Scenario discovery is a novel model-based approach to scenario development in the presence of deep uncertainty. Scenario discovery frequently relies on the Patient Rule Induction Method (PRIM). PRIM identifies regions in the model input space that are highly predictive of producing model outcomes that are of interest. To identify these, PRIM uses a lenient hill climbing optimization procedure. PRIM struggles when confronted with cases where the uncertain factors are a mix of data types, and can be used only for binary classifications. We compare two more lenient objective functions which both address the first problem, and an alternative objective function using Gini impurity which addresses the second problem. We assess the efficacy of the modification using previously published cases. Both modifications are effective. The more lenient objective functions produce better descriptions of the data, while the Gini impurity objective function allows PRIM to be used when handling multinomial classified data. This paper makes use of the Exploratory Modeling Workbench, available via https://github.com/quaquel/EMAworkbench. Section 3.4 relies on extensions to classes available in the workbench. These extensions are provided as supplementary material. The detailed code with rudimentary documentation is provided in the form of 3 pdf representations of the underlying IPython notebooks. 
Improving scenario discovery for handling heterogeneous uncertainties and multinomial classified outcomes