Inferring Population Preferences via Mixtures of Spatial Voting Models
© 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...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing
2021
|
| Online Access: | https://hdl.handle.net/1721.1/137887 |
| _version_ | 1826190610620481536 |
|---|---|
| author | Nahm, Alison Pentland, Alex Krafft, Peter |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Nahm, Alison Pentland, Alex Krafft, Peter |
| author_sort | Nahm, Alison |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T08:43:00Z |
| format | Article |
| id | mit-1721.1/137887 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:43:00Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1378872023-02-08T21:01:52Z Inferring Population Preferences via Mixtures of Spatial Voting Models Nahm, Alison Pentland, Alex Krafft, Peter Massachusetts Institute of Technology. Media Laboratory Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 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. 2021-11-09T14:42:46Z 2021-11-09T14:42:46Z 2016 2019-07-26T16:36:01Z Article http://purl.org/eprint/type/ConferencePaper 0302-9743 1611-3349 https://hdl.handle.net/1721.1/137887 Nahm, Alison, Pentland, Alex and Krafft, Peter. 2016. "Inferring Population Preferences via Mixtures of Spatial Voting Models." en 10.1007/978-3-319-47880-7_18 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer International Publishing arXiv |
| spellingShingle | Nahm, Alison Pentland, Alex Krafft, Peter Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title | Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title_full | Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title_fullStr | Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title_full_unstemmed | Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title_short | Inferring Population Preferences via Mixtures of Spatial Voting Models |
| title_sort | inferring population preferences via mixtures of spatial voting models |
| url | https://hdl.handle.net/1721.1/137887 |
| work_keys_str_mv | AT nahmalison inferringpopulationpreferencesviamixturesofspatialvotingmodels AT pentlandalex inferringpopulationpreferencesviamixturesofspatialvotingmodels AT krafftpeter inferringpopulationpreferencesviamixturesofspatialvotingmodels |