The Marine Corps' Project Albert seeks to model complex phenomenon by observing the behavior of relatively simple simulations over thousands of runs. A rich data base is developed by running the simulations thousands of times, varying the agent and scenario input parameters as well as the random seeds. Exploring this result space may provide significant insight into nonlinear, surprising, and emergent behaviors. Capturing these results can provide a path for making the results usable for decision support to a military commander. This paper presents two data mining approaches, rule discovery and Bayesian networks, for analyzing the Albert simulation data. The first approach generates rules from the data and then uses them to create descriptive model. The second generates Bayesian Networks which provide a quantitative belief model for decision support. Both of these approaches as well as the Project Albert simulations are framed in the context of a system architecture for decision support.