Filos-Ratsikas, Aris and Micha, Evi and Voudouris, Alexandros A (2020) The distortion of distributed voting. Artificial Intelligence, 286. p. 103343. DOI https://doi.org/10.1016/j.artint.2020.103343
Filos-Ratsikas, Aris and Micha, Evi and Voudouris, Alexandros A (2020) The distortion of distributed voting. Artificial Intelligence, 286. p. 103343. DOI https://doi.org/10.1016/j.artint.2020.103343
Filos-Ratsikas, Aris and Micha, Evi and Voudouris, Alexandros A (2020) The distortion of distributed voting. Artificial Intelligence, 286. p. 103343. DOI https://doi.org/10.1016/j.artint.2020.103343
Abstract
Voting can abstractly model any decision-making scenario and as such it has been extensively studied over the decades. Recently, the related literature has focused on quantifying the impact of utilizing only limited information in the voting process on the societal welfare for the outcome, by bounding the distortion of voting rules. Even though there has been significant progress towards this goal, almost all previous works have so far neglected the fact that in many scenarios (like presidential elections) voting is actually a distributed procedure. In this paper, we consider a setting in which the voters are partitioned into disjoint districts and vote locally therein to elect local winning alternatives using a voting rule; the final outcome is then chosen from the set of these alternatives. We prove tight bounds on the distortion of well-known voting rules for such distributed elections both from a worst-case perspective as well as from a best-case one. Our results indicate that the partition of voters into districts leads to considerably higher distortion, a phenomenon which we also experimentally showcase using real-world data.
Item Type: | Article |
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Uncontrolled Keywords: | Distributed voting; District-based elections; Distortion |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Jun 2020 16:25 |
Last Modified: | 30 Oct 2024 16:19 |
URI: | http://repository.essex.ac.uk/id/eprint/27911 |
Available files
Filename: 1905.01882.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0