Price of Pareto Optimality in Hedonic Games
Abstract
Price of Anarchy measures the welfare loss caused by selfish behavior: it is defined as the ratio of the social welfare in a socially optimal outcome and in a worst Nash equilibrium. A similar measure can be derived for other classes of stable outcomes. In this paper, we argue that Pareto optimality can be seen as a notion of stability, and introduce the concept of Price of Pareto Optimality: this is an analogue of the Price of Anarchy, where the maximum is computed over the class of Pareto optimal outcomes, i.e., outcomes that do not permit a deviation by the grand coalition that makes all players weakly better off and some players strictly better off. As a case study, we focus on hedonic games, and provide lower and upper bounds of the Price of Pareto Optimality in three classes of hedonic games: additively separable hedonic games, fractional hedonic games, and modified fractional hedonic games; for fractional hedonic games on trees our bounds are tight.
Cite
Text
Elkind et al. "Price of Pareto Optimality in Hedonic Games." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10048Markdown
[Elkind et al. "Price of Pareto Optimality in Hedonic Games." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/elkind2016aaai-price/) doi:10.1609/AAAI.V30I1.10048BibTeX
@inproceedings{elkind2016aaai-price,
title = {{Price of Pareto Optimality in Hedonic Games}},
author = {Elkind, Edith and Fanelli, Angelo and Flammini, Michele},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {475-481},
doi = {10.1609/AAAI.V30I1.10048},
url = {https://mlanthology.org/aaai/2016/elkind2016aaai-price/}
}