This chapter introduces the reader to the big picture of what analytics science is. What is analytics science? What types does it have, and what is its scope? How can analytics science be used to improve various tasks that society needs to carry out? Is analytics science all about using data? Or can it work without data? What is the role of data versus models? How can one develop and rely on a model to answer essential questions when the model can be wrong due to its assumptions? What is ambiguity in analytics science? Is that different from risk? And how do analytics scientists address ambiguity? What is the role of simulation in analytics science? These are some of the questions that the chapter addresses. Finally, the chapter discusses the notion of "centaurs" and how a successful use of analytics science often requires combining human intuition with the power of strong analytical models.

This chapter explores how metaphysical models, particularly the compositional and transformational approaches, can help elucidate the doctrine of the Incarnation. While these models face challenges, such as the Nestorian and Attributes Problems, various solutions have been proposed to address these issues and align the models with orthodox Christology. Ultimately, metaphysical models aim to provide coherence and plausibility to the mystery of the Incarnation, contributing to the ongoing work of analytic theology in understanding this central Christian doctrine.

The chapter will help you to be able to explain what Social Anxiety Disorder is and how it typically presents, including distorted mental representations and selective focus of attention, describe and use evidence-based CBT protocols for Social Anxiety Disorder, choose and use appropriate formulation models for CBT for Social Anxiety Disorder, describe the importance of using exposure to social situations in any treatment plan, develop a treatment plan for CBT for Social Anxiety Disorder, using appropriate measures, and take account of comorbidity in managing CBT for Social Anxiety Disorder

The chapter will help you to be able to explain what panic disorder is and how it typically presents, including unexpected panic attacks, and subsequent fear and attempted avoidance of further attacks, describe and use evidence-based CBT protocols for panic disorder, choose and use appropriate formulation models for CBT for panic disorder, describe the importance of using exposure to panic symptoms in any treatment plan, develop a treatment plan for CBT for panic disorder, using appropriate measures, and take account of comorbidity in managing CBT for panic disorder

Chapter 2 explores an important premise which underlies this critique of the law: it examines the idea that disfigurement inequality is a problem which merits a legal response – namely the granting of protective rights under the Act. It concludes that, despite some uncomfortable distinctions, there is a compelling case for a legal response in this area. The nature of law’s current response is then laid out. Relevant parts of the international legal framework – including EU law, the UN Convention on the Rights of Persons with Disabilities (‘CRPD’) and decisions of the European Court of Human Rights (‘ECtHR’) applying the European Convention on Human Rights – are explained by reference to the models of disability which implicitly inform them.

What is the nature of discovery? As a human being and a physicist, I can only observe one mind at work first-hand. This mind accepts every new scrap as a discovery, whether it originates in the external world of knowledge or springs from an internal process. To explore the nature of discovery from the point of view of the inner observer, I chose to turn over past experiences, fitting together days on which years of determined and dogged plodding resulted in a finished equation, or finally, a coherent assembly of disparate ideas gave the clue to why storm damage in breakwaters is like a phase change in liquid crystals. Old, unfashionable methods are suddenly useful with new computer architectures. Theory, experiment, observation, and simulation fit together as aids to thinking. The properties of complex systems can provide intuitive insight into the social science of science. We will need every ability to assemble the puzzle pieces in the coming years to discover how to extricate the planet from the difficulties in which we have placed it: as an observer and actor, I suggest that the evolution of human thinking, and of aids to thinking, are critical.

Psychiatric disorders are complex and multifaceted conditions that profoundly impact various aspects of an individual’s life. Although the neurobiology of these disorders is not fully understood, extensive research suggests intricate interactions between genetic factors, changes in brain structure, disruptions in neurotransmitter pathways, as well as environmental influence.

In the case of psychotic disorders, such as schizophrenia, strong genetic components have been identified as a key feature in the development of psychosis. Moreover, alterations in dopamine function and structural brain changes that result in volume loss seem to be pervasive in people affected by these disorders. Meanwhile, mood disorders, including major depressive disorder and bipolar disorder, are characterized by disruptions in neurotransmitter systems responsible for mood regulation, such as serotonin, norepinephrine, and dopamine. Anxiety and personality disorders also exhibit neurotransmitter dysfunction and neuroanatomical changes, in addition to showing a genetic overlap with mood and psychotic disorders.

