Inferring Population Preferences via Mixtures of Spatial Voting Models
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1610.07004.pdf
Description
Accepted version
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861.03 KB
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Adobe PDF
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Author(s)
Nahm, Alison
Pentland, Alex
Krafft, Peter
Date Issued
2016
Publisher
Springer International Publishing
Citation
Nahm, Alison, Pentland, Alex and Krafft, Peter. 2016. "Inferring Population Preferences via Mixtures of Spatial Voting Models."
Version
Author's final manuscript
Abstract
© Springer International Publishing AG 2016. 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 costeffective 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-47880-7_18
