Multi-Dimensional Single-Peaked Consistency and Its Approximations
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 375-381.
Abstract
Single-peakedness is one of the most commonly used domain restrictions in social choice. However, the extent to which agent preferences are single-peaked in practice, and the extent to which recent proposals for approximate single-peakedness can further help explain voter preferences, is unclear. In this article, we assess the ability of both single-dimensional and multi-dimensional approximations to explain preference profiles drawn from several real-world elections. We develop a simple branch-and-bound algorithm that finds multi-dimensional, single-peaked axes that best fit a given profile, and which works with several forms of approximation. Empirical results on two election data sets show that preferences in these elections are far from single-peaked in any one-dimensional space, but are nearly single-peaked in two dimensions. Our algorithms are reasonably efficient in practice, and also show excellent anytime performance.
@inproceedings{ijcai2013p63,
title = {Multi-Dimensional Single-Peaked Consistency and Its Approximations},
author = {Sui, Xin and Francois-Nienaber, Alex and Boutilier, Craig},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {375--381},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/63},
}
TY - CPAPER TI - Multi-Dimensional Single-Peaked Consistency and Its Approximations AU - Sui, Xin AU - Francois-Nienaber, Alex AU - Boutilier, Craig T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 375 EP - 381 UR - https://ijcai.org/proceedings/2013/63 ER -Copied
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