This study employs a Markov chain model of vegetation dynamics to examine the economic and ecological benefits of post-fire revegetation in the Great Basin sagebrush steppe. The analysis is important because synergies between wildland fire and invasive weeds in this ecosystem are likely to result in the loss of native biodiversity, less predictable forage availability for livestock and wildlife, reduced watershed stability and water quality, and increased costs and risk associated with firefighting. The analysis is based on a parameterized state-and-transition model of vegetation change for Wyoming big sagebrush community in the Great Basin sagebrush steppe. This conceptual model was formulated into a quantitative, predictive model by implementing it as a Markov chain process that links vegetation change, management, and costs. Simulation results were used to develop cost curves for achieving ecological goals and to evaluate uncertainty in future vegetation conditions. The Markov chain model shows that post-fire revegetation using either a native seed mix or crested wheatgrass was more effective than no revegetation for achieving ecosystem objectives. Further, post-fire revegetation with either seed mix cost less than no revegetation because of resulting reductions in fire suppression costs. Consequently, post-fire revegetation makes both ecological and economic sense, and the choice of seed mix should depend on the prioritization of management objectives. Identifying the economic and ecological tradeoffs of different management strategies should enable improved management of the sagebrush-steppe, and Markov processes provide a straight-forward method for identifying these trade-offs.