The assumptions at the beginning of a trade space exploration are that the decision makers have a model of some complex engineered system that relates design variables to performance and cost metrics, they know what the inputs and outputs to the model are, and they know they will be forming a preference over some subset of the inputs and outputs. What they do not know is the relationship between the inputs and outputs (in exact or an intuitive sense), the feasible range of inputs and outputs, the subset of inputs and outputs they will form their preference on, or the exact form of the preference. These assumptions probably form the least informative starting point for trades. To conduct the trade study the model is tied to an Exploration Engine, which initially randomly exercises the model, creating different system concepts. A user simultaneously visually explores the trade space in real time as it emerges using multi-dimensional data visualization tools and then visually steers further model runs to desired trade space regions of interest by specifying attractors in the trade space, such as desired inputs, outputs, preference functions, Pareto frontier. To ground the presentation, the paper uses a satellite design model, which relates design and performance variables to form a multi-dimensional trade space for satellite configurations. The trade space is discontinuous and complex, and presents a suitable test case.