Parameterized Complexity of Hedonic Games with Enemy-Oriented Preferences

TL;DR

This paper investigates the computational complexity of finding stable partitions in enemy-oriented hedonic games, providing new fixed-parameter tractable algorithms and hardness results based on various graph parameters.

Contribution

It offers a comprehensive parameterized complexity analysis of core stability problems in enemy-oriented hedonic games, including new algorithms and hardness proofs.

Findings

Polynomial algorithms for specific graph classes

FPT algorithms for treewidth and number of friends

W[1]-hardness for other parameters

Abstract

Hedonic games model settings in which a set of agents have to be partitioned into groups which we call coalitions. In the enemy aversion model, each agent has friends and enemies, and an agent prefers to be in a coalition with as few enemies as possible and, subject to that, as many friends as possible. A partition should be stable, i.e., no subset of agents prefer to be together rather than being in their assigned coalition under the partition. We look at two stability concepts: core stability and strict core stability. This yields several algorithmic problems: determining whether a (strictly) core stable partition exists, finding such a partition, and checking whether a given partition is (strictly) core stable. Several of these problems have been shown to be NP-complete, or even beyond NP. This motivates the study of parameterized complexity. We conduct a thorough computational study using several parameters: treewidth, number of friends, number of enemies, partition size, and coalition size. We give polynomial algorithms for restricted graph classes as well as FPT algorithms with respect to the number of friends an agent may have and the treewidth of the graph representing the friendship or enemy relations. We show W[1]-hardness or para-NP-hardness with respect to the other parameters. We conclude this paper with results in the setting in which agents can have neutral relations with each other, including hardness-results for very restricted cases.