Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Single-peaked consistency and its complexity
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
A latent space model for rank data
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
Winner-imposing strategyproof mechanisms for multiple facility location games
WINE'10 Proceedings of the 6th international conference on Internet and network economics
The complexity of manipulative attacks in nearly single-peaked electorates
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
Strategy-proof mechanisms for facility location games with many facilities
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Mechanism design on discrete lines and cycles
Proceedings of the 13th ACM Conference on Electronic Commerce
Stable matching with preferences derived from a psychological model
Operations Research Letters
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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.