[2510.24906] Fair Indivisible Payoffs through Shapley Value

Computer Science > Computer Science and Game Theory arXiv:2510.24906 (cs) [Submitted on 28 Oct 2025 (v1), last revised 1 Apr 2026 (this version, v2)] Title:Fair Indivisible Payoffs through Shapley Value Authors:Mikołaj Czarnecki, Michał Korniak, Oskar Skibski, Piotr Skowron View a PDF of the paper titled Fair Indivisible Payoffs through Shapley Value, by Miko{\l}aj Czarnecki and 3 other authors View PDF HTML (experimental) Abstract:We consider the problem of payoff division in indivisible coalitional games, where the value of the grand coalition is a natural number. This number represents a certain quantity of indivisible objects, such as parliamentary seats, kidney exchanges, or top features contributing to the outcome of a machine learning model. The goal of this paper is to propose a fair method for dividing these objects among players. To achieve this, we define the indivisible Shapley value and study its properties. We demonstrate our proposed technique using three case studies, in particular, we use it to identify key regions of an image in the context of an image classification task. Subjects: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI) Cite as: arXiv:2510.24906 [cs.GT]   (or arXiv:2510.24906v2 [cs.GT] for this version)   https://doi.org/10.48550/arXiv.2510.24906 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mikołaj Czarnecki [view email] [v1] Tue, 28 Oct 2025 19:18:07 UTC (2,330 KB) [v2] Wed, 1 Apr 2026 12:...