Equitable Mechanism Design for Facility Location

Authors: Toby Walsh

IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We first prove a simple but fundamental impossibility result that no strategy proof mechanism can bound the approximation ratio of the optimal Gini index of utilities for one or more facilities. Theorem 1. No strategy proof mechanism for locating one or more facilities on [0, 1] has a bounded approximation ratio for the Gini index of utilities. Proof. Suppose there exists a strategy proof mechanism for two agents with a bounded approximation ratio for locating a single facility.
Researcher Affiliation Academia Toby Walsh AI Institute, UNSW Sydney EMAIL
Pseudocode No The paper describes various mechanisms (e.g., LEFTMOST, MEDIAN, MIDORNEAREST, ENDPOINT) in textual descriptions, but it does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any explicit statements or links indicating the release of source code for the methodology described.
Open Datasets No The paper is theoretical, analyzing mechanisms for facility location on the real line [0, 1] with n agents, and does not use or refer to any specific publicly available datasets for experimental evaluation.
Dataset Splits No The paper is theoretical and does not use specific datasets; therefore, no information regarding dataset splits (e.g., training, test, validation) is provided.
Hardware Specification No The paper presents theoretical results and proofs for mechanism design problems and does not describe any experimental setup or hardware used for computations.
Software Dependencies No The paper is purely theoretical, focusing on mathematical proofs and analysis of mechanisms, and therefore does not list any software dependencies with specific version numbers.
Experiment Setup No The paper is entirely theoretical, presenting theorems and their proofs for various mechanism design properties. It does not describe any empirical experiments, and thus no experimental setup details like hyperparameters or training configurations are provided.