Proceedings

Multi-Dimensional Single-Peaked Consistency and Its Approximations

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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.

Xin Sui, Alex Francois-Nienaber, and Craig Boutilier. 2013. Multi-Dimensional Single-Peaked Consistency and Its Approximations. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 375–381. https://ijcai.org/proceedings/2013/63
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