$\varepsilon$-fractional core stability in Hedonic Games.
Authors: Simone Fioravanti, Michele Flammini, Bojana Kodric, Giovanna Varricchio
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To circumvent these problems, we propose the notion of "-fractional core-stability, where at most an "-fraction of all possible coalitions is allowed to core-block. It turns out that such a relaxation may guarantee both existence and polynomial-time computation. Specifically, we design efficient algorithms returning an "-fractional core-stable partition, with " exponentially decreasing in the number of agents, for two fundamental classes of HGs: Simple Fractional and Anonymous. |
| Researcher Affiliation | Academia | 1 Gran Sasso Science Institute (GSSI), L Aquila, Italy 2 University of Calabria, Rende, Italy 3 Ca Foscari University of Venice, Venice, Italy 4 Goethe-Universität, Frankfurt am Main, Germany |
| Pseudocode | Yes | Algorithm 1: Stabilizing Simple FHGs |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper does not mention the use of any publicly available or open datasets for training or evaluation. The research is theoretical. |
| Dataset Splits | No | The paper does not provide information about training/test/validation dataset splits. The research is theoretical. |
| Hardware Specification | No | The paper does not describe any specific hardware used for experiments. The research is theoretical. |
| Software Dependencies | No | The paper does not provide details about specific software dependencies or their version numbers. The research is theoretical. |
| Experiment Setup | No | The paper does not provide specific experimental setup details, hyperparameters, or training configurations. The research is theoretical. |