Robust Voting Rules from Algorithmic Robust Statistics

• URL: http://arxiv.org/abs/2112.06380v1
• Date: Mon, 13 Dec 2021 02:30:26 GMT
• Title: Robust Voting Rules from Algorithmic Robust Statistics
• Authors: Allen Liu, Ankur Moitra
• Abstract summary: We give an algorithm that can accurately estimate the central ranking even when a constant fraction of its samples are arbitrarily corrupted. Our work can be thought of as a natural infusion of perspectives from algorithmic robust statistics into one of the central inference problems in voting and information-aggregation.
• Score: 25.313563663123354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we study the problem of robustly learning a Mallows model. We give an algorithm that can accurately estimate the central ranking even when a constant fraction of its samples are arbitrarily corrupted. Moreover our robustness guarantees are dimension-independent in the sense that our overall accuracy does not depend on the number of alternatives being ranked. Our work can be thought of as a natural infusion of perspectives from algorithmic robust statistics into one of the central inference problems in voting and information-aggregation. Specifically, our voting rule is efficiently computable and its outcome cannot be changed by much by a large group of colluding voters.

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