This chapter provides insights from economic theory and empirical methods to determine if production of medical services in a firm is efficient. The concepts of a production function can indicate whether production is technically efficient and estimate the marginal contribution to revenue of each input (labor of different types, capital, etc.). With data on the prices or wages of these inputs, the manager can determine the profit-maximizing rate of use. A cost function can indicate whether there are economies of scale in quantity (somehow defined) and economies or diseconomies in scope. Examples from classic health economics literature are used to show that there was underuse of physician aides and other substitutes in office-based physician practices, that there are constant returns in scale in the production of hospital admissions, and that there are increased returns in emergency rooms. Caution in interpretation of variation in cost per unit output (of the type provided for Medicare in the Dartmouth Atlas) is offered.

This chapter investigates price discrimination among buyers and sellers of healthcare. It is very common for different buyers to pay different prices for the same medical service or drug. Economics does not predict that profit-maximizing sellers will increase the price to other buyers if one buyer reduces price (no cost-shifting), but it does hypothesize that buyers with less price-responsive demands can be charged more than those with more responsive demands by sellers with market power. Likewise, buyers with more buyer market power (e.g., larger insurers) often pay less than smaller insurers or individual uninsured consumers. This chapter explains why price discrimination may improve efficiency compared to simple monopoly by allowing a lower price to be charged to those with lower willingness to pay that is still above marginal cost. The role of pharmacy benefit managers (PBMs) in extracting discounts for drugs is described.

This chapter explores the dual nature of algorithms, distinguishing between traditional, linear instructions and adaptive machine-learning algorithms. While traditional algorithms yield consistent outputs, machine-learning adapts with new information, offering benefits such as personalization, efficiency, and scalability. Despite their advantages, complexities arise, including regulatory challenges and the potential for algorithmic bias driven by data skew and stereotypes. The chapter highlights ethical dilemmas and drawbacks in various aspects of modern life, particularly in areas like job-postings and criminal justice decisions. To address bias, it advocates for careful algorithm design, diverse training data, human oversight, fairness metrics, and organizational accountability. Emphasizing transparency through reporting requirements, audits, and user control, the chapter acknowledges ongoing regulatory efforts like the Algorithmic Accountability Act of 2023 and the GDPR. It also explores individual bias concerns, compares algorithmic bias to human judgment, and suggests a combined approach for decision-making optimization.

Molecular motors that transform chemical energy into mechanical motion can be modeled in different ways. Thermodynamically consistent ratchet-type models lead to transport against an external force in a periodic potential that switches between different shapes. In a second class of models, the motor is described by a set of internal states that leads to discrete steps along a filamentous track. In the class of hybrid models, the motor cycles through internal states while pulling a cargo particle that follows a Langevin dynamics. For these motors, the first and second laws and the thermodynamic efficiency are discussed and illustrated with experimental data for a rotary motor, the F1-ATPase.

Chapter 10 evaluates the challenges of SDG 7: Affordable and Clean Energy, which aims to ensure access to affordable, reliable, sustainable, and modern energy for all. The scarcity of non-renewable minerals and energy resources presents a critical global challenge that could constrain economic growth and well-being. Various ways to measure natural resource scarcity are evaluated, and an economic analysis of the optimal extraction of exhaustible resources over time is established. Policies to address future demands for mineral and energy resources while balancing the environmental impacts of extraction and use are discussed. For example, substituting non-renewable energy with renewable energy sources poses economic and environmental challenges. Concerns over supply constraints and reliance on critical minerals have prompted calls for self-sufficiency, reducing reliance on imports of essential raw materials, and creating incentives to enhance recycling, recovery, and reuse, especially of rare earth elements. In addition, developing new technologies to improve end-use efficiency can support the decoupling of dependency on non-renewable resources from economic growth.

This chapter explores “predictive coding” models, which challenge classic theories of perception and brain function. By incorporating details of both the connectivity between brain areas and the levels of microcircuitry within cortical regions, these models suggest a radical new way to conceive of perception and cognition. Whereas classic models assume that feed-forward, or bottom-up, processing is mainly responsible for our perception, predictive coding theories suggest that top-down models determine our perception, with bottom-up processing simply correcting errors in those models. Neuroscience evidence is presented for the abundance of top-down connections, the efficiency of neural coding, the role of expectancy in attention, and how the balance of top-down and bottom-up processing is related to the dysfunctional attention processes in some clinical groups. The allocation of attention is thought to be a dynamic and changing process wherein top-down hyper-priors are integrated with current priors that are being continually updated within and across levels. According to such models, attention affects the expected precision (reliability) of bottom-up information and the likelihood that this information will be used to update the current top-down models. Predictive coding theories that are opening new ways of thinking about the neural mechanisms that drive our attention are discussed.

This chapter explores how common challenges facing long-term care systems across the world have given rise to common trends in the development of long-term care service delivery - a focus on improving integration, the shift from residential care to home- and community-based care, the growing role of the private sector in care provision and the emergence of digital technologies with transformative potential. Recent developments in five countries (Germany, Japan, Sweden, Norway, and Romania) are used to exemplify and distil overarching lessons for strengthening long-term care service delivery.

This chapter introduces the vanishing trial phenomenon – the emphasis on settlement and plea bargains and the decline of the judicial verdict. This phenomenon began in common law systems and coincided with the rise of alternative dispute resolution (ADR). ADR has been promulgated through a variety of legal constructs, including national laws and transnational directives. However, to date, it is often the case that neither the normative values of adjudication nor the fundamental values of ADR (such as dialogue and relation building) prevail. In their stead, especially in common law countries, there is a drive for efficiency in both courts and mediation sessions. Efficiency has, to a large extent, become synonymous with settlement and the means by which settlement is reached receive little to no notice. Judges, in this setting, are expected to manage cases until they settle – though, as our research shows, some judges have more ambitious horizons for their role, lending new insights to the possible new trajectories. As methods to replace the judicial role are under experimentation, the value and place of the judicial role have reached a critical crossroads.

This chapter looks at the most recent climate science and starkly sets out the severity of the problems ahead. It gives the reader all the knowledge needed to broadly understand the critical issues of our day from a technical perspective, including systems of production and consumption for energy and food, biodiversity loss, pollution (including plastics), disease threats and population levels. It then looks at ways in which we can technically transfer to a sustainable way of living.