Inferring Population Preferences via Mixtures of Spatial Voting Models

TL;DR

This paper introduces a mixture of spatial voting models to infer voters' political preferences from election data, providing a cost-effective alternative to surveys and enabling direct comparison of voter and candidate preferences.

Contribution

The paper presents a novel mixture model that estimates the distribution of political preferences using only voting records and candidate positions, facilitating analysis of polarization.

Findings

Electorates in Texas, New York, and Ohio may be less bimodal than candidate distributions.

Voter preferences are more extreme in variance compared to candidate preferences.

The model effectively infers preferences and polarization from election data.

Abstract

Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.