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.

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.

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.

This chapter aims to close the gap between the theory and practice of Dynamic Adaptive Planning (DAP).

Rather than treating the plays as objects to be studied, described and interpreted, Engagements with Shakespearean Drama examines precisely what about Shakespeare's plays is so special – why they continue to be discussed and performed all around the world. This book highlights the importance of our experience as readers and audiences and argues that what makes the plays great is that they cause a wide range of intense, pleasurable and valuable experiences. This highly personal and emotive approach allows students to engage with the plays on a new level, taking their own responses seriously as grounds for assessing the plays' success and quality. The book also engages with the essential criticism of the plays from Shakespeare's time to our own, equipping students to engage in contemporary debates about the nature and achievement of Shakespearean drama.

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.

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.

This chapter describes a methodology that policymakers can use to overcome the uncertainties that hinder Intelligent Transport System (ITS) implementation and that can enable them to start to implement ITS despite these uncertainties and the inherently uncertain future. It explains how Adaptive Policymaking (APM) can be used to deal with uncertainty regarding acceptance. This chapter illustrates how to use APM to design adaptive policies using two real-world ITS examples. One based on desk research, the other based on participative research. APM specifies a series of generic steps for decision-making under uncertainty that can be used to design an adaptive policy. The potential of APM has been demonstrated by various researchers using transportation cases that reflect real-world policy problems. ITS is highly promising when it comes to achieving transportation policy goals. A lot of research has been performed on Intelligent Speed Adaptation and Personal Intelligent Travel Assistance acceptance.

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.

By 2050, about two-thirds of the world’s people are expected to live in urban areas. But, the economic viability and sustainability of city centers is threatened by problems related to transport, such as pollution, congestion, and parking. Much has been written about automated vehicles and demand responsive transport. The combination of these potentially disruptive developments could reduce these problems. However, implementation is held back by uncertainties, including public acceptance, liability, and privacy. So, their potential to reduce urban transport problems may not be fully realized. We propose an adaptive approach to implementation that takes some actions right away and creates a framework for future actions that allows for adaptations over time as knowledge about performance and acceptance of the new system (called ‘automated taxis’) accumulates and critical events for implementation take place. The adaptive approach is illustrated in the context of a hypothetical large city.

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, milieubelasting, verkeersveiligheid en ruimtegebruik, doen toenemen. Traditionele oplossingen worden onvoldoende geacht om deze problemen te lijf gaan. De hoop is gevestigd op implementatie van innovatieve ontwikkelingen zoals zelfrijdende auto’s en vraaggestuurd transport. De combinatie van deze disruptieve ontwikkelingen 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. In dit artikel wordt daarom een adaptieve implementatie-benadering voorgesteld en geillustreerd voor een innovatief transportsysteem voor steden: zelfrijdende taxi’s. Volgens deze adaptieve benadering worden eerst zelfrijdende taxi’s met chauffeur geimplementeerd en allerlei adaptieve maatregelen voorbereid die in de toekomst geimplementeerd kunnen worden naarmate kennis over prestaties en acceptatie van zelfrijdende taxi’s toeneemt en kritieke ontwikkelingen voor verdere implementatie plaatsvinden. De adaptieve benadering wordt geillustreerd voor de stad Amsterdam.

