Technological innovation is almost always investigated from an economic perspective; with few exceptions, the specific technological and social nature of innovation is often ignored. We argue that a novel way to characterise and make sense of different types of technological innovation is to start considering uncertainty. This seems plausible since technological development and innovation almost always occur under conditions of uncertainty. We rely on the distinction between, on the one hand, uncertainty that can be quantified (e.g. probabilistic risk) and, on (...) the other, deep forms of uncertainty that may resist the possibility of being quantified (e.g. severe or fundamental uncertainties). On the basis of these different ingredients of uncertainty in technological innovation, we propose a new taxonomy that reveals the technological nature of innovation. Unlike previous taxonomies employed to handle different types of technological innovations, our taxonomy does not consider the economic value of innovation alone; it is much more oriented towards societal preferences and forms of technological uncertainty. Finally, we investigate the coherence of our proposal with the dual nature of technological artefacts, showing that innovation can be grounded on structural and functional factors and not just on economic ones. (shrink)

Decision-making under uncertainty is sometimes investigated as a homogeneous problem, independently of the type of decision-maker and the level and nature of the decision itself. However, when the decision-maker is a public authority, there immediately arise problems additional to those that concern any other (private) decision-maker. This is not always clearly recognised in orthodox discussions on decisions under conditions of uncertainty. This article investigates the methodological, strategic and procedural challenges of taking public decisions in such conditions. It focuses mainly on (...) decisions involving urban contexts, such as planning decisions regarding land use and building transformations, by trying to develop some pioneering research studies in this field. (shrink)

Decisions where there is not enough information for a well-informed decision due to unidentified consequences, options, or undetermined demarcation of the decision problem are called decisions under great uncertainty. This paper argues that public policy decisions on _how_ and _if_ to implement decision-making processes based on machine learning and AI for public use are such decisions. Decisions on public policy on AI are uncertain due to three features specific to the current landscape of AI, namely (i) the vagueness of the (...) definition of AI, (ii) uncertain outcomes of AI implementations and (iii) pacing problems. Given that many potential applications of AI in the public sector concern functions central to the public sphere, decisions on the implementation of such applications are particularly sensitive. Therefore, it is suggested that public policy-makers and decision-makers in the public sector can adopt strategies from the argumentative approach in decision theory to mitigate the established great uncertainty. In particular, the notions of framing and temporal strategies are considered. (shrink)