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Policy learning is an established conceptual and analytical framework to study the policy process. Its progress as a research agenda hinges on solid micro-foundations, its integration with other policy process theories, the consideration of both positive and negative cases (of learning and change) from diverse continents and countries, and the clarification of the causal pathways connecting individual learning to decisions and change. We sketch these problematic areas, showing where policy learning research has encountered problems, and illustrate how the articles of this special issue contribute to their solution.

This article examines the persuasive potential of polysemic ideas in contexts where powerful actors are needed to generate new policy decisions or non-incremental changes to existing policies. Polysemic ideas are those characterised by ambiguity, allowing for multiple interpretations, and consequently adaptability to new environments (Cino Pagliarello, 2022a). However, we argue that there is a paucity of empirical cases, especially cases pointing to variation in the contexts in which polysemic ideas are successful in facilitating coalition-building and driving policy change. We address this gap by examining two cases of polysemic ideas from the European Union: the ‘Europe of Knowledge' in education policy, which succeeded in forging a new policy consensus and shifting preferences about cooperation in education (Cino Pagliarello, 2022b), and ‘Solidarity' in asylum policy, which failed to unite Member States around the need for major changes to the EU asylum distribution system (Tubakovic, 2019). We explain this variation by drawing on the policy learning literature. We argue that, when successful, polysemic ideas persuade policymakers to reassess their beliefs and recognise new shared possibilities to achieving their interests under the new policy framework. Trust and collaborative structures are nevertheless essential to this learning process.

Policy learning is a key mechanism of policy change through which policy actors revise their beliefs and preferences over time as a result of social interactions and new information. The individual psychology of policy actors is crucial to understanding how institutional settings and social practices influence policy learning. This article looks at the effects of self-esteem—i.e., how policy actors value themselves—and egocentrism—i.e., their tendency to confuse their subjective perceptions with objective reality and to disqualify the perceptions of others.Based on regression analyses of a 2012 survey of 255 Belgian policy actors who had been involved in the European liberalization process of the rail and electricity sectors, the findings suggest that policy actors who score higher on self-esteem or egocentrism feel that they “know better”: they align their policy preferences to new policy information less than policy actors who score lower. Only egocentrism directly leads to a negative adjustment of policy actors' preferences towards liberalization policies over time. The theoretical and practical implications of these findings are discussed. They shed light on policy actors' modes of reasoning and are thus an important step in the research agenda on “learning governance”.

Policy learning is a crucial mechanism for policy change. Yet, there is still uncertainty about the conditions under which learning actually leads to change. This article clarifies the causal chain from policy learning to policy change in two steps. First, it develops a so-called “Learning Product Framework” which distinguishes three central features of learning products: policy belief change, policy preference change, and policy output change. Second, it presents a “Typology of Causal Pathways between Learning and Change”, leading to four different learning-induced policy changes. In the first pathway, policy beliefs have changed, but preferences and outputs remain unchanged, resulting in policy stability rather than policy change. In the second pathway, policy beliefs and preferences have changed, but the output has not been altered, also leading to policy stability. In the third pathway, beliefs, preferences, and outputs have changed, but they are not aligned, resulting in “Non-Congruent Policy Change”. Only in the fourth pathway are all three features aligned and fulfilled, leading to “Congruent Policy Change”. This conceptual clarification confirms previous findings that policy learning alone is not sufficient for policy change. It demonstrates the combination of cognitive, behavioral, and social mechanisms needed for learning-induced policy change.

In recent years, policy innovation labs (PILs) have emerged to develop greater capacity for addressing pressing public policy problems and achieving policy objectives. An implicit assumption is that PILs can bring new data and evidence to support policy learning. Policy learning enables individuals, organizations, and systems to advance their capacity to achieve policy objectives and produce desirable policy outcomes. We investigate the role of policy learning in “data-based” PILs, which address the growing interest in big data and advanced technologies to address public policy issues. With a focus on applying new information and data systems technologies to assess public policy and management issues, data-based PILs would be expected to support policy learning and thus serve as a critical case for assessing how the design of policy venues can support intentional policy learning. Yet, limited research has examined whether and how policy learning occurs in these PILs. Using an exploratory study design and analyzing findings from key informant interviews, insights into policy learning reveal that data-based PILs enable policy learning processes by acquiring, translating, and disseminating data, information, and experiences across organizations. The most significant policy learning challenge that data-based PILs face is inadequate systems to connect data across agencies. This barrier was perceived as limiting the ability of PILs to fulfill their role of enhancing knowledge sharing in a policy system.

The orthodox literature on policy transfer and lesson drawing tends to define and distinguish the mechanism of learning as a more horizontal and rational process. Much ink has been spent on theorising learning in an instrumental fashion, based on the ‘intentionality' of agents and the updating of information. Nevertheless, little is known about the actual practices through which learning takes place at both the individual and collective levels. This is particularly true in the case of South-South policy learning which, despite its growing popularity, has remained less explored in mainstream discussions. This article addresses these gaps by conceptualising the mechanism of cross-national policy learning among Southern countries from both the supply side – how various transfer agents facilitate the flow of ideas – and the demand side – how policy-makers receive information and change their ideas. By studying the case of the industrial park programme in Ethiopia, with information drawn from interviews, participant observation, policy documents, news reports, and secondary literature, the article highlights the multiple strategies employed by transfer agents that have facilitated ideational change in the process. It also indicates the ways in which ‘bounded rationality' operates in policy-making, which has further accelerated a rapid policy transfer from China to Ethiopia.

Recent research has pointed to the need to investigate policy learning under conditions of contingency and ambiguity. The Multiple Streams Framework seems particularly suited to addressing policy-making in such a context, but also appears to be at odds with policy learning approaches. Against the background of the literature, and the framework's assumption of loosely coupled streams, we argue that learning can be investigated within and across the three streams. Based on nine constellations of streams, we distinguish six types of learning characterized by different tasks, actors, and effects. Policy learning is often necessary in policy arenas where different logics of action clash and where value conflicts cannot be dissolved. One paradigmatic case is the long-standing fight over the re-establishment of wolf populations in Europe. Using Lower Saxony in Germany as a paradigmatic case, we trace the streams in the investigation period 2017-2022 and probe them for instances of potential learning from the sources suggested by the typology. In light of these results, we discuss the chances and the limitations of integrating learning in multiple streams.

Collaborative governance and the delegation of tasks to independent agencies are among the most important governance trends in recent years. However, these trends have given rise to separate literatures that do not engage with each other. Research on collaborative governance shows the complexity of accountability relations and uncovers the factors that undermine accountability. However, it fails to consider some relevant findings from research on ‘agencification' that should be incorporated into its research agenda. Governance scholars should consider more thoughtfully that, for a variety of reasons, accountability holders may be less watchful than expected, but also that accountable behavior can be more attractive than expected, again for diverse reasons. Policy-makers' strategies for avoiding accountability have been emphasized, but to advance the understanding of real-world accountability processes, we must consider that control deficits can be caused by the passivity of accountability ‘forums', while policy-makers may find their accountability valuable.