🤖 AI Summary
This study addresses the limitation of classical cooperative game theory, which typically assumes a deterministic environment and thus struggles to capture real-world uncertainty. The paper provides a systematic review of cooperative game models incorporating uncertainty since the seminal work of Charnes and Granot (1976), integrating modeling tools from fuzzy set theory, probability theory, and belief functions. It establishes, for the first time, a unified framework to comparatively analyze solution concepts—particularly allocation rules and the core—across these diverse models. Notably, the authors introduce the “Bel coalition game,” a novel model grounded in belief functions, which clarifies the interrelationships and distinctions among existing approaches to uncertainty in cooperative games. This framework offers a coherent analytical foundation and clear pathways for future theoretical development and practical applications.
📝 Abstract
The main stream of the literature in cooperative game theory considers
games as being deterministic. In this survey, we review the main models
incorporating uncertainty in cooperative games, starting from the seminal
paper of Charnes and Granot (1976) and finishing with our own contribution,
called Bel coalitional games. The different models are explained and compared,
making a focus on the notion of allocation and core, since all the described
models propose a notion of core.