Statistical Foundations of Virtual Democracy

Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3173-3182, 2019.

Abstract

Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method – or voting rule – to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people’s true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count.

Cite this Paper

BibTeX
@InProceedings{pmlr-v97-kahng19a, title = {Statistical Foundations of Virtual Democracy}, author = {Kahng, Anson and Lee, Min Kyung and Noothigattu, Ritesh and Procaccia, Ariel and Psomas, Christos-Alexandros}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {3173--3182}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/kahng19a/kahng19a.pdf}, url = {https://proceedings.mlr.press/v97/kahng19a.html}, abstract = {Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method – or voting rule – to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people’s true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count.} }
Endnote
%0 Conference Paper %T Statistical Foundations of Virtual Democracy %A Anson Kahng %A Min Kyung Lee %A Ritesh Noothigattu %A Ariel Procaccia %A Christos-Alexandros Psomas %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-kahng19a %I PMLR %P 3173--3182 %U https://proceedings.mlr.press/v97/kahng19a.html %V 97 %X Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method – or voting rule – to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people’s true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count.
APA
Kahng, A., Lee, M.K., Noothigattu, R., Procaccia, A. & Psomas, C.. (2019). Statistical Foundations of Virtual Democracy. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:3173-3182 Available from https://proceedings.mlr.press/v97/kahng19a.html.

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