In many planning problems, planners face major challenges in coping with uncertain and changing physical conditions, and rapid unpredictable socioeconomic development. How should society prepare itself for this confluence of uncertainty? Given the presence of irreducible uncertainties, there is no straightforward answer to this question. Effective decisions must be made under unavoidable uncertainty (Dessai et al. 2009; Lempert et al. 2003). In recent years, this has been labeled as decision making under deep uncertainty. Deep uncertainty means that the various parties to a decision do not know or cannot agree on the system and its boundaries; the outcomes of interest and their relative importance; the prior probability distribution for uncertain inputs to the system (Lempert et al. 2003; Walker et al. 2013); or decisions are made over time in dynamic interaction with the system and cannot be considered independently (Haasnoot et al. 2013a, b; Hallegatte et al. 2012). From a decision analytic point of view, this implies that there are a large number of plausible alternative models, alternative sets of weights to assign to the different outcomes of interest, different sets of inputs for the uncertain model parameters, and different (sequences of) candidate solutions (Kwakkel et al. 2010). Decision making under deep uncertainty is a particular type of wicked problem (Rittel and Webber 1973). Wicked problems are problems characterized by the involvement of a variety of stakeholders and decision makers with conflicting values and diverging ideas for solutions (Churchman 1967). What makes wicked problems especially pernicious is that even the problem formulation itself is contested (Rittel and Webber 1973). System analytic approaches presuppose a separation between the problem formulation and the solution. In wicked problem situations this distinction breaks down. Solutions and problem formulation are intertwined with each other. Depending on how a problem is framed, alternative solutions come to the fore; and, vice versa, depending on the available or preferred solutions, the problem can be framed differently. Even if there is agreement on the difference between observed and desired outcomes, rival explanations for the existence of this difference are available, and, hence, different solutions can be preferred. An additional factor adding to the wickedness is that decision makers can ill afford to be wrong. The consequences of any decision on wicked problems can be profound, difficult if not impossible to reverse, and result in lock-ins for future decision making. Planning and decision making in wicked problem situations should, therefore, be understood as an argumentative process: in which the problem formulation, a shared understanding of system functioning and how this gives rise to the problem, and the set of promising solutions, emerge gradually through debate among the involved decision makers and stakeholders (Dewulf et al. 2005). When even the problem formulation itself is uncertain and contested, planning and decision making requires an iterative approach that facilitates learning across alternative framings of the problem, and learning about stakeholder preferences and trade-offs, all in pursuit of a collaborative process of discovering what is possible (Herman et al. 2015). Modeling and optimization can play a role in facilitating this learning. They can help in discovering a set of possible actions that is worth closer inspection, and make the trade-offs among these actions more transparent (Liebman 1976; Reed and Kasprzyk 2009). Under the moniker of decision making under deep uncertainty, a variety of new approaches and tools are being put forward. Emerging approaches include (multiobjective) robust decision making (Kasprzyk et al. 2013; Lempert et al. 2006), info-gap decision theory (Ben Haim 2001), dynamic adaptive policy pathways (Haasnoot et al. 2013a, b), and decision scaling (Brown et al. 2012). A common feature of these approaches is that they are exploratory model-based strategies for designing adaptive and robust plans or policies. Although these frameworks are used in a wide variety of applications, they have been most commonly applied in the water domain, in which climate change and social change are key concerns that affect the long-term viability of current management plans and strategies. Liebman (1976) recognized that water resources planning problems are wicked problems in which modeling, simulation, and optimization cannot be straightforwardly applied. In recent years, this observation has been reiterated (Herman et al. 2015; Lund 2012; Reed and Kasprzyk 2009). If decision making under deep uncertainty is a particular type of wicked problem, to what extent do the recent methodological advances address some of the key aspects of what makes wicked problems wicked? To answer this question, the authors look at two exemplary approaches for supporting decision making under deep uncertainty: (multiobjective) robust decision making and dynamic adaptive policy pathways. This article first briefly outlines each approach, and then discusses some of the ongoing scientific work aimed at integrating the two approaches. This sets the stage for a critical discussion of these approaches and how they touch on the key concerns of supporting decision making in wicked problem situations.

Resource lack is a persisting condition for public agencies in the 1980s. Court and other justice agency managers, like their executive branch counterparts, are trying to cope with diminished appropria tions. Two research studies and a court improvement project evaluation that were concluded earlier in this decade may help in understanding and dealing with the effects of cutback and in outlining some judicial administration innovations that may expedite the work of courts, while holding the line or reducing costs. The first, The Impact of Fiscal Limitation on Californias Criminal Justice System, analyzes what happened as a result of the adoption of Proposition 13 in 1978. All facets of the criminal justice process from law enforcement to corrections are covered, but this review, except for some general observations, is limited to courts and those closely-linked

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.

Adaptation of existing infrastructure is a response to climate change that can ensure a viable, safe, and robust transportation network. However, deep uncertainties associated with climate change pose significant challenges to adaptation planning. Specifically, current transportation planning methods are ill-equipped to address deep uncertainties, as they rely on designing responses to a few predicted futures, none of which will occur exactly as envisioned. In this paper, we propose using dynamic adaptive planning (DAP), an emerging general strategic planning method, to account for deep uncertainties by building flexibility and learning mechanisms into plans that enable continuous adaptation throughout implementation. This paper first reviews uncertainty in general, introduces what is meant by deep uncertainty, and then introduces DAP. Then, DAP is applied to a case study of the Oakland approach to the San Francisco-Oakland Bay Bridge, which was initially assessed under the 2010–2011 FHWA Climate Change Vulnerability Assessment Pilot program, to illustrate how DAP could be applied as a response to climate change in the context of evolving transportation infrastructure adaptation planning practices in the United States. We conclude that DAP is well suited to account for the deep uncertainties of climate change in transportation and infrastructure planning, and provide suggestions for further research to better apply DAP in this field.

We suggest that those involved with environmental and water resources planning and management need to consider the social responses as well as the economic and environmental impacts of our decisions. But predicting such responses now, and especially in the future, will not be possible. All we know about the future is that it will differ from the present. This includes the goals or objectives society wishes to achieve. But, in spite of this, decisions have to be made today that shape or influence the future we think we will want. What can policymakers do to better insure that a policy is able to achieve society’s objectives over time? One way is to include members of society (or at least their behavior) in the policymaking process. Another way is to make the policy adaptive and include monitoring and learning so that new actions can be taken when conditions change. That is, policies should include provisions for being prepared for changes. In this paper we offer a conceptual model of a coupled social-water system, each component influencing decisions that affect the other over time. We give some examples of just how hard it is to attempt predictions, why, and what can go wrong if those predictions are wrong. And we offer some analytic approaches to policymaking that do not depend on such predictions. These approaches include the use of (1) agent-based models that simulate the behavior of individual or collective entities, (2) stakeholders who represent the social component interacting with computer models of the water systems within a decision support framework or within the framework of a smart game, or (3) dynamic adaptive policies, which rely on monitoring and adapting rather than implementing a fixed policy. All of these approaches can be used to gain insights and understanding of the coupled social and natural components of water resource systems.