Existence, Computation and Efficiency of Nash Stable Outcomes in Hedonic Skill Games

This article deals with hedonic skill games, a non-transferable utility counterpart of coalitional skill games which model collaboration among entities through the abstract notions of tasks and the skills required to complete them. In the weighted tasks setting, we show that deciding whether an instance of the game admits a Nash stable outcome is NP-complete. We then characterize the instances admitting a Nash stable outcome. This characterization relies on the fact that every agent holds (resp., every task requires) either a single skill or more than one skill. For these instances, the complexity of computing a Nash stable outcome is determined, together with the possibility that natural dynamics converge to a Nash stable outcome from any initial configuration. Our study is completed with a thorough analysis of the price of anarchy of instances always admitting a Nash stable outcome.

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Publication Details

Journal
Journal of Artificial Intelligence Research
Published
2025-03-25
DOI
https://doi.org/10.1613/jair.1.17157
Primary Topic
Game Theory and Voting Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Existence, Computation and Efficiency of Nash Stable Outcomes in Hedonic Skill Games

Laurent Gourvès, Gianpiero Monaco
Journal of Artificial Intelligence Research
Game Theory and Voting Systems
article

Existence, Computation and Efficiency of Nash Stable Outcomes in Hedonic Skill Games

Laurent Gourvès, Gianpiero Monaco
article en

Abstract

This article deals with hedonic skill games, a non-transferable utility counterpart of coalitional skill games which model collaboration among entities through the abstract notions of tasks and the skills required to complete them. In the weighted tasks setting, we show that deciding whether an instance of the game admits a Nash stable outcome is NP-complete. We then characterize the instances admitting a Nash stable outcome. This characterization relies on the fact that every agent holds (resp., every task requires) either a single skill or more than one skill. For these instances, the complexity of computing a Nash stable outcome is determined, together with the possibility that natural dynamics converge to a Nash stable outcome from any initial configuration. Our study is completed with a thorough analysis of the price of anarchy of instances always admitting a Nash stable outcome.

Journal of Artificial Intelligence ResearchVol. 82
University of Chieti-Pescara (IT)
Openalex Percentile: Top 91%
Game Theory and Voting Systems
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