Relaxed Core Stability in Fractional Hedonic Games
Authors: Angelo Fanelli, Gianpiero Monaco, Luca Moscardelli
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate these concepts of stability in fractional hedonic games, that is a well-known subclass of hedonic games for which core stable outcomes are not guaranteed to exist and it is computationally hard to decide nonemptiness of the core. Interestingly, the considered relaxed notions of core also possess the appealing property of recovering, in some notable cases, the convergence, the existence and the possibility of computing stable solutions in polynomial time. |
| Researcher Affiliation | Academia | Angelo Fanelli1 , Gianpiero Monaco2 , Luca Moscardelli3 1CNRS, (UMR-6211), France 2University of L Aquila, L Aquila, Italy 3University of Chieti-Pescara, Pescara, Italy |
| Pseudocode | No | The paper describes an algorithm within the text of Theorem 6, but it does not include a clearly labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments involving data splits. |
| Hardware Specification | No | This is a theoretical paper and does not mention specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training settings. |