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It seems paradoxical to suggest that theories of learning might be used to explain policy failure. Yet the Brexit fiasco connects with recent approaches linking varieties of policy learning to policy pathologies. This article sets out to explain the UK government's (mis)management of the Brexit process from June 2016 to May 2019 from a policy learning perspective. Drawing on interviews with UK policy-makers and stakeholders, we ask how did the UK government seek to learn during the Brexit negotiations? We consider four modes of learning: reflexivity, epistemic, hierarchical, and bargaining. By empirically tracing the policy process and scope conditions for each of these, we argue that learning through the first three modes proved highly dysfunctional. This forced the government to rely on bargaining between competing factions, producing a highly political form of learning which stymied the development of a coherent Brexit strategy. We argue that the analysis of Brexit as a policy process – rather than a political event – reveals how policy dynamics play an important role in shaping the political context within which they are located. The article concludes that public policy analysis can, therefore, add significant value to our understanding of Brexit by endogenising accounts of macro political developments. [ABSTRACT FROM AUTHOR]

Reference to policy learning is commonplace in the public policy literature but the question of whether it qualifies as an analytical framework applicable to the policy process has yet to be systematically addressed. We therefore appraise learning as analytical framework in relation to four standards: assumptions and micro‐foundations, conceptual apparatus, observable implications, normative applications. We find that policy learning meets the four standards, although its theoretical leverage varies across them. Since we are not aware of theories of the policy process that meet all of these standards all the time, we conclude that policy learning fares reasonably well and it's worth investing intellectual resources in this field. [ABSTRACT FROM AUTHOR]

Policy learning is an attractive proposition, but who learns and for what purposes? Can we learn the wrong lesson? And why do so many attempts to learn what works often fail? In this article, we provide three lessons. First, there are four different modes in which constellations of actors learn. Hence our propositions about learning are conditional on which of the four contexts we refer to. Second, policy learning does not just happen; there are specific hindrances and triggers. Thus, learning can be facilitated by knowing the mechanisms to activate and the likely obstacles. Third, learning itself is a conditional final aim: although the official aspiration of public organisations and politicians is to improve on public policy, policy learning can also be dysfunctional - for an organisation, a policy, a constellation of actors or even democracy. [ABSTRACT FROM AUTHOR]

The field of policy learning is characterised by concept stretching and a lack of systematic findings. To systematise them, we combine the classic Sartorian approach to classification with the more recent insights on explanatory typologies, distinguishing between the genus and the different species within it. By drawing on the technique of explanatory typologies to introduce a basic model of policy learning, we identify four major genera in the literature. We then generate variation within each cell by using rigorous concepts drawn from adult education research. By looking at learning through the lenses of knowledge utilisation, we show that the basic model can be expanded to reveal sixteen different species. These types are all conceptually possible, but are not all empirically established in the literature. Our reconstruction of the field sheds light on mechanisms and relations associated with alternative operationalisations of learning and the role of actors in the process of knowledge construction and utilisation. By providing a comprehensive typology, we mitigate concept-stretching problems and lay the foundations for the systematic comparison across and within cases of policy learning. [ABSTRACT FROM AUTHOR]