This chapter explores the European Union’s ongoing efforts to simplify and modernize company law to enhance legal clarity, reduce administrative burdens and support cross-border business activity. It examines key initiatives such as the Company Law Package, digitalization of company processes and the reduction of formalities for corporate operations. The chapter evaluates how these reforms aim to improve competitiveness, foster innovation and align company law with the needs of modern businesses. Challenges related to implementation, legal coherence and Member State diversity are also discussed. Overall, the chapter highlights the shift towards a more efficient, accessible and future-oriented company law framework in the EU.

The use of Bayesian modelling has been growing in the cognitive sciences, as it provides a flexible framework for modelling complex and heterogeneous data, as well as incorporating prior knowledge and uncertainty into the modelling process. In this chapter, we focus on predictive Bayesian modelling, which allows for the construction of models that can make accurate and meaningful predictions about future events, behaviours or outcomes. We begin by providing a brief overview of Bayesian statistics and the key concepts necessary for understanding predictive Bayesian modelling. We then discuss the benefits and limitations of using predictive Bayesian modelling in cognitive science research. Next, we explore several examples of how predictive Bayesian modelling has been applied to different areas of cognitive science, including perception, memory, decision-making and language. We discuss how these models have contributed to our understanding of cognitive processes and how they can be used to make predictions about human behaviour. Finally, we outline some of the challenges and future directions for predictive Bayesian modelling in cognitive science research. We discuss the importance of evaluating model fit and model comparison, the need for more accurate and informative prior distributions and the potential for combining predictive Bayesian modelling with other methods, such as machine learning.

This chapter analyses the EU framework governing takeovers, focusing on the Takeover Directive designed to ensure fair treatment of shareholders and transparency during public acquisition bids. It examines key principles such as mandatory bid rules, disclosure obligations and protection of minority shareholders. The chapter explores the challenges of harmonizing takeover regulations across diverse Member States and balancing market efficiency with investor protection. By reviewing case law and national implementations, it highlights recent reforms and their impact on corporate control dynamics within the EU. The discussion underscores the role of takeover regulation in fostering competitive, transparent and integrated European capital markets.

A large body of research examines suburban built environments and the effects of urban sprawl, car dependency, and low density on social connections. Yet little attention has been given to how suburban design influences neighbourhood connections for culturally diverse groups, particularly migrants. This chapter focuses on Iranian migrants in Melbourne, who come from a culture that values hospitality and strong neighbourhood ties. Many have faced high residential mobility and settlement in suburban contexts that differ physically and culturally from Iranian neighbourhoods, often challenging their sense of connection to neighbours and place. The chapter investigates how the urban form of suburban Melbourne shapes migrants’ belonging, identification, and expectations of their environment. It highlights the role of liveability indicators and the 20-minute neighbourhood framework in shaping neighbourhood experiences. Finally, it suggests planning policies to strengthen migrants’ capacity to build connections and foster a deeper ‘sense of place.’

This chapter discusses the ideas and practices that shaped family and kinship relationships in the Song dynasty. After a brief introduction to basic principles that had governed Chinese kinship for centuries prior to the Song, the chapter traces how the growing economy, expansion of the examination system, and enlargement of the literate elite class over the course of the dynasty contributed to changes in family structures and kinship practices. It describes the elaboration of new marriage strategies, the expansion of concubinage, and the development of new institutions to promote kinship solidarity, all of remained central to Chinese kinship relations down to the early twentieth century.

This chapter examines creativity as a cognitive, social, and motivational process that involves divergent idea generation and convergent refinement. It contextualizes generative AI within the long history of technology shaping art, from Renaissance science to modern algorithmic art. Through cases such as AARON and MidJourney, the authors question whether AI creativity is “real” or an illusion shaped by anthropomorphism. They highlight how motivation, childhood development, and organizational culture shape creativity, and how AI can act as collaborator, accelerator, or threat depending on its use. Ultimately, human–AI co-creativity is positioned as both an opportunity and a challenge for redefining authorship and innovation.

This chapter reconsiders the prevailing logic of risk categorisation in artificial intelligence regulation, focusing on how the European Union?s Artificial Intelligence Act operates in practice as a four-tiered, domain- and use-case-based framework. While the Act introduces a structured and ostensibly proportionate approach to governance, it rests on an assumption that risk is a function of application domain rather than the behavioural, contextual and technical dynamics that shape real-world harm. Drawing on the interdisciplinary field of behavioural data science – which integrates behavioural science, cognitive psychology and empirical data analytics – we argue for a scenario-specific model of risk assessment. This model accounts for how artificial intelligence systems interact with human cognitive biases, demographic vulnerabilities and shifting deployment conditions. By reconceptualising risk as an emergent property of human–machine co-production, the chapter introduces a semi-quantitative scoring framework grounded in behavioural indicators. This framework enables regulators and developers to assess risk more adaptively and responsively across general-purpose and domain-specific artificial intelligence applications. In doing so, the chapter proposes a shift from assumption-driven to evidence-based governance, positioning behavioural data science as a critical lens for advancing socially robust artificial intelligence regulation.

