Scenario discovery translates large simulation ensembles into interpretable input regions linked to policy-relevant outcomes. While previous studies have compared scenario discovery algorithms, they were ad hoc and hard to reproduce. We propose a general workflow to evaluate rule induction methods for scenario discovery. The workflow (i) provides synthetic benchmarks that expose axis and directional misalignment, nonlinearity, boundary fuzziness, and dimensional noise; (ii) unifies metrics and diagnostics around coverage–density trade-offs, interpretability, runtime, and scaling; and (iii) prescribes a staged experiment design from low-dimensional screening to stress testing. We illustrate the approach by comparing established algorithms PRIM and CART with an oblique decision tree variant called HHCART(D), finding that the latter does not outperform the former. Our workflow surfaces method-specific trade-offs and supports principled, reproducible algorithm selection for scenario discovery.

The Delft Method for System Dynamics (SD) is a proven method for learning basic SD. The method focuses on learning by doing: first you try to work through an exercise, and if you do not understand something, then you can look up the theory. The book contains exercises on topics such as causal loop diagrams, delays, and when SD is an appropriate methodology. It also contains modelling exercises that show students how to build low to medium complexity models, and how to use these models for policy analysis. The theory chapters cover all phases of the modelling cycle: problem articulation, conceptualisation, formulation, evaluation (including validation and scenario analysis), and policy analysis. This book is intended for students and teachers in large or small System Dynamics courses, and for motivated students that want to learn SD at their own pace.

Scenarios are commonly used for decision support and future exploration of complex systems. Using simulation models to generate these scenarios, called scenario discovery, has received increased attention in the literature as a principled method of capturing the uncertainty, complexity, and dynamics inherent in such problems. However, current methods of incorporating dynamics into scenario discovery are limited to a single outcome of interest. Furthermore, there is little work on the post-generation evaluation of the generated scenarios. In this work, we extend scenario discovery to multiple dynamic outcomes of interest, and present a number of visual and statistical approaches for evaluating the resulting scenario sets. These innovations make model-based scenario generation more widely applicable in decision support for complex societal problems, and open the door to multimethod scenario generation combining model-based and model-free methods such as Intuitive Logics or futures cones.

An increased interest in the resilience of complex socio-ecological and socio-technical systems has led to a variety of metrics being proposed. An overview of these metrics and their underlying concepts would support identifying useful metrics for applications in science and engineering. This study undertakes a scoping review of resilience metrics for systems straddling the societal, ecological, and technical domains to determine how resilience has been measured, the conceptual differences between the proposed approaches, and how they align with the domains of their case studies. We find that a wide variety of resilience metrics have been proposed in the literature. Conceptually, ten different quantification approaches were identified. Four different disturbance types were observed, including sudden, continuous, multiple, and abruptly ending disturbances. Surprisingly, there is no strong pattern regarding socio-ecological systems being studied using the “ecological resilience” concept and socio-technical systems being studied using the “engineering resilience” concept. As a result, we recommend that researchers use multiple resilience metrics in the same study, ideally following different conceptual approaches, and compare the resulting insights. Furthermore, the used metrics should be mathematically defined, the included variables explained and their units provided, and the chosen functional form justified.

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.

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.