Decision theory and game theory are both interdisciplinary domains that focus on modelling and {analyzing} decision-making processes. On the one hand, decision theory aims to account for the possible behaviors of an agent with respect to an uncertain situation. It thus provides several frameworks to describe the decision-making processes in this context, including that of belief functions. On the other hand, game theory focuses on multi-agent decisions, typically with probabilistic uncertainty (if any), hence the so-called class of Bayesian games. In this paper, we use the Coq/SSReflect proof assistant to formally prove the results we obtained in [Pierre Pomeret{-}Coquot et al., 2022]. First, we formalize a general theory of belief functions with finite support, and structures and solutions concepts from game theory. On top of that, we extend Bayesian games to the theory of belief functions, so that we obtain a more expressive class of games we refer to as Bel games; it makes it possible to better capture human behaviors with respect to lack of information. Next, we provide three different proofs of an extended version of the so-called Howson-Rosenthal’s theorem, showing that Bel games can be casted into games of complete information, i.e., without any uncertainty. We thus embed this class of games into classical game theory, enabling the use of existing algorithms.