Probably approximately correct stability of allocations in uncertain coalitional games with private sampling

Conference item

We study coalitional games with exogenous uncertainty in the coalition value, in which each agent is allowed to have private samples of the uncertainty. As a consequence, the agents may have a different perception of stability of the grand coalition. In this context, we propose a novel methodology to study the out-of-sample coalitional rationality of allocations in the set of stable allocations (i.e., the core). Our analysis builds on the framework of probably approximately correct learning. ...

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Authors: Pantazis, G; Fele, F; Fabiani, F; Grammatico, S; Margellos, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Host title: Proceedings of the 6th Annual Learning for Dynamics & Control Conference
• Series: PMLR
• Series number: 242
• Pages: 1702–1714
• Publication date: 2024-06-11
• Acceptance date: 2024-03-28
• Event title: 6th Annual Learning for Dynamics & Control Conference (L4DC 2024)
• Event location: Oxford
• Event website: https://l4dc.web.ox.ac.uk/home
• Event start date: 2024-07-15
• Event end date: 2024-07-17
• ISSN: 2640-3498
• Language: English
• Keywords: uncertain coalitional games; statistical learning; data privacy