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.

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 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.

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.

A natural starting point for the study of graphs with "random-like" properties is the notion of jumbled graphs, introduced by Thomason. We develop the properties of these pseudorandom graphs and study the related notion of quasirandom graphs and spectral properties of graphs, including (n, d, λ)-graphs, the expander mixing lemma, the Alon–Boppana Theorem, the Haemers–Hoffman–del Sarte bound, and the proof of the sensitivity conjecture.

This chapter argues that throughout his prose works, Wilde demonstrates ambivalence regarding the significance of fashion in relation to flesh, and this chapter traces that ambivalence through texts including The Picture of Dorian Gray, De Profundis, and “The Birthday of the Infanta.” Treating ugliness and beauty, monstrosity and martyrdom, this chapter demonstrates Oscar Wilde’s continued fascination with beautiful flesh that is betrayed as such flesh decays, rots, and becomes loose and baggy, like an ill-fitting garment. Wilde writes against the backdrop of eugenicist discourse, which proves a foil to his aesthetic project while simultaneously animating his response to criminal charges against him for gross indecency. But Wilde also looks to an earlier precedent to work out questions of flesh and fashion, particularly that of baroque painting and sculpture by artists such as Guido Reni and Gianlorenzo Bernini in their representations of the flesh of martyrs. The world remains unsure whether Wilde was a monster or a martyr; this chapter shows that Wilde himself shared those concerns.

Understanding behavioural data within complex economic and business systems requires a shift from linear, equilibrium-oriented perspectives to frameworks that accommodate emergence, adaptation and interdependence. Complexity science offers a valuable lens through which the dynamics of modern economic behaviours can be analysed, particularly in an era characterised by interconnected supply chains, automated financial ecosystems and data-intensive marketplaces. This chapter explores how behavioural data can be harnessed to illuminate patterns of interaction within such systems, examining feedback loops, tipping points, network effects and the role of institutional context. Drawing on case studies from financial markets, organisational decision-making and global production networks, this chapter illustrates how Behavioural Data Science can capture the micro-motives and macro-behaviours that define complexity. The work also addresses methodological challenges – including multi-level inference, non-stationarity and limited observability – and outlines new directions in agent-based modelling, behavioural data fusion and computational experimentation. The discussion culminates in a reflection on ethical considerations and the role of behavioural data in governing complex systems, especially under uncertainty.

This chapter provides an overview of how Behavioural Data Science can be used to understand human decision-making. It describes the methods and models used to study decision-making, including surveys, experiments and observational studies. The chapter also discusses the different types of decision-making models, including normative, descriptive and prescriptive models, and highlights their strengths and limitations. The chapter then explores the applications of Behavioural Data Science in understanding decision-making in various contexts, such as consumer behaviour, finance and healthcare. The chapter emphasises the importance of understanding the underlying mechanisms that drive decision-making, such as cognitive biases and social influences. This chapter provides a concise but informative overview of how Behavioural Data Science can be applied to understand human decisions, choices and judgements. It highlights the importance of studying decision-making in various contexts and provides insights into the methods and models used in this field. This overview serves as a foundation for further exploration of the methods and techniques used in this rapidly evolving field. The chapter concludes with a discussion of the future of Behavioural Data Science and its potential for further advancements in the study of human behaviour.