Scenario Discovery is a widely used method in model-based decision support for identifying common input space properties across ensembles of exploratory model runs. For model runs with behavior over time, these properties are identified by reducing each run to a single value, which obscures potentially decision-relevant dynamics. We address the problem of considering dynamics in Scenario Discovery by applying time series clustering to the ensemble of model runs, and then finding the common input properties for each cluster. This separates the input space into multiple scenarios, each corresponding to a distinct model dynamic. Policy interventions can be targeted at different scenarios by analyzing overlap of these subspaces. Our work expands Scenario Discovery by improving consideration of system behavior over time, which is highly relevant for the management of complex nonlinear systems such as ecosystems or technical infrastructure.

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Between 2013 and 2015, over one million migrants entered Europe. Many European countries were unprepared for and overwhelmed by the rapidly unfolding, chaotic situation. To better handle future migration waves, and allow quicker exploration of government policy effects and interactions, a geospatial-based system dynamics model of migration flows across Europe was developed together with the national police of a major EU country. The model, built around subscripted data and vector operations, distributes migration inflows across the continent based on dynamic mechanisms such as societal stress, social group pull and country attractiveness. It is lightweight yet versatile, and is useful for exploratory, early-stage policy modelling, especially when investigating the system-wide effects and interactions of national migration policies.@en