Political Dimensionality Estimation Using a Probabilistic Graphical Model

Yoad Lewenberg, Yoram Bachrach, Lucas Bordeaux, Pushmeet Kohli
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:138-147, 2016.

Abstract

This paper attempts to move beyond the left-right characterization of political ideologies. We propose a trait based probabilistic model for estimating the manifold of political opinion. We demonstrate the efficacy of our model on two novel and large scale datasets of public opinion. Our experiments show that although the political spectrum is richer than a simple left-right structure, peoples’ opinions on seemingly unrelated political issues are very correlated, so fewer than 10 dimensions are enough to represent peoples’ entire political opinion.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-lewenberg16a, title = {Political Dimensionality Estimation Using a Probabilistic Graphical Model}, author = {Lewenberg, Yoad and Bachrach, Yoram and Bordeaux, Lucas and Kohli, Pushmeet}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {138--147}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/lewenberg16a/lewenberg16a.pdf}, url = {https://proceedings.mlr.press/r14/lewenberg16a.html}, abstract = {This paper attempts to move beyond the left-right characterization of political ideologies. We propose a trait based probabilistic model for estimating the manifold of political opinion. We demonstrate the efficacy of our model on two novel and large scale datasets of public opinion. Our experiments show that although the political spectrum is richer than a simple left-right structure, peoples’ opinions on seemingly unrelated political issues are very correlated, so fewer than 10 dimensions are enough to represent peoples’ entire political opinion.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Political Dimensionality Estimation Using a Probabilistic Graphical Model %A Yoad Lewenberg %A Yoram Bachrach %A Lucas Bordeaux %A Pushmeet Kohli %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-lewenberg16a %I PMLR %P 138--147 %U https://proceedings.mlr.press/r14/lewenberg16a.html %V R14 %X This paper attempts to move beyond the left-right characterization of political ideologies. We propose a trait based probabilistic model for estimating the manifold of political opinion. We demonstrate the efficacy of our model on two novel and large scale datasets of public opinion. Our experiments show that although the political spectrum is richer than a simple left-right structure, peoples’ opinions on seemingly unrelated political issues are very correlated, so fewer than 10 dimensions are enough to represent peoples’ entire political opinion. %Z Reissued by PMLR on 04 October 2026.
APA
Lewenberg, Y., Bachrach, Y., Bordeaux, L. & Kohli, P.. (2016). Political Dimensionality Estimation Using a Probabilistic Graphical Model. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:138-147 Available from https://proceedings.mlr.press/r14/lewenberg16a.html. Reissued by PMLR on 04 October 2026.

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