For generating a design of experiments, the workbench currently iterates over scenarios and policies, creating a set of runs from the itertool.product of these two collections. So if there are 5 policies and 10 scenarios, there are 50 experiments. This is a reasonable approach for most EMA applications.

However, for running experiments to generate a meta-model, it is more efficient to "zip" these: make 50 draws for both policies and scenarios, pair them up, and run 50 experiments.

From the perspective of meta-model development, there is no difference at all between uncertainties and levers -- we merely seek to build a replacement black box for each model structure that converts inputs (both scenarios and policies) to outputs. To build the meta-model, it is often more efficient to have maximum variation in each of the input parameters. By providing a zip-over process, we can increase the variability in the inputs for the same number of experiments.