As the British Empire expanded in Asia, the concept of piracy was reconstructed: the pirate became part-criminal and part-sovereign—a desperate savage born of anarchic geography, yet a powerful despot wielding political violence against the enshrined freedoms of humanity. The paradox of piratical statehood saw potentates punished as if they were pirates, and seafarers warred upon as if they were states. The British construed their own sea power as a force for modernity while consigning those who challenged them to a bygone era. Such ‘pirates’ were not to be found upon the high seas, but along coastlines and in converging straits and waterways, where dynamic, local sovereignties had hitherto held sway. As a criminalising category, charges of piracy were not levelled by the hegemonic authorities of a monolithic empire, but arose in the conversations between administrators, the petitions of merchants, in newspaper columns, and through the diplomacy of ‘men on the spot’. The condemned seldom conformed to any pirate archetype; they could at best be described as ‘piratical’—a semantic slippage that transformed the specific actions of individuals into a pervasive and immutable condition.

The first decade of the nineteenth century in Britain stood out for the rise of the juvenile tradition. The felt symbolism of the new century provided a representational force auspicious and propulsive for young writers. Before 1810 Browne, Byron, Clarke, Hunt, Moore, Shelley, and Smythe published as teenagers. Southey edited collections of the dead young poets Chatterton and Kirke White. Such ‘literary remains’ provided the juvenile tradition with a heritage in this decade at a calendrical juncture that brought together past, present, and future. Kirke White’s ‘literary remains’ inspired living poets to take up this form to express their sense of current neglect and hope for future recognition. Literary remains as a form changed understandings of Romantic lyric. To writers sure of early death, subjectivity was not a privilege taken for granted. Collecting scraps to capture the remnants of the dead was not abstract but poignant because so inertly material.

This chapter argues that two of the common methods used in behavioural and social sciences to reduce the chances that models overfit the available data, namely heavy reliance on benchmark models and rigorous parameter estimation techniques, can slow the advancement of these sciences. An examination of classical decision research highlights how applying these methods shaped the field but have also led to limited success. As an alternative, the chapter proposes a prediction-oriented approach to the development of behavioural models. Evaluating and comparing models based on their predictive power inherently guards against overfitting and also facilitates accumulation of knowledge. The chapter reviews research employing the prediction-oriented approach in behavioural decision research and demonstrates that, in contrast to a common misconception, the focus on predictions can also facilitate better understanding of the underlying processes.

I present historical background, contemporary status, and potential future development of the psychology of religion (PoR) in Russia, beginning with its origins: the formative people, places, and various intellectual schools of thought. Writing about the current state of the topic, I reflect on influential factors that are either facilitating or inhibiting the study of PoR, including publication options, topical emphases, practitioners, orientations, methodologies, and professional organizations.

I offer opinions concerning future development topics that are emerging as important in the immediate future and/or are perennially important in order to stimulate creative and useful research including Western theoretical relevance, the extent to which Western PoR theories may or may not contain reasonable expectations and concepts for this region, contextual nuances, Indigenous theoretical concerns, collaborative research opportunities, and common faux pas – reflections on what people unfamiliar with this region commonly and incorrectly assume about conducting PoR work in this context.

Extended reality (XR), encompassing virtual reality (VR) and augmented reality (AR), has become a crucial tool in Behavioural Data Science. This chapter explores the applications of XR, VR and AR in this field, with a focus on analysing human behaviour and decision-making in immersive environments. The chapter begins with an overview of XR, VR and AR technologies and their potential in Behavioural Data Science. It discusses the advantages of using immersive environments for studying human behaviour, such as the ability to control and manipulate variables, measure behaviour in real time and simulate complex scenarios. It reviews various applications of XR, VR and AR in Behavioural Data Science. The chapter covers how immersive environments aid in studying decision-making, social interaction, learning, training and cognitive processes, with specific examples like using VR for consumer behaviour studies and AR for employee training. The challenges and opportunities of applying XR, VR and AR in Behavioural Data Science are also discussed. This includes the need for advanced data collection and analysis tools, ethical considerations around data privacy and security and potential new applications in fields like healthcare, education and entertainment. The chapter emphasises the significance of XR, VR and AR in understanding human behaviour and decision-making in immersive environments. It calls for ongoing research to further explore the potential applications of these technologies in Behavioural Data Science and to develop new tools and methods for analysing data from immersive environments.

From this chapter on, we study upper ramification subgroups. In this chapter we introduce a geometric constuction used in the definition of the upper ramification subgroups. We take an immersion to a smooth scheme, take a dilatation, and construct a stable integral model by taking a base change and normalization. The existence of stable integral models is a consequence of the reduced fiber theorem proved in Chapter 9. As an example, we compute the construction in the monogenic case explicitly.

In this chapter, we study the fundamental question of finding large matchings in uniform regular hypergraphs and present the semirandom method – sometimes called the Rödl nibble. We present important applications to transversals in Latin squares and the existence of asymptotic designs.

