Acquisition of "quantitative" models of sufficient accuracy to enable effective analysis of requirements tradeoffs is hampered by the slowness and difficulty of obtaining sufficient data. "Qualitative" models, based on expert opinion, can be built quickly and therefore used earlier. Such qualitative models are nondeterminate which makes them hard to use for making categorical policy decisions over the model. The nondeterminacy of qualitative models can be tamed using "stochastic sampling" and "treatment learning". These tools can quickly find and set the "master variables" that restrain qualitative simulations. Once tamed, qualitative modeling can be used in requirements engineering to assess more options, earlier in the life cycle.