Altruism in Coalition Formation Games

Authors: Anna Maria Kerkmann, Jörg Rothe

IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We propose to extend their model to coalition formation games in general, considering also the friends in other coalitions. Comparing the two models, we argue that excluding some friends from the altruistic behavior of an agent is a major disadvantage that comes with the restriction to hedonic games. After introducing our model, we additionally study some common stability notions and provide a computational analysis of the associated verification and existence problems.
Researcher Affiliation Academia Anna Maria Kerkmann and J org Rothe Institut f ur Informatik, Heinrich-Heine-Universit at D usseldorf, Germany {anna.kerkmann, rothe}@uni-duesseldorf.de
Pseudocode No The paper describes theoretical concepts, definitions, and theorems, but does not include any pseudocode or algorithm blocks.
Open Source Code No The paper does not mention providing open-source code for the described methodology. It is a theoretical paper.
Open Datasets No The paper focuses on theoretical analysis and computational complexity in coalition formation games. It does not use or reference any publicly available datasets for training or experimentation in the empirical sense.
Dataset Splits No The paper focuses on theoretical analysis and computational complexity. There is no mention of dataset splits (training, validation, test) as no empirical experiments are conducted.
Hardware Specification No The paper is theoretical and focuses on computational analysis. It does not mention any specific hardware used for experiments.
Software Dependencies No The paper is theoretical and focuses on computational analysis. It does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and focuses on computational analysis. It does not describe an experimental setup with hyperparameters or training settings.