Pith. sign in

Optimality of the coordinate-wise median mechanism for strategyproof facility location in two dimensions

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

We consider the facility location problem in two dimensions. In particular, we consider a setting where agents have Euclidean preferences, defined by their ideal points, for a facility to be located in $\mathbb{R}^2$. We show that for the $p-norm$ ($p \geq 1$) objective, the coordinate-wise median mechanism (CM) has the lowest worst-case approximation ratio in the class of deterministic, anonymous, and strategyproof mechanisms. For the minisum objective and an odd number of agents $n$, we show that CM has a worst-case approximation ratio (AR) of $\sqrt{2}\frac{\sqrt{n^2+1}}{n+1}$. For the $p-norm$ social cost objective ($p\geq 2$), we find that the AR for CM is bounded above by $2^{\frac{3}{2}-\frac{2}{p}}$. We conjecture that the AR of CM actually equals the lower bound $2^{1-\frac{1}{p}}$ (as is the case for $p=2$ and $p=\infty$) for any $p\geq 2$.

citation-role summary

other 1

citation-polarity summary

fields

cs.DS 1

years

2025 1

verdicts

REJECT 1

roles

other 1

polarities

unclear 1

representative citing papers

Prediction-Augmented Mechanism Design for Weighted Facility Location

cs.DS · 2025-07-09 · reject · novelty 5.0

The paper claims a strategyproof prediction-augmented mechanism for weighted facility location with consistency-robustness bounds depending on the ratio of maximum to minimum agent weight, but the supporting reduction proof is incomplete.

citing papers explorer

Showing 1 of 1 citing paper.

  • Prediction-Augmented Mechanism Design for Weighted Facility Location cs.DS · 2025-07-09 · reject · none · ref 28 · internal anchor

    The paper claims a strategyproof prediction-augmented mechanism for weighted facility location with consistency-robustness bounds depending on the ratio of maximum to minimum agent weight, but the supporting reduction proof is incomplete.