Uncertainty beyond the data

How one theorizes uncertainty is central to how uncertainty is analysed. If uncertainty is conceptualised as incomplete knowledge, uncertainty analysis will focus on describing knowledge gaps. If uncertainty is conceptualised as the challenge of predicting the future, uncertainty analysis will focus on describing how present variables may change in the future. In this post, I argue that a theoretical understanding of uncertainty is necessary to guide decision-making, and that the analysis of uncertainty must be theoretically informed and not only solution-oriented.

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Governance in pluralism

What happens to science advice to policy when science speaks with multiple voices? How does pluralism affect the science policy interface? Is pluralism irreducible or can it be governed? In this post, I review three approaches to governance in the context of pluralism.

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The challenge of development in complex adaptive systems

The complexity view is that economic systems are adaptive systems, becoming systems, self-organising systems. Adaptive systems are not as susceptible to planning as machines may be. Complexity may be useful to discuss the challenges of scaling up the actions and innovations of economic agents and firms to economic growth, and the limits of trickle down logics in the context of persistent and growing inequalities in terms of non-linearities between lower level and higher level system components.

Why complexity matters

Why write a blog about complexity and science for policy? Because complexity matters for science advice to policy. Complex systems may lead to a situation in which science cannot provide all answers. Complexity refers to situations in which “the whole is different from the sum of its parts”, because of emerging properties at different scales of analysis. This situation is challenging for governance, because providing evidence on “the parts” will lead to different decisions than providing evidence on “the whole”.