Optimality and Impossibility in Multidimensional Strategyproof Facility Location
Jianan Lin
We study facility location in multidimensional normed spaces with the objective of minimizing social cost, the sum of agents' distances to their nearest facility. A deterministic mechanism selects $k$ facility locations based on the reports of $n$ agents and is strategyproof (SP) if no agent can reduce her distance by misreporting her location. We establish optimal approximation ratios and impossibility results. For a single facility in the Euclidean plane, we prove that the optimal approximation ratios among deterministic SP mechanisms are exactly those obtained by Goel and Hann-Caruthers [SCW 2023] for the coordinate-wise median (CM): $\frac{\sqrt{2(n^2+1)}}{n+1}$ for odd $n\ge3$ and $\sqrt{2}$ for even $n\ge4$. We remove the anonymity assumption from their optimality result. However, CM is not always optimal in non-Euclidean spaces: we give an SP mechanism that minimizes social cost exactly in $\ell_\infty^d$, whereas CM has a tight uniform approximation ratio of $2$ already in the plane. We then consider two facilities in $\ell_p$ spaces with $1<p<\infty$ and prove two impossibility results for every $n>2$. In the plane, no deterministic SP mechanism has a finite approximation ratio. In higher dimensions, the same impossibility holds for anonymous deterministic SP mechanisms. Finally, a reduction using distant agents and a limiting argument extends both impossibility results to every $k>2$ with $n>k$.
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