|Fr 24. März 2023
|Policy makers face the difficult task to make far-reaching decisions that impact the life of the the entire population based on uncertain parameters that they have little to no control
over, such as environmental impacts. Often, they use scenarios in their decision making process. Scenarios provide a common and intuitive way to communicate and characterize different uncertain outcomes in many decision support applications, especially in broad public debates. However, they often fall short of their potential, particularly when applied for groups with diverse interests and worldviews, due to the difficulty of choosing a small number of scenarios to summarize the entire range of uncertain future outcomes. Scenario discovery addresses these problems by using statistical or data-mining algorithms to find easy-to-interpret, policy-relevant regions in the space of uncertain input parameters of computer simulation models. One of many approaches to scenario discovery is subgroup discovery, an approach from the domain of explainable Artificial Intelligence.
In this thesis, we test and evaluate multiple different subgroup discovery methods for their applicabilty to scenario discovery applications.