94 research outputs found
Optimal Partitions in Additively Separable Hedonic Games
We conduct a computational analysis of fair and optimal partitions in
additively separable hedonic games. We show that, for strict preferences, a
Pareto optimal partition can be found in polynomial time while verifying
whether a given partition is Pareto optimal is coNP-complete, even when
preferences are symmetric and strict. Moreover, computing a partition with
maximum egalitarian or utilitarian social welfare or one which is both Pareto
optimal and individually rational is NP-hard. We also prove that checking
whether there exists a partition which is both Pareto optimal and envy-free is
-complete. Even though an envy-free partition and a Nash stable
partition are both guaranteed to exist for symmetric preferences, checking
whether there exists a partition which is both envy-free and Nash stable is
NP-complete.Comment: 11 pages; A preliminary version of this work was invited for
presentation in the session `Cooperative Games and Combinatorial
Optimization' at the 24th European Conference on Operational Research (EURO
2010) in Lisbo
Hedonic Games with Graph-restricted Communication
We study hedonic coalition formation games in which cooperation among the
players is restricted by a graph structure: a subset of players can form a
coalition if and only if they are connected in the given graph. We investigate
the complexity of finding stable outcomes in such games, for several notions of
stability. In particular, we provide an efficient algorithm that finds an
individually stable partition for an arbitrary hedonic game on an acyclic
graph. We also introduce a new stability concept -in-neighbor stability- which
is tailored for our setting. We show that the problem of finding an in-neighbor
stable outcome admits a polynomial-time algorithm if the underlying graph is a
path, but is NP-hard for arbitrary trees even for additively separable hedonic
games; for symmetric additively separable games we obtain a PLS-hardness
result
Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games
Additively separable hedonic games and fractional hedonic games have received
considerable attention. They are coalition forming games of selfish agents
based on their mutual preferences. Most of the work in the literature
characterizes the existence and structure of stable outcomes (i.e., partitions
in coalitions), assuming that preferences are given. However, there is little
discussion on this assumption. In fact, agents receive different utilities if
they belong to different partitions, and thus it is natural for them to declare
their preferences strategically in order to maximize their benefit. In this
paper we consider strategyproof mechanisms for additively separable hedonic
games and fractional hedonic games, that is, partitioning methods without
payments such that utility maximizing agents have no incentive to lie about
their true preferences. We focus on social welfare maximization and provide
several lower and upper bounds on the performance achievable by strategyproof
mechanisms for general and specific additive functions. In most of the cases we
provide tight or asymptotically tight results. All our mechanisms are simple
and can be computed in polynomial time. Moreover, all the lower bounds are
unconditional, that is, they do not rely on any computational or complexity
assumptions
Role Based Hedonic Games
In the hedonic coalition formation game model Roles Based Hedonic Games (RBHG), agents view teams as compositions of available roles. An agent\u27s utility for a partition is based upon which role she fulfills within the coalition and which additional roles are being fulfilled within the coalition. I consider optimization and stability problems for settings with variable power on the part of the central authority and on the part of the agents. I prove several of these problems to be NP-complete or coNP-complete. I introduce heuristic methods for approximating solutions for a variety of these hard problems. I validate heuristics on real-world data scraped from League of Legends games
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