2025-05-152025-05-152024Pantazis, G., Fele, F., Fabiani, F., Grammatico, S. y Margellos, K. (2024). Probably approximately correct stability of allocations in uncertain coalitional games with private sampling. En 6th Annual Learning for Dynamics and Control Conference, L4DC 2024, Proceedings of Machine Learning Research, 242., Oxford.2640-3498https://hdl.handle.net/11441/172783We 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. Initially, we state a priori and a posteriori guarantees for the entire core. Furthermore, we provide a distributed algorithm to compute a compression set that determines the generalization properties of the a posteriori statements. We then refine our probabilistic robustness bounds by specialising the analysis to a single payoff allocation, taking, also in this case, both a priori and a posteriori approaches. Finally, we consider a relaxed ζ-core to include nearby allocations and also address the case of empty core. For this case, probabilistic statements are given on the eventual stability of allocations in the ζ-core.application/pdf13 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Uncertain coalitional gamesStatistical learningData privacyinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess