In this paper, we introduce an agent-based model of lost person behavior that may be used to improve current methods for wilderness search and rescue (SAR). The model defines agents moving on a landscape with behavior considered as a random variable. The behavior uses a distribution of four known lost person behavior strategies in order to simulate possible trajectories for the agent. We simulate all possible distributions of behaviors in the model and compute distributions of horizontal distances traveled in a fixed time. By comparing these results to analogous data from a database of lost person cases, we explore the model’s validity with respect to real-world data.