The aim of this paper is to provide a conceptual basis for the systematic treatment of uncertainty in model-based decision support activities such as policy analysis, integrated assessment and risk assessment. It focuses on the uncertainty perceived from the point of view of those providing information to support policy decisions (i.e., the modellers' view on uncertainty) -uncertainty regarding the analytical outcomes and conclusions of the decision support exercise. Within the regulatory and management sciences, there is neither commonly shared terminology nor full agreement on a typology of uncertainties. Our aim is to synthesise a wide variety of contributions on uncertainty in model-based decision support in order to provide an interdisciplinary theoretical framework for systematic uncertainty analysis. To that end we adopt a general definition of uncertainty as being any deviation from the unachievable ideal of completely deterministic knowledge of the relevant system. We further propose to discriminate among three dimensions of uncertainty: location, level and nature of uncertainty, and we harmonise existing typologies to further detail the concepts behind these three dimensions of uncertainty. We propose an uncertainty matrix as a heuristic tool to classify and report the various dimensions of uncertainty, thereby providing a conceptual framework for better communication among analysts as well as between them and policymakers and stakeholders. Understanding the various dimensions of uncertainty helps in identifying, articulating, and prioritising critical uncertainties, which is a crucial step to more adequate acknowledgement and treatment of uncertainty in decision support endeavours and more focused research on complex, inherently uncertain, policy issues.

Transitions are transformation processes in which society changes in a fundamental way over a generation or more. Although the goals of a transition are ultimately chosen by society, governments can play a role in bringing about structural change in a stepwise manner. Their management involves sensitivity to existing dynamics and regular adjustment of goals to overcome the conflict between long-term ambition and short-term concerns. This article uses the example of a transition to a low emission energy supply in the Netherlands to argue that transition management provides a basis for coherence and consistency in public policy and can be the spur to sustainable development.

Sustainable development requires changes in socio-technical systems and wider societal change-in beliefs, values and governance. In this article we present a model for managing processes of co-evolution: transition management. Transition management is a multilevel model of governance which shapes processes of co-evolution using visions, transition experiments and cycles of learning and adaptation. Transition management helps societies to transform themselves in a gradual, reflexive way through guided processes of variation and selection, the outcomes of which are stepping stones for further change. It shows that societies can break free from existing practices and technologies, by engaging in co-evolutionary steering. This is illustrated by the Dutch waste management transition. Perhaps transition management constitutes the third way that policy scientists have been looking for all the time, combining the advantages of incrementalism (based on mutual adaptation) with the advantages of planning (based on long-term objectives).

Keywords:complex adaptive systems emergence governance industrial ecology sustainable development transitions SummaryThis article presents a framework, transition management, for managing complex societal systems. The principal contribution of this article is to articulate the relationship between transition management and complex systems theory. A better understanding of the dynamics of complex, adaptive systems provides insight into the opportunities, limitations, and conditions under which it is possible to influence such systems. Transition management is based on key notions of complex systems theory, such as variation and selection, emergence, coevolution, and self-organization. It involves a cyclical process of phases at various scale levels: stimulating niche development at the micro level, finding new attractors at the macro level by developing a sustainability vision, creating diversity by setting out experiments, and selecting successful experiments that can be scaled up.

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