It started with good intent; the battleground a different and older Union and an earlier referendum in 2014 over Scotland, not Europe. I didn’t see this at the time, but think if it hadn’t come hot on the heels of the Independence Referendum the civil service would have taken a different and more careful approach in 2016. We would have been more protective of the principle of impartiality and seen the EU Referendum as the deeply political and divisive vote that it turned out to be. It had been okay in Whitehall to express a view in the Scottish Referendum – we wanted the Union to stay together. So, just as we were happy in September 2014, people were upset at the result in June 2016. It was normal to express views in the open that leaving the EU would have grave and damaging consequences, especially economically, as it had been normal in 2014 to say the same about Scotland. For both the commonplace presumption was that the people who had voted to leave had not understood how things worked. We rolled from default remain to default remain.

Mediation is an alternative dispute resolution (ADR) mechanism where a neutral third party intervenes in a dispute to help the parties achieve their goals, such as finding an agreement. In this chapter, we examine how artificial intelligence (AI) can be used to further enhance and expand the process of mediation. We explore a variety of different integration points between AI and mediation as illustrated by academic research projects, focused on supporting the disputants and mediator. Then, we discuss some overall insights that can be gained from these projects, including opportunities and challenges that arise when integrating AI in mediation. Overall, we see AI as having significant potential in both increasing the efficiency of mediation and introducing new elements to mediation. Hopefully, these integrations will increase the accessibility and further enhance the benefits of mediation, thus contributing to a more harmonious society.

This chapter examines the evolution and deployment of AI tools in the delivery of dispute resolution in sub-Sahara Africa (SSA) with particular focus on arbitration. The chapter draws on publicly available original data to argue that there indeed is greater opportunity to deploy AI in arbitration as a tool for efficiency, which may lead to cost and time savings. It also explores the emerging regulation of these tools globally, regionally, and in some SSA countries and concludes that regulation of the use of AI must maintain the right balance of achieving efficiency in the process of arbitration and mitigation of its negative effects.

This chapter sets forth a framework based on a historical analysis of the role of efficiency in ICA. This chapter asserts that more so than party-autonomy, arbitrator discretion, the right to second instance review, or emphasis on privacy (and even confidentiality), the main historical principle upon which the legitimacy of ICA was premised, concerns a very narrow concept of efficiency. This legacy construct of efficiency is one that prioritizes the rendering of a binding and enforceable award over all other considerations. The primacy of “process efficiency” is such that even due process has been sacrificed at the altar of expediency. The text explores the interplay between efficiency and due process. It is suggested that only a voluntary settlement can yield “optimal efficiency,” and thereby redeem ICA’s promise to be efficient

Chapter 1.1 discusses the use of taxes and social health insurance contributions. A key objective of health financing is to redistribute financial resources from the healthy to the sick and from the well-off to the poor. This can be best achieved through compulsory prepayment mechanisms like taxes and social contributions. Key learning includes that

• A high reliance on public revenue raising instruments (taxes and/or social health insurance) is essential to progress towards universal health coverage.

• Large informal economies and poor governance can make collecting public revenues difficult.

• Health financing systems have to be able to adapt to

• – Offset challenges to the revenue base such as economic decline, low levels of economic development or a preponderance of informal employment or economic activity and

• – Meet increasing health care demands which grow with rising expectations and population.

• The traditional distinction between health systems that rely on general taxation (Beveridge or NHS systems) and social insurance contributions (Bismarck or SHI systems) has blurred with time.

• Health systems increasingly rely on a diverse mix of revenue raising instruments to finance health care.

• There is a growing focus on de-linking employment from entitlement to services in historically SHI-based systems and on emphasizing general taxation as a preferred source of revenues.

Health financing is a key component of any health system, but its role is more complex than simply raising and spending money on health. It is a crucial determinant of the overall performance of the health system, defining, among other things, how much money is available to be spent on health and who pays for it, who gets to benefit fromthose financial resources, what services that money can purchase and who ultimately receives resources from the health system as income. Without careful attention to the way health financing systems are designed, incentives for providers or patients can bemisaligned with policy goals, leading to poor health outcomes, financial hardship for users of health care, wasted resources, failure to address inequalities and disruption of countries’ progress towards universal health coverage (UHC) (Box 0.2.1).