Behavioural Data Science has become a crucial tool in finance, helping researchers and practitioners understand the behaviours of individuals and markets. This chapter investigates how unstructured customer feedback data, collected from the online review platform Trustpilot, can be used to model consumer behaviour in financial services. Through a large-scale text analysis of customer reviews, we examine (i) the differences in how customers perceive traditional financial institutions compared to fintech firms; (ii) the predictive power of context-dependent sentiment in forecasting customer satisfaction; and (iii) the methodological advantages of using real-time, unstructured behavioural data for improving customer experience analytics. Our findings indicate that traditional financial service providers and fintech companies often elicit orthogonal sentiment patterns from customers – even when offering similar services – highlighting the importance of brand identity and user expectation in behavioural outcomes. We also demonstrate that models trained on Trustpilot-derived textual data outperform conventional natural language processing approaches in predicting customer satisfaction. By embedding sentiment analysis within a broader Behavioural Data Science framework, this chapter illustrates how financial institutions can more accurately interpret and respond to consumer feedback, contributing to more adaptive, customer-centric service design in both traditional and emerging financial ecosystems.

Behavioural Data Science represents the convergence of behavioural theory, computational modelling and empirical analysis to understand, predict and shape human, algorithmic and systems behaviour. As the field matures, its relevance hinges not only on its conceptual elegance but on its demonstrable value in addressing real-world challenges. This chapter serves as an introduction to Part V, which presents a broad spectrum of applied domains – ranging from healthcare and education to financial services, digital marketing, cybersecurity, public policy and environmental sustainability. Each application domain embodies the core tenets of Behavioural Data Science: interdisciplinarity, responsiveness to contextual variability and methodological agility. The chapter explores how behavioural theory is operationalised through data science techniques and how complex behavioural systems are navigated and intervened upon using algorithmic strategies. It critically assesses the translational challenges encountered when models developed in controlled settings are deployed in dynamic, heterogeneous environments. Ethical considerations – ranging from data governance and fairness to manipulation and informed consent – are shown to be not peripheral but integral to application design and implementation. Drawing on illustrative cases and cross-cutting insights from the chapters that follow, this chapter argues that applications of Behavioural Data Science must balance predictive power with interpretability, impact with equity and automation with accountability. In doing so, the chapter positions Part V not merely as a repository of case studies but as a reflection of how Behavioural Data Science performs under real-world constraints – and how its promises are tested, refined or reimagined in practice.

This chapter explores the complexities and responsibilities of allyship in planning and placemaking, emphasising the critical importance of adopting a Country-centred approach. Reflecting on personal experiences as a non-Indigenous planner, the author highlights the necessity of confronting the colonial roots of land-use planning in Australia and embracing a more inclusive, two-way model of planning that incorporates Indigenous perspectives and values. Drawing on collaborative experiences with Indigenous colleagues and communities, the chapter identifies practical strategies for systemic change, including building team capacity, developing Country-informed principles, and addressing power imbalances in project governance. The author argues for a courageous and humble approach to allyship, urging non-Indigenous Australians to actively participate in truth-telling, reconciliation, and the healing of Country. Ultimately, the chapter advocates for a transformative shift towards inter-cultural practice, promoting a sustainable and equitable future grounded in mutual respect and shared responsibility for Country.

This chapter examines the creation of the U.S. presidency under Article II, arguing that the Executive Vesting Clause grants a limited power to execute laws, not a broad reservoir of executive authority. Unlike Article I’s enumerated legislative powers, Article II’s vesting of “the executive Power” omits “herein granted,” prompting debates over its scope. The chapter explores the Constitutional Convention’s intent and influences like Locke and Blackstone and contends that Article II’s opening grant primarily assigns the President the duty to execute Congress’s laws, with other powers – such as the commander-in-chief power or treatymaking – explicitly enumerated. It critiques expansive views, like Theodore Roosevelt’s stewardship theory, as deviations from the original, formalist design, which sought an energetic executive while ensuring the presidency would not devolve into monarchy. The chapter clarifies that the “unitary executive” debate concerns the scope, not unity, of presidential power, emphasizing that the Constitution cabins executive authority so that such authority can be safely entrusted to a single chief magistrate.

I present historical background, contemporary status, and potential future development of the psychology of religion (PoR) in Estonia, 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.