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

Objectives: In early stages, the consequences of innovations are often unknown or deeply uncertain, which complicates early health economic modeling (EHEM). The field of decision making under deep uncertainty uses exploratory modeling (EM) in situations when the system model, input probabilities/distributions, and consequences are unknown or debated. Our aim was to evaluate the use of EM for early evaluation of health technologies. Methods: We applied EM and EHEM to an early evaluation of minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage and compared these models to derive differences, merits, and drawbacks of EM. Results: EHEM and EM differ fundamentally in how uncertainty is handled. Where in EHEM the focus is on the value of technology, while accounting for the uncertainty, EM focuses on the uncertainty. EM aims to find robust strategies, which give relatively good outcomes over a wide range of plausible futures. This was reflected in our case study. EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a policy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs. Conclusions: EHEM and EM were found to complement each other. EM seems most suited in the very early phases of innovation to explore existing uncertainty and many potential strategies. EHEM seems most useful to optimize promising strategies, yet EM methods are complex and might only add value when stakeholders are willing to consider multiple solutions to a problem and adopt flexible research and adoption strategies.@en

Mobility as a Service (MaaS) involves integration of various forms of public and private transport services into a single mobility service, accessible on demand. For MaaS to become successful, different suppliers of transport services have to cooperate in alliances in order to bring new benefits in the short and long term. Past experience demonstrates that this is a challenge, and existing transport providers are struggling with this cooperation. Various factors, including divergent interests of stakeholders, may limit the formation of such alliances. In this paper, we resort to the theories of alliance formation to extend our understanding of the formation of alliances within MaaS. Based on the economic, sociological and business literature we propose a conceptual model and formulate ten fundamental propositions for alliance formation to offer MaaS systems. The model takes the perspective of business firms for whom the institutional environment is an exogenous influence. We then apply this conceptual alliance-formation-model for a MaaS-pilot in Nijmegen, The Netherlands. Stakeholders within this pilot were interviewed on their conditions for forming an alliance. It appears that shared goals, limited risks for the partners involved, trust, and stimulating public actions are crucial for a successful alliance. For the pilot, however, learning appears to be the main motive for the firms to get involved. For future transition from the exploration to the exploitation phase of MaaS alliance, these results should be taken into account. The framework and the propositions developed in this paper could be adopted as the necessary preconditions for designing a proper governance structure for providing MaaS services.

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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

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.

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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