Multiagent systems is a branch of artificial intelligence (AI) that examines the behavior within communities of rational actors, where the actions of one actor may have consequences for another. Multiagent systems takes the idea of individual incentives from economic game theory and applies it to distributed computation and decentralized mechanisms. It examines not only how certain overall economic or computational goals can be accomplished, but also why individual participants will choose to cooperate in accomplishing that goal. Pedagogy within multiagent system is rich in mathematical rigor and theory. However, there is a gap in pedagogical practices that ties that theoretical training with the application of that theory to actual human agents. This is especially important as AI is deployed in increasingly sociotechnical domains. This paper presents the first exploration of using large participation activities to facilitate experiential learning to bridge this gap. We conduct an in-person game-like activity (a megagame'') where up to 43 participants engage in a day-long resource allocation scenario, where learners can apply their theoretical frameworks to analyze and solve emerging problems, while motivated by and under the pressure of meaningful stakes. We present these megagames as a potential pedagogical tool for both multiagent systems and other domains.