Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
False-Name-Proof Locations of Two Facilities: Economic and Algorithmic Approaches
Authors: Akihisa Sonoda, Taiki Todo, Makoto Yokoo
AAAI 2016 | Venue PDF | 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 ef๏ฌciency 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. |