A generalized many‐objective optimization approach for scenario discovery

Scenario discovery is a model‐based approach for scenario development, aimed at finding one or more subspaces within the uncertainty space associated with a model that is decision‐relevant. These identified subspaces can subsequently be translated into narratives or shared in other ways a broader participatory process. Finding such as subspace involves solving a three‐objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, focus only on finding a subspace that minimizes the number of nondecision relevant model runs. Adopting a single objective optimization approach for a many‐objective optimization problem implies that the full trade‐off space is not identified. In this paper, we introduce a many‐objective optimization approach for scenario discovery. We compare this with an improved usage of Patient Rule Induction Method (PRIM) for identifying the multidimensional trade‐offs amongst coverage, density, and interpretability. We find that the many‐objective optimization approach produces results which slightly dominate those of the improved version of PRIM on all three objectives. Qualitatively, however, both approaches identify essentially the same subspaces. The prime benefits of the many‐objective optimization approach are its potential in bringing additional scenario relevant concerns such as consistency or diversity into the scenario discovery framework, as well as its ability to avoid overfitting. Potentially more important, it also paves the way for future work on using more sophisticated metaheuristic optimization approaches for scenario discovery.