In this chapter, we discuss and demonstrate the use of ‘on the spot’ and ‘on the map’ scenario exploration and policy-support in workshop settings. First we justify the need for exploratory model-based policy workshops. Then we present some methods and techniques needed for these workshops. Special attention is paid to new techniques we believe are crucially needed for this kind of interactive workshop if time is of the essence, namely (1) techniques to quickly generate small but diverse ensembles of alternative scenarios, and (2) techniques to visualize whole-system dynamics on maps by means of geospatial animations. We subsequently describe a workshop related to the 2015–2016 European refugee crisis for which this approach and these techniques were developed and used. Finally, we discuss shortcomings and improvements to deal with these shortcomings and conclude.@en

Nowadays, decision-makers face deep uncertainties from a myriad of external factors such as climate change, population growth, new technologies, and economic developments. The challenge is to develop robust policies, which perform well across all possible resolutions of the uncertainties. One approach for achieving this is to design a policy to be adapted over time in response to how the future actually unfolds. A key determinant for the efficacy of such an adaptive policy is the specification of when and how to adapt it. This specification depends on how robustness is being operationalized. To date, there is little guidance for selecting an appropriate robustness metric. In this chapter we address this problem, using a case study of designing a policy for stimulating the transition of the European energy system towards more sustainable functioning using five different robustness metrics. We compare the policies as identified by each metric and discuss their relative merits. We highlight that the different robustness metrics emphasize different aspects of what makes a policy robust. More specifically, measures that separate dispersion and the mean, effectively doubling the number of objectives, provide very valuable information on the trade-offs between the mean performance of the policy and dispersion around this mean. We also discuss, based on our case, why analysts should use multiple robustness metrics.

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During the first months, the 2014 outbreak of the Ebola virus (EBOV) in West Africa was characterised by inadequate intervention capacities. In this paper, we investigate (1) the influence of limited but dynamic intervention capacities and their effect on the effective reproduction number, and (2) the effects of proactive versus reactive intervention approaches. We use a transmission model extended with dynamical intervention capacities. Taking into account a bandwidth for potential over- and under-reporting in reported Ebola virus disease cases, the model is used to generate ensembles of plausible scenarios. Next, it is used for testing the effectiveness of more proactive approaches in extending intervention capacities across these scenarios. We show that reactive approaches in extending intervention capacities can lead to continued under-capacity, and, consequently, to an increase of the effective reproduction number and to accelerated EBOV transmission. Proactive approaches, which take deployment delays, doubling times of diseases, and potential under-reporting of the number of cases into account, help in limiting the total number of cases and deaths if the effective reproduction number in isolation is lower than the effective reproduction number outside of isolation. If the effective reproduction number in isolation is higher, proactive intervention policies still outperform reactive intervention policies.@en

While the shale revolution was largely a US’ affair, it affects the global energy system. In this paper, we look at the effects of this spectacular increase in natural gas, and oil, extraction capacity can have on the mix of primary energy sources, on energy prices, and through that on internal political stability of rentier states. We use two exploratory simulation models to investigate the consequences of the combination of both complexity and uncertainty in relation to the global energy system and state stability. Our simulations show that shale developments could be seen as part of a long term hog-cycle, with a short term drop in oil prices if unconventional supply substitutes demand for oil. These lower oil prices may lead to instability in rentier states neighbouring the EU, especially when dependence on oil and gas income is high, youth bulges are present, or buffers like sovereign wealth funds are too limited to bridge the negative economic effects of temporary low oil prices.

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This paper introduces an approach to compare simulation runs from multiple System Dynamics simulation models. Three dynamic hypotheses regarding the uncertain evolutions of long-term copper availability are introduced and used to illustrate the new approach. They correspond to three different perspectives on the copper system (global top-down, global bottom-up, and regional top-down). Although each of these models allows to generate a wealth of behavioural patterns, the focus in this paper is on the differences in trajectories caused by different models for identical values of shared parameters and identical settings of other assumptions, not on differences in behavioural patterns caused by each of the models. Hence, differences in trajectories between the three models are identified, quantified, and classified based on a quantified measure of difference. For these models, small differences between the trajectories are only found in stable runs, while the alternative perspectives are largely responsible for medium to large differences. Hence, it is concluded that multiple dynamic hypotheses may have to be modelled when dealing with uncertain issues.@en