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Multiagent systems is a key area within artificial intelligence (AI) that explores the behavior of interacting rational agents where the decisions of one agent impact others. Rooted in economic game theory, 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 with reaching that goal. While multiagent systems is grounded in rigorous mathematical theory, current pedagogical approaches often lack opportunities for students to connect abstract theory with real-world human dynamics. This disconnect is particularly pressing as AI increasingly operates in sociotechnical environments, where understanding human behavior and interaction is critical. This paper presents the first exploration of using large participation activities to facilitate experiential learning to bridge this gap. We report on a day-long resource allocation scenario involving up to 43 participants, designed to simulate multiagent interactions under pressure and with meaningful stakes, where learners can apply their theoretical knowledge to analyze and solve emerging problems. We propose ``megagames'' as a powerful pedagogical tool not only for multiagent systems, but also for other domains as well.