Approximation guarantees of Median Mechanism in $\mathbb{R}^d$
Nick Gravin, Jianhao Jia

TL;DR
This paper systematically analyzes the approximation ratios of the coordinate-wise median mechanism in multi-dimensional Euclidean spaces, providing tight bounds that are independent of dimension and extend to generalized median mechanisms.
Contribution
It derives constant upper bounds for the approximation ratio of coordinate median in any dimension and norm, and shows these bounds are nearly tight with matching lower bounds.
Findings
Constant upper bounds for approximation ratios in all norms.
Bounds are nearly tight with matching lower bounds in high dimensions.
Extension of analysis to generalized median mechanisms across dimensions.
Abstract
The coordinate-wise median is a classic and most well-studied strategy-proof mechanism in social choice and facility location scenarios. Surprisingly, there is no systematic study of its approximation ratio in -dimensional spaces. The best known approximation guarantee in -dimensional Euclidean space is via embedding into metric space, that only appeared in appendix of [Meir 2019].This upper bound is known to be tight in dimension , but there are no known super constant lower bounds. Still, it seems that the community's belief about coordinate-wise median is on the side of . E.g., a few recent papers on mechanism design with predictions [Agrawal, Balkanski, Gkatzelis, Ou, Tan 2022], [Christodoulou, Sgouritsa, Vlachos 2024], and [Barak, Gupta, Talgam-Cohen 2024]…
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Taxonomy
TopicsAdvanced Numerical Analysis Techniques · Mathematical Approximation and Integration · Stochastic processes and financial applications
