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.

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.