Abstract
Scenario discovery is a model-based approach to scenario development under deep uncertainty. Scenario discovery relies on the use of statistical machine learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method (PRIM). This algorithm identifies regions in an uncertain model input space that are highly predictive of model outcomes that are of interest. To identify these regions, PRIM 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 valleys. 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 using three cases. Each of the cases has been published before and used PRIM. We compare the results found using PRIM with the results found using the improved version of PRIM. We find that the improved version is more robust to new data, can better cope with heterogeneously typed data, and is less prone to overfitting.
Highlights
Scenario discovery (Bryant and Lempert, 2010) is an approach for addressing the challenges of characterizing and communicating deep uncertainty associated with simulation models (Dalal et al, 2013)
The second case is based on the work of Rozenberg et al (2013), where the standard scenario discovery approach was used with discrete uncertain factors
If we look at normal Patient Rule Induction Method (PRIM) on the entire dataset, we see a scenario, which is broadly consistent with the one found using the random boxes approach
Summary
Scenario discovery (Bryant and Lempert, 2010) is an approach for addressing the challenges of characterizing and communicating deep uncertainty associated with simulation models (Dalal et al, 2013). The resulting data set is subsequently analyzed using statistical machine learning algorithms in order to identify regions in the uncertainty space that are of interest (Bryant and Lempert, 2010, Kwakkel et al, 2013). These identified regions, which are typically characterized by only a small subset of the deeply uncertain factors, can subsequently be communicated to the actors involved in the decision problem. Preliminary experiments with real world decision makers suggest that scenario discovery results are decision relevant and easier to interpret for decision makers than probabilistic ways of conveying the same information (Parker et al, 2015)
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