Improving scenario discovery by bagging random boxes

Scenario discovery is a novel participatory model-based approach to scenario development in the presence of deep uncertainty. Scenario discovery relies on the use of statistical machine-learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method. This algorithm identifies regions in the uncertain model input space that are highly predictive of producing model outcomes that are of interest. To identify these regions, PRIM in essence uses a hill climbing optimization procedure. This suggests that PRIM can suffer from the usual defects of hill climbing optimization algorithms, including local optima, plateaus, and ridges and alleys. In case of PRIM, these problems are even more pronounced when dealing with heterogeneously typed data. Drawing inspiration from machine learning research on random forests, we present an improved version of PRIM. This improved version is based on the idea of performing multiple PRIM analyses based on randomly selected features and combining these results using a bagging technique. The efficacy of the approach is demonstrated through a case study of scenario discovery for the transition of the European energy system towards more sustainable functioning, focusing on identifying scenarios where the transition fails.