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Calls for a strategy to improve the quality of the rules produced by the European Union (EU) date back to the early 1990s. During the last thirty years, this strategy has emerged in waves of ‘high quality regulation’ and ‘better regulation’. Since the early 2000s, this agenda has gradually taken its role in the EU policy process, especially at the stage of policy formulation (with the tools of consultation and impact assessment) and, in the last decade, with attempts to include other stages of policy process with tools for the retrospective review of legislaton and regulatory offsetting. Better regulation is an overall commitment binding the EU institution (with the inter-institutional agreement on better law-making agreed in 2016) 1 and the member states. The reality on the ground is that the Commission has deployed the tools of better regulation more intensively than the European Parliament and (even more so) the Council. Member states and the Commission are not always on the same page when it comes to the choice and specification of how to use the tools, and whether the political aim is to improve on regulatory quality or to reduce the quantity of rules. For all these nuances and political differences, today, better regulation is a formally endorsed working method of the European Commission (2019a). In the near future, the challenges concern the integration of foresight, the connection between different phases of the EU policy process (what in Commission’s parlance is known as ‘closing the policy cycle’, Mastenbroek et al. 2016), and policy coherence for sustainability. In this chapter, we outline the foundations of better regulation by reviewing the historical record tracing the main episodes. Specifically, we cover: the 1992 Edinburgh summit to the Mandelkern Report of 2001, the Prodi Commission’s package on regulatory reform, impact assessment and consultation, the dialogue between the Council and the Commission on setting targets for administrative burdens, the rise of regulatory oversight first with the Impact Assessment Board (2007) and then with the Regulatory Scrutiny Board (RSB), the Juncker-Timmermans vision for better regulation and the commitment to regulatory offsetting of the von der Leyden Commission. With the foundations laid, we finally explore what lies ahead.

We examine the classic four dimensions of policy style through a new perspective: policy learning. The approach of ‘modes of policy learning’ sheds light on the dynamic dimensions of anticipation, reaction, consensus and imposition that characterise policy styles in terms of problem solving and relationships among actors. What is the learning mode that matches each of the four dimensions of style, and what are these modes good for? We find that for each of these dimensions there is a particular type or mode of policy learning. By this we mean a mode of learning that seems particularly suited to that dimension. However, other modes of learning can also be empirically found to operate within the four dimensions with their own effects in terms of problem solving. Further, a mode may not be functional to a dimension but can still be empirically present because it empowers a certain constellation of actors. However, particular modes of learning are not chosen by actors writing on blank sheet. They are selected within particular conditions defining the institutional, political and administrative context.

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.在公共政策文献中时常见到政策学习的参考资料, 但这样的参考资料是否能成为合格的分析框架, 应用于政策过程呢？这是还需要进行系统处理的问题。因此, 作者以四个标准评价了作为分析框架的政策学习。四个标准分别是：假设和微观基础、概念工具、可见的意义/影响, 以及规范性应用。作者发现, 政策学习达到了这四项标准, 尽管其在各项标准中的理论影响有所不同。由于本文并未发现能在任何时候都达到这些标准的政策过程理论, 结论认为, 政策学习是相当成功的, 并且值得对此领域投资智力资源。.La referencia al aprendizaje político es común en la literatura de la política pública, pero el interrogante de si esto califica como un marco analítico que se puede aplicar al proceso político todavía tiene que ser sistemáticamente abordado. Entonces evaluamos el aprendizaje como un marco analítico en relación con cuatro estándares: las suposiciones y las micro fundaciones; los aparatos conceptuales; las implicaciones observables; las aplicaciones normativas. Hallamos que el aprendizaje político cumple con los cuatro estándares, pero su ventaja teórica varía entre ellos. Ya que no estamos conscientes de teorías del proceso político que cumplan con todos estos estándares todo el tiempo, concluimos que el aprendizaje político se defiende razonablemente bien y tiene valor de inversión y recursos de inteligencia en este campo.

First published online: 16 April 2018 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.