Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on ‘prepare, monitor, and adapt’, enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.@en

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.

@en

Policymakers need to make policies for an uncertain future, and policy analysts assist policymakers in choosing preferred courses of action. Despite a longstanding recognition that the futures field can contribute a great deal to policy analysis, futures work is not used to its full potential as an element of policy analysis. This is partly due to an absence of well-defined links between the fields and a common unambiguous typology. This paper proposes a framework for linking policy analysis, policymaking, and the futures field so that they can benefit mutually from a shared approach and tools. This integrated framework is intended to guide policy analysts on the appropriate use of futures approaches so that they can improve their analyses and contribute to better policies. At the same time, futures practitioners will be encouraged to align their approaches with the needs of policy analysts, thereby leading to increased uptake of futures work in policy analysis.@en

Here we examine whether a study conducted 25 years ago (1992) would have had different conclusions if concepts and analytical methods developed since then had been used. The 1992 problem was to identify a strategy for reducing flood risk in the Netherlands by, for example, strengthening the river dikes against the risk of flooding. Since then, conditions related to flooding have been recognised as increasingly uncertain. In response, a new paradigm for strategic planning has emerged: the “adaptive planning approach,” which aims to identify and assess strategies allowing for change, learning, and adaptation over time. We found that using the adaptive planning approach in 1992 would not have changed the main conclusions. But, it would have made explicit the need for the identification of vulnerabilities of the chosen strategy, a monitoring system to keep track of the uncertainties, and the possible actions to deal with the vulnerabilities that can be taken as the world evolves.@en

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.

@en

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.@en

Er wordt verwacht dat rond 2050 ongeveer 2/3 van de wereldbevolking in steden zal wonen. Een dergelijke verstedelijking zal transport-gerelateerde problemen, zoals bereikbaarheid, milieubelastmg, verkeersveiligheid en ruimtegebruik, doen toenemen. Traditionele oplossingen worden onvoldoende geacht om deze problemen te lijf te gaan. De hoop is gevestigd op implementatie van innovatieve ontwikkelingen zoals zelfrijdende auto's en vraaggestuurd transport. De combinatie van deze disruptieve ontwikkelmgen kunnen toekomstige stedelijke transport problemen significant reduceren. Echter, de implementatie van dergelijke innovaties worden gehinderd door allerlei onzekerheden zoals bijv. maatschappelijke acceptatie, wettelijke aansprakelijkheid en privacy. Deze onzekerheden zorgen ervoor dat de implementatie van deze innovaties niet of maar langzaam gerealiseerd wordt. Traditionele, scenario-gebaseerde benaderingen zijn onvoldoende om met deze onzekerheden om te gaan. I n dit artikel wordt daarom een adaptieve implementatie-benadering voorgesteld en geülustreerd voor een innovatief tiansportsysteem voor steden: zelfrijdende taxi's. Volgens deze adaptieve benadering worden eerst zelfrijdende taxi's met chauffeur geïmplementeerd en aherlei adaptieve maatregelen voorbereid die hl de toekomst geïmplementeerd kunnen worden naarmate de kennis over de prestaties en acceptatie van zelfrijdende taxi's toeneemt en kritieke ontwikkelingen voor verdere implementatie plaatsvinden. De adaptieve benadering wordt geïllustreerd voor de stad Amsterdam. @en

A changing climate and ongoing socio-economic developments are putting severe stress on water management practices in many deltaic countries in the world, including the Netherlands. To address this, the Dutch initiated a new Delta Program, which aimed at developing a strategy for keeping the Netherlands safe from floods and droughts during this century. The challenge faced by the Delta Program is how to proactively address deep uncertainty in the long-term management of the Dutch Delta. Future climatological conditions, socio-economic developments, land use patterns, and social preferences are deeply uncertain, interconnected, changing dynamically over time, and co-evolve with each other and any water management actions that are taken. In light of this, existing predictive and scenario-based approaches were deemed inadequate. Instead, an adaptive planning approach called dynamic adaptive policy pathways emerged. This approach explicitly considers that adaptation decisions are taken over time. Therefore, we need to explore the future and sequences of decisions (adaptation pathways). It emphasizes that any plan should commit to short-term actions, specify a range of long-term options, and establish a monitoring framework for guiding the implementation of these options if and when necessary. In order to develop such adaptation pathways, the way in which models are used is changed from prediction to exploration. This adaptive approach, applied in the Netherlands under the label of Adaptive Delta Management, is now being adopted in a wide variety of countries. In this talk, we will introduce the adaptation pathways approach and discuss how it emerged in the Netherlands. We will also present various examples of how the design of adaptation pathways, and the decision making using them, can be supported using exploratory modeling approaches, simulation gaming, and game structuring techniques. Examples will be drawn from the Netherlands as well as from other countries @en

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.

@en