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.

1 Introduction

2 The Early Days of Serious Games

 2.1 Examples of Influential Games, 1970s to 2010

 2.2 The Emergence of Serious Games as an Academic Discipline

 2.3 Further Analysis of the Constituent Components in Game Models

  2.3.1 User Experience

  2.3.2 Learning and Learning Outcomes

  2.3.3 Game Characteristics

  2.3.4 Instructional Content

  2.3.5 Player Characteristics and Context

 2.4 Evaluation of Serious Games

3 Recent and Current Advances

 3.1 Examples of Influential Games, 2010–20

 3.2 Recent Progress on Serious Games as an Academic Discipline

  3.2.1 Recognition of the Need for Rigorous Evaluation

  3.2.2 Learning / Learning Outcomes and Twenty-First-Century Skills

  3.2.3 More Recent Taxonomies and Models

  3.2.4 Content

4 Looking to the Future

 4.1 New Areas of Development

 4.2 Technological Advances

 4.3 Learning Analytics and Personalisation

 4.4 Constraints on Serious Games

5 Conclusion

Multilevel multicomponent complex adaptive systems are not reducible to the sum of the causal effects of independent variables. Causal inference, which has a privileged place in contemporary IR (and many other social sciences) cannot address systems effects, which arise from interdependent elements and operations (not the impact of independent variables on dependent variables). Systems effects explanations explain why by showing how. They identify mechanisms and processes of causation. They thus are able to establish causal efficacy; that is, show how processes produce – actually cause – outcomes (rather than merely identify some elements that are part of an unspecified causal process). Such an understanding leads us away from a “laws and theories” conception of science, which remains popular in Physics and Chemistry, towards a “models and mechanisms” understanding, which predominates in the life sciences (which, on their face, seem a much better model for the social sciences).