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 [1].
Stability in Online Coalition Formation
Authors: Martin Bullinger, René Romen
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a comprehensive picture in additively separable hedonic games, leading to dichotomies, where positive results are obtained by deterministic algorithms and negative results even hold for randomized algorithms. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford 2School of Computation, Information and Technology, Technical University of Munich |
| Pseudocode | Yes | Algorithm 1: Contractually Nash-stable partition of online symmetric { y, x}-ASHGs for y x > 0. |
| Open Source Code | No | The paper cites a technical report (https://arxiv.org/abs/2312.09119) which is an extended version of the paper itself, but it does not contain an explicit statement about releasing source code for the described methodology or a direct link to a code repository. |
| Open Datasets | No | This paper is theoretical in nature and does not describe experiments performed on any datasets, thus there is no mention of a training dataset or its accessibility. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets, therefore, there is no mention of training/validation/test dataset splits. |
| Hardware Specification | No | This is a theoretical paper that focuses on algorithm design and proofs, and as such, it does not describe any computational experiments or specify hardware used. |
| Software Dependencies | No | This is a theoretical paper that focuses on algorithm design and proofs. It does not mention any specific software or library dependencies with version numbers that would be required to replicate computational results. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm design and proofs. It does not describe an experimental setup, hyperparameters, or training configurations. |