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Many of today's most pressing policy challenges are usefully characterized as wicked problems. With contested framings parties to a decision disagree not only on potential solutions, but on the nature of the problem they are trying to solve. The quantitative tools of risk and policy analysis, commonly designed to develop and compare choices within a single decision framing, are poorly designed to bring quantitative information into debates with contested framings. This study aims to build on recent advances in decision making under deep uncertainty (DMDU) to demonstrate methods and tools that may help resolve the tension between quantitative decision support and multiworldview approaches for addressing wicked problems. The study employs robust decision making (RDM), one common DMDU method, and a new version of the lake model, a simple and widely used model of a coupled human and natural system, to conduct a stylized analysis that reflects three different worldviews. The RDM analysis solves the decision challenge independently for each worldview and then compares each set of solutions from the vantage of the other worldviews. The resulting utopia–dystopia matrix informs problem reframing that seeks robust, adaptive strategies independently consistent with each worldview and thus provides a locus for agreement. The study describes how stakeholder engagements might use such analytic tools and their information products to provide overlapping but alternative entry points for groups with fundamentally different worldviews to engage with each other in deliberative processes appropriate for wicked problems. [ABSTRACT FROM AUTHOR]

The scientific community is now developing a new set of scenarios, referred to as Shared Socio-economic Pathways (SSPs) that will be contrasted along two axes: challenges to mitigation, and challenges to adaptation. This paper proposes a methodology to develop SSPs with a 'backwards' approach based on (i) an a priori identification of potential drivers of mitigation and adaptation challenges; (ii) a modelling exercise to transform these drivers into a large set of scenarios; (iii) an a posteriori selection of a few SSPs among these scenarios using statistical cluster-finding algorithms. This backwards approach could help inform the development of SSPs to ensure the storylines focus on the driving forces most relevant to distinguishing between the SSPs. In this illustrative analysis, we find that energy sobriety, equity and convergence prove most important towards explaining future difference in challenges to adaptation and mitigation. The results also demonstrate the difficulty in finding explanatory drivers for a middle scenario (SSP2). We argue that methodologies such as that used here are useful for broad questions such as the definition of SSPs, and could also be applied to any specific decisions faced by decision-makers in the field of climate change. [ABSTRACT FROM AUTHOR]

This article provides a concise overview of methods for analyzing policy choices that have been used in the study of long-term environmental challenges. We open with an overview of the broad classes of methods used for long-term policy analysis, and subsequent sections will describe in depth three particular methods. They are: statistical models, such as employed in the debate on the environmental Kuznets curve, which infer past patterns from data and project them into the future; robust decision-making, a decision analytic framework that supports choices under deep uncertainty, and relates near-term policy interventions to different clusters of long-term environmental futures; and adaptive control and agent-based modeling, which provide an approach to simulation modeling that focuses on cooperation and conflict among multiple actors and their choice of strategies. While all three approaches can be used for various applications, this article focuses on the challenge of a potential transition to a low-carbon future to illustrate the strengths, weaknesses, and synergies among the respective methods. In the final section, we offer guidance for choosing among methods. [ABSTRACT FROM AUTHOR]