently, data are available up to the time scale of centuries (for elections) and describe the average opinion of the whole society and not only a subgroup.…”

“…[26] or in other words: how can observed opinion time series be explained by voter models? In previous studies election data over several decades [24][25][26][41][42][43][44] were analysed and linked to voter models. The model can be used to fit parameters and make predictions for future election outcomes [25].…”

“…In previous studies election data over several decades [24][25][26][41][42][43][44] were analysed and linked to voter models. The model can be used to fit parameters and make predictions for future election outcomes [25]. Such studies focus on the probability density function (PDF) and not on auto-correlations and time scales.…”