Sustainable development is a long-term endeavour involving deep uncertainty and requiring transformative change at multiple scales. To navigate such a grand challenge, new approaches have been developed in various academic fields, such as Policy Analysis and Sustainability Transitions. Two prominent approaches to strategic planning within these two fields are Decision Making under Deep Uncertainty (DMDU) and Transition Management (TM). While DMDU provides analytical concepts and tools to prepare for change (one that happens anyway, whether or not we desire it), TM offers a governance approach to condition change (one that we desire). We argue that the sustainable development agenda could benefit from an explicit cross-fertilisation across the two approaches. We will highlight the commonalities and differences between the two approaches and reflect on potential cross-connections. We argue that DMDU can benefit from the participatory process of TM, and its interventionist approach, which helps to mobilise actors and build networks for sustainability transformations. DMDU can also learn from some of the governance instruments offered by TM, such as visioning, experimentation, and social learning to better prepare for change that can only be dealt with through transformative actions. TM, on the other hand, can be enriched by analytical concepts and tools developed by and widely used in DMDU, such as tipping points and signposts, exploratory scenarios, and Exploratory Modelling, to operationalise transition pathways into actionable policy decisions. An illustrative example is used to demonstrate what a cross-connection between the two approaches might look like.

Policymakers need to make policies for unknown and uncertain futures. Researchers in the futures field have a great deal to contribute to the policymaking process. But, futures research is often neglected as an element of policymaking. The aim of this paper is to improve the link between futures research and policymaking. More specifically, as Policy Analysis has a strong link with policymaking, this paper explores the possibility of linking Policy Analysis to the futures field through the use of an uncertainty typology applied in Policy Analysis. The typology can be used to structure the various forward-looking disciplines (or subfields) of the futures field according to the level of uncertainty that they address. This linkage can add significantly to the use of futures research in policymaking.

Glynn et al. (2017, https://doi.org/10.1002/2016EF000487) note the importance of engaging stakeholders in the process of public policymaking and analysis. In particular, they highlight the central role biases, beliefs, heuristics, and values play in such engagement. However, the framework they propose neglects uncertainty, which significantly restricts any ability to engage effectively with BBHV. We show how their paper's narrow view can be widened to include aspects of risk and uncertainty.

A variety of model-based approaches for supporting decision-making under deep uncertainty have been suggested, but they are rarely compared and contrasted. In this paper, we compare Robust Decision-Making with Dynamic Adaptive Policy Pathways. We apply both to a hypothetical case inspired by a river reach in the Rhine Delta of the Netherlands, and compare them with respect to the required tooling, the resulting decision relevant insights, and the resulting plans. The results indicate that the two approaches are complementary. Robust Decision-Making offers insights into conditions under which problems occur, and makes trade-offs transparent. The Dynamic Adaptive Policy Pathways approach emphasizes dynamic adaptation over time, and thus offers a natural way for handling the vulnerabilities identified through Robust Decision-Making. The application also makes clear that the analytical process of Robust Decision-Making is path-dependent and open ended: an analyst has to make many choices, for which Robust Decision-Making offers no direct guidance.