Many-Objective Robust Decision Making (MORDM) is a prominent model-based approach for dealing with deep uncertainty. MORDM has four phases: a systems analytical problem formulation, a search phase to generate candidate solutions, a trade-off analysis where different strategies are compared across many objectives, and a scenario discovery phase to identify the vulnerabilities. In its original inception, the search phase identifies optimal strategies for a single reference scenario for deep uncertainties, which may result in missing locally near-optimal, but globally more robust strategies. Recent work has addressed this issue by generating candidate strategies for multiple policy-relevant scenarios. In this paper, we incorporate a systematic scenario selection procedure in the search phase to consider both policy relevance and scenario diversity. The results demonstrate an increased tradeoff variety besides higher robustness, compared to the solutions found for a reference scenario. Future research can routinize multi-scenario search in MORDM with the aid of software packages.

Several model-based, analytical approaches have been developed recently to deal with the deep uncertainty present in situations for which futures studies are conducted. These approaches focus on covering a wide variety of scenarios and searching for robust strategies. However, they generally do not take the multiplicity of stakeholder perspectives into account in analytic terms, which could bring in diverse opinions and views, not only on possible futures but also on values and interests. In this study, we present an approach to incorporate stakeholder perspectives into model-based scenarios for exploring the future dynamics of the Dutch gas sector. The results demonstrate that the scenario space can be demarcated according to the perspectives. This allows for a systematic comparison of the perspectives and provides a basis for identification of robust strategies. Also, the analysis shows that incompatible elements between the model and perspectives, or within perspectives can be identified. This provides insights about the problem complexity and potential barriers to the futures envisioned by the perspectives. Future research can strengthen this approach by involving stakeholders in modelling and in the model-based representation of the perspective narratives to enhance learning and credibility, and can extend the analysis to identify (socially) robust policies.

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.