Decision-makers increasingly need to bring together multiple models across a broad range of disciplines to guide investment and policy decisions around highly complex issues such as population health and safety. We discuss the use of the Smarter Planet Platform for Analysis Simulation of Health (Splash) for cross-disciplinary modeling, simulation, sensitivity analysis, and optimization in the setting of complex systems of systems. Splash is a prototype system that allows combination of existing heterogeneous simulation models and datasets to create composite simulation models of complex systems. Splash, built on a combination of data-integration, workflow management, and simulation technologies, facilitates loose coupling of models via data exchange. We describe the various components of Splash, with an emphasis on the experiment-management component. This latter component uses user-supplied metadata about models and datasets to provide, via an interactive GUI, a unified view over all of the parameters in all of the component models that make up a composite model, a mechanism for selecting the factors to vary, and a means for allowing users to easily specify experimental designs for the selected factors. The experiment manager also provides a mechanism for systematically varying the inputs to the composite models. We show how the experiment manager can be used to implement some simple stochastic-optimization functionality by implementing the Rinott procedure for selecting the best system. We also implement a sensitivity-analysis method based on a fractional-factorial experimental design. We demonstrate this technology via a composite model comprising a financial-rate model and a healthcare payer model. © 2012 IEEE.