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The Impact of Selfishness in Hypergraph Hedonic Games
Author(s) -
Alessandro Aloisio,
Michele Flammini,
Cosimo Vinci
Publication year - 2020
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v34i02.5542
Subject(s) - hypergraph , price of anarchy , price of stability , core (optical fiber) , selfishness , nash equilibrium , mathematics , mathematical economics , outcome (game theory) , class (philosophy) , combinatorics , economics , computer science , social psychology , monetary policy , telecommunications , psychology , artificial intelligence , monetary economics
We consider a class of coalition formation games that can be succinctly represented by means of hypergraphs and properly generalizes symmetric additively separable hedonic games. More precisely, an instance of hypegraph hedonic game consists of a weighted hypergraph, in which each agent is associated to a distinct node and her utility for being in a given coalition is equal to the sum of the weights of all the hyperedges included in the coalition. We study the performance of stable outcomes in such games, investigating the degradation of their social welfare under two different metrics, the k-Nash price of anarchy and k-core price of anarchy, where k is the maximum size of a deviating coalition. Such prices are defined as the worst-case ratio between the optimal social welfare and the social welfare obtained when the agents reach an outcome satisfying the respective stability criteria. We provide asymptotically tight upper and lower bounds on the values of these metrics for several classes of hypergraph hedonic games, parametrized according to the integer k, the hypergraph arity r and the number of agents n. Furthermore, we show that the problem of computing the exact value of such prices for a given instance is computationally hard, even in case of non-negative hyperedge weights.

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