In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. From the data collected, visitor behaviors are quantified, modeled, and eventually incorporated into a massive agent-based simulation where up to 15,000 visitor agents are modeled. Finally, we demonstrate how our agent-based simulator can be used to understand the crowd build-up and the impacts of various control policies on visitor experience.