This chapter emphasizes the foundational importance of clearly defining the scope and goals of an organization as the initial step in the organizational design process. Scope determines how the organization frames its purpose and communicates its identity, which in turn influences strategic choices, stakeholder alignment, and operational priorities. Goals – particularly those related to efficiency and effectiveness – serve as critical dimensions for assessing organizational performance and guiding design decisions. The chapter has illustrated how sustainability considerations and the pursuit of the triple bottom line can reshape both scope and goal formulation, requiring integrated design solutions that balance financial, social, and environmental outcomes. Through examples and empirical evidence, we have shown that organizations must navigate trade-offs and potential misalignments between scope, goals, and design components. Ultimately, a coherent and well-articulated scope, combined with a nuanced understanding of goal priorities, provides the analytical foundation for diagnosing organizational fit and initiating effective design interventions.

The multi-contingency model frames organizational design as a continuous executive task shaped by globalization, digitalization, AI, sustainability, and shifting societal expectations. It identifies nine interdependent components – goals and scope, strategy, environment, configuration, leadership, climate, task design and agents, coordination and control, and incentives and people – whose alignment drives performance. Extending traditional contingency theory, it integrates insights from economics, information processing, and organizational theory, viewing organizations as systems that manage complexity by balancing information-processing demand and capacity. This can mean reducing demand (e.g., modularization, predictive tools) or increasing capacity (e.g., AI, lateral communication, skilled talent). Examples from Microsoft, Aarhus University, Danish healthcare, Uber, and luxury fashion brands show how design adapts to digital innovation, sustainability, and agility. A seven-step method supports the model: getting started, strategic positioning, structuring, defining agents and leadership dynamics, setting coordination and incentives, finalizing architecture, and implementing change.

Efficiency is one of the most pervasive arguments in favour of implementing algorithms in courts of law. Across different legal contexts, many judiciaries find themselves pressured towards efficiency by growing caseloads and budgetary constraints. The purported speed of the use of AI can be seen as a solution to many existing problems, and even as a positive contribution to the value of access to justice. Through a case study of the Brazilian Judiciary’s strategy of the implementation of algorithms, the drive towards efficiency is examined and unpacked to reveal a series of tensions. First, there is a lack of conceptual clarity which leads to multiple, and sometimes competing, notions of efficiency, especially in light of the interpretation and interplay of legal principles. Moreover, the neutral appearance of efficiency can obscure political choices that cause substantive changes to the legal system without being submitted to democratic control. In this sense, a more nuanced view of efficiency as a judicial value is necessary, where it can be both contested and balanced against other core judicial values, and also seen as directional and at the service of specific ends of law.

This chapter reviews specific steps for operating room designing and perioperative planning. It covers efficient means of designing a floorplan for an operating room enviornment that eliminates waste, decreases staff inefficiencies, and improves patient experience. Included in this chapter are figures and diagrams process mapping this goal of improving efficiency in the perioperative enviornment.

Coase or Pigou? Economic property rights and decentralized bargaining or centralized regulation and potential rent-seeking in the political arena in confronting externalities? What are the tradeoffs if rent-seeking, not efficiency determine environmental regulation. That is the question for this volume.

Chapter 10 synthesizes ten key lessons from Nordic capitalism to guide the transformation toward sustainable capitalism. Drawing on evidence from previous chapters, it demonstrates how Nordic societies have successfully coupled market efficiency with democratic accountability to advance sustainable development. The chapter emphasizes how overcoming denial, establishing universal systems, expanding positive freedoms, and fostering cooperation are essential for addressing global sustainability challenges. Through detailed analysis of Nordic policies and practices – from universal childcare to critical thinking in education – it shows how democratic processes can align market incentives with sustainability goals. The chapter concludes that while Nordic capitalism remains imperfect, it serves as a valuable “North Star” for realizing sustainable capitalism, offering proven approaches for expanding individual freedom through collective investment while operating within planetary boundaries.

As a first example of a transformation trading simplicity for efficiency, it’s shown in detail how to take the Fibonacci-number program from its simple, but slow O(N) formulation to a blinding fast O(log N) version that –on its own– is completely incomprehensible. It is correct, but you can’t see that at all by just browsing its concrete code. Your conviction that it is nevertheless correct is based on your step-by-step careful reasoning in the data-refinement style introduced here.