This chapter explores the significance of adopting a posthuman approach to placemaking as a strategy for cultivating hope and resilience amidst rapid global change. Drawing on posthuman theory, the author argues for moving beyond human-centric perspectives to embrace interconnectedness with other species, technology, and the environment. To strengthen a ‘psychology of place’, the author recommends recognising our kinship and deep psychological bonds with non-human entities and environments, embracing Indigenous knowledge systems, and fostering ethical relationships with technology. Practical strategies include designing places that encourage multispecies interactions, promoting local biodiversity, and integrating cultural rituals and ceremonies that reinforce communal and ecological bonds. Furthermore, fostering collective dialogues across communities to repair human and ecological relationships is emphasised. Ultimately, the chapter calls for a transformative approach to placemaking that prioritises ecological and relational repair, recognising shared vulnerabilities, and actively cultivating inclusive environments where diverse forms of life collectively can thrive.

This chapter explores the flourishing of Buddhist print culture during the Song dynasty, focusing on its visual, material, and transregional dimensions. It shifts attention from elite literary publishing to religious book production, highlighting how Buddhist printing developed in both court-sponsored and commercial contexts. Beginning with rare early blocks, it sheds light on some of the earliest extant printed materials. The discussion on the Imperial Secret Treasures demonstrates visual strategies in imperially sponsored scripture printing. Finely produced Buddhist texts discovered in the tomb of Lady Sun reveal how printed books functioned in funerary practice and lay devotional life. The final section examines Hangzhou as a vibrant printing hub, where family-run publishers produced richly illustrated scriptures. Modular visual strategies in these frontispieces facilitated the circulation of Buddhist imagery, spreading Hangzhou’s Buddhist print culture to Xi Xia, Korea, Japan, and other parts of Asia. The chapter argues that image-bearing prints operated as visual and ritual media, enabling dynamic visual transmissions that often exceeded the textual reach alone.

Network and collective choice models are foundational tools in Behavioural Data Science, offering deep insight into how individual decisions scale into system-level outcomes. These models underpin everything from public health planning and traffic optimisation to social media influence and climate action coordination. Yet, as they become more integrated into decision-making architectures – especially under the regulatory framing of the European Union’s AI Act – questions of bias, equity, explainability and accountability become unavoidable. This chapter argues for a responsible approach to network and collective choice modelling, grounded in legal foresight, social ethics and behavioural realism. Beginning with an overview of the theoretical foundations and methodological advantages of these models, it then unpacks critical concerns: selection and confirmation bias, representational fairness, algorithmic opacity and privacy loss. Special attention is paid to the risks of amplification of systemic inequality and marginalisation through flawed modelling assumptions. The chapter draws on real-world applications to show how stakeholder co-design, model interpretability and participatory governance can mitigate harm. By weaving legal obligations under the AI Act with behavioural science principles, this chapter offers a pathway to designing socially beneficial, transparent and context-aware models of collective decision-making in digital systems.

After presenting motivating examples, cyclotomic extensions, the relation between the conductor of Galois representations and the level of the corresponding automorphic forms, and the Grothendieck–Ogg–Shafarevich formula, we sketch the fact that fundamental properties of the main objects of the book, lower and upper ramification groups, show sharp contrasts. The structure of the book together with the content of each chapter are explained. Required backgrounds are also listed.

Over the last few decades, psychologists have increasingly found that the mind stores and uses the statistics of its environment. However, less work has analysed whether the environmental statistics have changed and what that would imply for the mind. In this chapter, we consider human memory as the solution to the computational problem of predicting what events will happen next given a history of past events. Prior work examining two years of data (1986–1987) found that the environmental statistics of events occurring in the world are reflected in human memory of events, such as practice and retention effects. We analyse the last century of event statistics by assuming that words in the headlines of The New York Times are each an event. While presenting our methods, we do so in the form of a case study – we discuss general practices for Behavioural Data Science projects, standard issues that arise and how to resolve different issues as they arise for the presented analyses. After replicating prior work analysing event statistics in this manner during 1986–1987, we extend the methodology to the last century (1919–2019). Our analyses suggest that the events are occurring in denser bursts, meaning that, if a new event occurs in the last few years, this event reoccurs more often in the short-term and less often in the long-term (as compared to events that first occurred in the early twentieth century). This suggests that human memory faces different environmental demands than it has in the past and may be adapting to the dynamics of event statistics.

The A-IQ project evaluates conversational artificial intelligence (AI) performance through a behavioural perspective, measuring observable external behaviours rather than internal processes considering algorithms developed pre-generative AI. This approach is valuable for applied research targeting end-user perception and where it is not necessary to consider internal processes. The Interdisciplinary Artificial Intelligence Model was developed, consisting of seven domains to solve problems, from which the Interdisciplinary Artificial Intelligence Quotient Scale (iAIQs Scale) was created. The iAIQs Scale contains 62 questions that are evaluated by human testers through a multi-level system of response categories. The A-IQ tests were conducted on Google Now (2018 release) and Google Assistant (2021 release), Siri (Apple), Cortana (Microsoft) and Alexa (Amazon). The results revealed that Siri had the best overall performance due to high scores in the working memory domain, while Cortana scored highest in explicit knowledge. However, no conversational AI scored in the critical or creative thinking domains. Testing, conducted in 2021 (pre-ChatGPT release), showed improvements in Google Assistant’s performance, followed by Alexa, while Siri showed minimal improvements. A prototype was developed to automate the testing process and facilitate continuous monitoring. Limitations were identified regarding reproducibility and objectivity, but the A-IQ project contributes to the evolving field of human–machine interaction, focusing on communication. This chapter focuses specifically on conversational agents developed prior to the release of ChatGPT, with generative AI systems examined in Chapter 15.