Simple Causes of Complexity in Hedonic Games

Authors: Dominik Peters, Edith Elkind

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we identify simple conditions on expressivity of hedonic games that are sufficient for the problem of checking whether a given game admits a stable outcome to be computationally hard. Somewhat surprisingly, these conditions are very mild and intuitive. Our results apply to a wide range of stability concepts (core stability, individual stability, Nash stability, etc.) and to many known formalisms for hedonic games (additively separable games, games with W-preferences, fractional hedonic games, etc.), and unify and extend known results for these formalisms. They also have broader applicability: for several classes of hedonic games whose computational complexity has not been explored in prior work, we show that our framework immediately implies a number of hardness results for them.
Researcher Affiliation Academia Dominik Peters and Edith Elkind Department of Computer Science University of Oxford, UK {dominik.peters, edith.elkind}@cs.ox.ac.uk
Pseudocode No The paper describes algorithms and reductions in prose and mathematical notation but does not include structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement about releasing source code for its methodology or any links to a code repository.
Open Datasets No The paper is theoretical and does not involve empirical experiments with datasets; no dataset access information for training is provided.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with datasets; no information about validation splits is provided.
Hardware Specification No The paper is theoretical and does not describe empirical experiments; no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe empirical experiments; no specific software dependencies with version numbers are mentioned.
Experiment Setup No The paper is theoretical and does not describe empirical experiments; no details on hyperparameters or system-level training settings are provided.