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..
Improved Approximation Ratio for Strategyproof Facility Location on a Cycle
Authors: Krzysztof Rogowski, Marcin Dziubiński
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To complement these theoretical results, we conducted numerical experiments to investigate which values of the approximation ratio are actually attained by the RD + PCD mechanism. The idea of the experiments is to compute the approximation ratio of the RD+PCD mechanism for a finite, computationally feasible, set of profiles, providing insight into the behavior of the mechanism for all possible profiles. |
| Researcher Affiliation | Academia | Krzysztof Rogowski , Marcin Dziubi nski University of Warsaw, Institute of Informatics EMAIL, |
| Pseudocode | No | The paper describes the mechanisms (Random Dictator, Proportional Circle Distance, and their mixture) using verbal descriptions and mathematical definitions, but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions "Extended version of the paper is available at https://arxiv.org/abs/2505.12943" which points to the paper on arXiv, not source code. There is no other explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper discusses mechanisms for facility location on a cycle and conducts numerical experiments by simulating agent profiles on a cycle with varying parameters (number of agents, number of allowed points). It does not use any pre-existing or publicly available datasets. |
| Dataset Splits | No | The paper's experimental section describes numerical simulations of mechanism performance on generated profiles, not on external datasets. Therefore, there are no dataset splits mentioned. |
| Hardware Specification | No | The paper's 'Experiments' section describes the computational analysis of the RD+PCD mechanism but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for these computations. |
| Software Dependencies | No | The paper does not mention any specific software or library names with version numbers that were used to conduct the numerical experiments. |
| Experiment Setup | Yes | During the experiments, we considered the number of agents n in the range [2..60]. The second parameter to consider is the set of possible reports for each agent. ... For l N, let Gl denote the subset of l points on the cycle G (of length 1) that are equally spaced and include the point 0. During the experiments, we restricted agents reports to the set Gl for l [2..60]. ... In such cases, we restricted our analysis to profiles in which agents reported no more than 3 distinct points. |