Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games

Saar Cohen, Noa Agmon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Hedonic games model cooperative games where agents desire to form coalitions, and only care about the composition of the coalitions of which they are members. Focusing on various classes of dichotomous hedonic games, where each agent either approves or disapproves a given coalition, we propose the random extension, where players have an independent participation probability. We initiate the research on the computational complexity of computing the probability that coalitions and partitions are optimal or stable. While some cases admit efficient algorithms (e.g., agents approve only few coalitions), they become computationally hard (#P-hard) in their complementary scenario. We then investigate the distribution of coalitions in perfect partitions and their performance in majority games, where an agent approves coalitions in which the agent is friends with the majority of its members. When friendships independently form with a constant probability, we prove that the number of coalitions of size 3 converges in distribution to a Poisson random variable.

Original languageEnglish
Title of host publicationAAAI-23 Technical Tracks 5
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAAAI press
Number of pages9
ISBN (Electronic)9781577358800
StatePublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023


Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This research was funded in part by ISF grant 1563/22.

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence ( All rights reserved.


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