False-Name-Proof Locations of Two Facilities: Economic and Algorithmic Approaches
Authors: Akihisa Sonoda, Taiki Todo, Makoto Yokoo
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contribution presented in this paper is two-fold. From an economic perspective, we characterize possible outcomes by false-name-proof mechanisms that also satisfy Pareto efficiency and another mild condition called peak-onlyness. ... From an algorithmic perspective, we clarify the approximation ratios of deterministic/randomized false-name-proof mechanisms on the line metric under the two well-studied cost functions. |
| Researcher Affiliation | Academia | Akihisa Sonoda and Taiki Todo and Makoto Yokoo Department of Informatics, Kyushu University, Motooka 744, Fukuoka, Japan {sonoda@agent., todo@, yokoo@}inf.kyushu.ac.jp |
| Pseudocode | No | The paper describes various mechanisms (Mechanism 1, 2, 3) in prose but does not provide them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus no experimental setup details like hyperparameter values or training configurations are provided. |