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Article Dans Une Revue Artificial Intelligence Année : 2020

Price of Pareto Optimality in hedonic games

Résumé

The 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. Similar measures can be derived for other classes of stable outcomes. We observe that Pareto optimality can be seen as a notion of stability: an outcome is Pareto optimal if and only if it does not admit a deviation by the grand coalition that makes all players weakly better off and some players strictly better off. Motivated by this observation, we introduce the concept of Price of Pareto Optimality: this is an analogue of the Price of Anarchy, with the worst Nash equilibrium replaced with the worst Pareto optimal outcome. We then study this concept in the context of hedonic games, and provide lower and upper bounds on the Price of Pareto Optimality in three classes of hedonic games: additively separable hedonic games, fractional hedonic games, and modified fractional hedonic games. © 2020 Elsevier B.V.
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hal-02932135 , version 1 (22-08-2022)

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Paternité - Pas d'utilisation commerciale

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Edith Elkind, Angelo Fanelli, Michele Flammini. Price of Pareto Optimality in hedonic games. Artificial Intelligence, 2020, 288, pp.103357. ⟨10.1016/j.artint.2020.103357⟩. ⟨hal-02932135⟩
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