Understanding the underlying mechanisms in the pathophysiology of these conditions is of paramount importance and involves integrating findings from various research areas, including at the molecular and cellular levels. This brief overview aims to highlight some of the important developments in our current understanding of psychiatric disorders. Future research should aim to incorporate a comprehensive approach to further unravel the complexity of these disorders and pave the way for targeted therapeutic strategies and effective treatments to improve the lives of individuals afflicted by them.

This is a revision of John Trimmer’s English translation of Schrödinger’s famous ‘cat paper’, originally published in three parts in Naturwissenschaften in 1935.

Estimates of the economic costs of climate change rely on guesswork in the face of huge uncertainties, and arbitrary judgements about what is important. The models can produce any number their creators want them to; and typically, they trivialise the risks. Despite being described as ‘worse than useless’ by leading academics, economic analysis of this kind has been credited with a Nobel Prize, and it continues to inform government policy.

Over the years, the Serengeti has been a model ecosystem for answering basic ecological questions about the distribution and abundance of organisms, populations, and species, and about how different species interact with each other and with their environment. Tony Sinclair and many other researchers have addressed some of these questions, and continue to work on understanding important biotic and abiotic linkages that influence ecosystem functioning. In common with all types of scientific inquiry, ecologists use predictions to test hypotheses about ecological processes; this approach is highlighted by Sinclair’s research that explored why buffalo and wildebeest populations were rapidly expanding. Like other scientists, ecologists use observation, modeling, and experimentation to generate and test hypotheses. However, in contrast with much biological inquiry, ecologists ask questions that link numerous levels of the biological hierarchy, from molecular to global ecology.

Forecasting elections is a high-risk, high-reward endeavor. Today’s polling rock star is tomorrow’s has-been. It is a high-pressure gig. Public opinion polls have been a staple of election forecasting for almost ninety years. But single source predictions are an imperfect means of forecasting, as we detailed in the preceding chapter. One of the most telling examples of this in recent years is the 2016 US presidential election. In this chapter, we will examine public opinion as an election forecast input. We organize election prediction into three broad buckets: (1) heuristics models, (2) poll-based models, and (3) fundamentals models.

How do children process language as they get older? Is there continuity in the functions assigned to specific structures? And what changes in their processing and their representations as they acquire more language? They appear to use bracketing (finding boundaries), reference (linking to meanings), and clustering (grouping units that belong together) as they analyze the speech stream and extract recurring units, word classes, and larger constructions. Comprehension precedes production. This allows children to monitor and repair production that doesn’t match the adult forms they have represented in memory. Children also track the frequency of types and tokens; they use types in setting up paradigms and identifying regular versus irregular forms. Amount of experience with language, (the diversity of settings) plus feedback and practice, also accounts for individual differences in the paths followed during acquisition. Ultimately, models of the process of acquisition need to incorporate all this to account for how acquisition takes place.

This chapter introduces you to foundational knowledge regarding frameworks and models which is applied in later chapters. Theoretical models and frameworks serve as the ‘connective tissue that meshes theory and practice’. The chapter presents an overview of some of the most pertinent models and frameworks that can support you in designing lessons or learning experiences that incorporate digital technologies. It also highlights how you can reflect on the integration of technology into your teaching.

This chapter begins with models of educator knowledge, TPACK and the UNESCO ICT model, followed by the WHO workflow that helps you plan for using digital technologies in learning. The chapter also examines models and frameworks for considering the degree of integration of technology into teaching (SAMR and RAT/PICRAT) and concludes with educator acceptance models (TAM and CBAM).

Inferences are never assumption free. Data summaries that do not account for all relevant effects readily mislead. Distributions for the Pearson correlation and for counts, and extensions accounting for handling extra-binomial and extra-Poisson variation are noted. Notions of statistical power are introduced. Resampling methods, the bootstrap, and permutation tests, extend available inferential approaches. Regression with a single explanatory variable is used as a context in which to introduce residual plots, outliers, influence, robust regression, and standard errors of predicted values. There are two regression lines – that of y on x and that of x on y. Power transformations, with the logarithmic transformation as a special case, are often effective in giving a linear relationship. The training/test approach, and the closely allied of cross-validation approach, can be important for avoiding over-fitting. Other topics include one- and two-way comparisons, adjustments when there are multiple comparisons, and the estimation of false discovery rates when there is severe multiplicity. Discussions of theories of inference, including likelihood, and Bayes Factor and other Bayesian perspectives, ends the chapter.