Statistical Foundations of Virtual Democracy

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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.

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