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Emerging results in cognitive science have far-reaching consequences concerning the cognitive processes that make for doxastic justification. Epistemologists commonly suppose that epistemological tasks can be, and are, managed by cognitive processes in which all the information bearing on a stretch of belief fixation is occurrently represented in the course of these processes. Recent work in cognitive science associated with “the frame problem” indicates that this common assumption is misguided (as are several tempting, weakened versions of it). It is argued that an epistemologically crucial aspect of effective belief-formation involves cognitive processing that accommodates a wide range of information in an implicit way—without the information being explicitly represented in the course of processing, either consciously or unconsciously. What emerges is a view of objectively justificatory human belief-fixing processes according to which much of the epistemic action, much of what contributes to one's systematic successes, lies below the readily accessible “surface” of one's cognition. This calls for an epistemology with two legitimate, and complementary, foci. One is the traditional focus on psychological factors that are conscious during belief formation, and/or are readily psychologically accessible after the fact. And the other focus is the full set of epistemologically relevant psychological factors, many of which may be only partially, piecemeal-fashion, accessible after the fact of belief-formation. Thus, one arrives at what is termed iceberg epistemology.

Explaining how policies may be changed over time is a fundamental theme common to the study of public policy and governance. Scholars have developed several competing perspectives on how and why policy change occurs; while policy practitioners are largely focused on the successful negotiation and implementation of policy improvement and occasional major policy reforms. This chapter focuses on frameworks for explaining how policy agendas shift, how policy change occurs, and how some proposals for change are constrained. In the real world of complexity, wicked problems and mediatised debate, the authority and capacity of the state are subjected to many countervailing pressures. The explanation of policy change must take account not only of how Ministers are involved in setting priorities and mobilising political support, but also how public agencies manage the policy process – including their contributions to policy framing, policy design, engagement, evaluation, and managing conflicting views within civil society. In the governance era, policy change has become a complex and nuanced enterprise. This chapter reconsiders the utility of classic accounts of policy dynamics concerning evidence-based policy, ideology, and populist partisanship in addressing complex policy challenges.

The chapters in this book have provided an overview of the state-of-the-art in the study of how people reason with conditionals. And outlining theories of conditionals has led to immediate contact with core debates concerning theories of human thought, and, indeed, the nature of rationality itself. Three issues arising across the volume seem to be of particularly broad significance: Firstly, probability vs. logic? What is the role of probability and or logic in explaining human thought? Are they in competition or are they complementary? Secondly, single vs. multiple systems? Is reasoning carried out by a single cognitive system, or by two or more reasoning systems? Finally, background knowledge and the frame problem. This is about how thought is influenced by, potentially arbitrarily large, amounts of background knowledge. This chapter briefly reviews the state of play in these three key areas, and highlights directions for future research, both in the study of reasoning with conditionals, and in cognitive science more generally.

Narrowing the perspective to the puzzle aspect of problem framing and structuring, this chapter addresses the historical development of policy analysis. It shows how policy analysis had to radically revise its epistemological and methodological assumptions. From decision support to leaders and their top-level staff or ‘speaking truth to power’, policy analysis had to revamp itself as ‘making sense together’. Meanwhile, leaving epistemological differences aside, in practice several doable styles of policy analysis have developed. The chapter ends with an overview of doable styles in policy analysis as instruments for reflexive practitioners.

This chapter analyses the emergence, the construction and the trajectory of road space reallocation as a policy solution to urban mobility challenges. It argues that policy solution ownership has been established through the continued efforts made by a coalition of policy entrepreneurs to transform a policy solution fitted for all seasons into an easily transferable set of standardized tools and techniques aimed at shifting the attention away from car use reduction in the name of fairness and modernity. This chapter contributes to the book’s main argument in three ways. First, it examines how and by whom policy solution ownership is built and contributes to redefining the boundaries of the problem at stake. Second, drawing on the notions of problematization and interessement, it contributes to the understanding of how policy solution ownership opens new opportunities for urban elites to challenge existing urban governance arrangements and promote their own political agenda. Three, it discusses the role of framing in shaping the subsequent trajectory of this policy solution in two urban governance contexts, namely Lisbon (Portugal) and Malmö (Sweden).

The second chapter, “Frame the problem,” provides tools to understand and define the problem in a constructive way. It proposes ways to identify and focus on the critical issue, as opposed to one of its symptoms or a real-but-less-important issue. The chapter also demonstrates how to describe the resolution process and capture this description in a what card. A key element of this chapter focuses on starting the diagnostic stage. It introduces the concept of a key question: the one overriding question that the problem solver should answer in a given project phase. It explains the attributes of good key questions—type, topic, scope, and phrasing—and provides a technique to develop a good key question. It also explains how to set the key question in an introductory flow that includes a situation and a complication and shows how to capture the vital information about the diagnostic in a why card.

The question of how technology impacts the existing forms of epistemology and forms a new kind of socially extended epistemology deserves a thorough philosophical investigation. Traditionally, epistemology has been bound to a vision of knowledge as internal beliefs justified via logical inference. This view was externalized by artificial intelligence research into knowledge representation. Yet historically this form of research has failed, with knowledge representation being unable to cope with the Frame Problem: How to capture a changing and fluid world in a formal system that can be mechanized? Today, people use search engines, tagging, and social media to leave an enactive “social trail” through the vast amount of information, creating new kinds of distributed and extended knowledge that challenges traditional theories of epistemology. This shaping of the epistemic environment allows humans to socially solve the Frame Problem and extend the bounds of knowledge via technological means.

With the motivation to develop computational and algorithmic levels of understanding how the mind comes into being, this chapter considers computer science, artificial intelligence, and cognitive systems perspectives. Questions are addressed, such as what ‘intelligence’ may actually be and how, and when an artificial system may be considered to be intelligent and to have a mind on its own. May it even be alive? Out of these considerations, the chapter derives three fundamental problems for cognitive systems: the symbol grounding problem, the frame problem, and the binding problem. We show that symbol-processing artificial systems cannot solve these problems satisfactorily. Neural networks and embodied systems offer alternatives. Moreover, biological observations and studies with embodied robotic systems imply that behavioral capabilities can foster and facilitate the development of suitably abstracted, symbolic structures. We finally consider Alan Turing’s question “Can machines think?” and emphasize that such machines must at least solve the three considered fundamental cognitive systems problems. The rest of the book addresses how the human brain, equipped with a suitably-structured body and body–brain interface, manages to solve these problems, and thus manages to develop a mind.

This chapter examines how SGCC managed to convince the government to accept its controversial ultra-high-voltage (UHV) AC and UHV DC projects as a policy solution to a bundle of problems. It highlights (a) a set of visions for SGCC and China’s transmission industry; (b) the way SGCC used its institutional capacity to translate the visions and ideas into policy alternatives and actions; (c) the ability of its management to manoeuvre the policy windows in the policy process; and (d) the active role SGCC played as a policy entrepreneur to keep its preferred policy alternatives on the government agenda. In this process, SGCC and policy makers enjoyed a reinforcing partnership: the government needed ideas to govern while SGCC was happy and able to ‘educate officials’, identify the problems and provide solutions. All was done from its perspective but justified in the interest of government and the country as a whole,