Pro-social nudging has become a prominent strategy in behavioural public policy, aiming to encourage cooperative, altruistic or community-enhancing behaviours without coercion or significant economic incentives. By subtly altering the choice architecture or informational framing of decisions, pro-social nudges seek to elicit actions that benefit others, such as donating to charity, conserving energy, recycling, volunteering or adhering to public health measures. This chapter offers a critical, data-driven examination of pro-social nudging as a methodological and theoretical construct within behavioural data science. It traces the evolution of the concept from early experimental interventions to digitally enabled nudging in algorithmically mediated environments. The chapter investigates how pro-social nudges function across different domains and populations, evaluates the psychological mechanisms and empirical evidence behind their efficacy and considers the role of emerging technologies – such as AI, digital twins and real-time behavioural tracking – in designing, personalising and evaluating these interventions. Ethical concerns, including paternalism, manipulation and differential impacts across social groups, are addressed in depth. By integrating theoretical reflection with practical applications, this chapter highlights the importance of context-sensitive, transparent and equitable designs in pro-social nudging strategies. It concludes with a call for participatory co-design, longitudinal validation and interdisciplinary collaboration to ensure that pro-social nudges serve the public good without compromising autonomy or equity.

Human–machine teaming (HMT) represents a critical frontier in Behavioural Data Science, where cognitive, social, cultural and algorithmic factors converge within complex systems. While traditional human–computer interaction (HCI) research has explored usability and task efficiency, human–machine teaming introduces new imperatives: mutual predictability, adaptive trust calibration, shared mental models and contextually sensitive collaboration. These imperatives are increasingly important in sectors such as defence, healthcare, manufacturing and disaster response, where human and machine agents co-perform roles that require high-stakes coordination. This chapter integrates systems thinking and cross-cultural theory to provide a framework for understanding HMT through the lens of Behavioural Data Science. It examines how systemic factors – ranging from team topology to environmental volatility – influence teaming outcomes, and how cultural variation impacts expectations, acceptance and performance of AI teammates. Drawing on empirical case studies and simulation-based experiments, we identify design principles for effective teaming and highlight methodological challenges in measuring team efficacy and behavioural alignment. We also interrogate ethical and philosophical issues in the automation of team roles and decision-making authority. Ultimately, we argue that human–machine teaming is not merely a technological phenomenon, but a socio-technical system embedded within organisational, institutional and cultural structures. Behavioural Data Science is uniquely positioned to illuminate these interdependencies and guide the development of trustworthy, transparent and inclusive teaming frameworks.

Recent advances in data-driven behavioural science and, particularly, in Behavioural Data Science, saw the rise of applying natural language processing techniques to understanding and modelling human behaviour, algorithmic behaviour, as well as behaviour of complex human–machine systems. From associative modelling in the standard psychological experiments to understanding emotional arcs in novels and movies through the way humans, algorithms and complex systems use language, this chapter demonstrates how computational linguistics methods allow behavioural methodology to go beyond the ‘sterile’ laboratory environments into the field using data, test hypotheses at scale and have scalable practical impact. This chapter also shows how language-based modelling can shed light on cognition, decision-making, judgements and cultural evolution. Using empirical example of a large-scale Behavioural Data Science study, this chapter also demonstrates how language analysis can be used as a ‘truth serum’, helping to obtain underlying human preferences from subjective judgements usually provided in surveys.

This chapter critically reviews the success of existing algorithms in explaining and predicting human behaviour. While traditional statistical methods have limitations in this area, algorithmic approaches have gained popularity. The chapter covers a range of algorithms, including decision trees, neural networks and clustering algorithms, evaluating their strengths and limitations in various applications. It also considers ethical concerns, such as bias and privacy violations and the need for transparency and explainability. The chapter emphasises the importance of interdisciplinary collaboration between computer science, statistics and behavioural science, and the need for ongoing development and refinement of these methods. By evaluating the effectiveness of algorithmic approaches to human behaviour, this chapter is a valuable resource for researchers and practitioners in the field.

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.

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.

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.

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.

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.

The proliferation of artificial intelligence (AI) technologies has introduced a profound paradox: while AI promises efficiency, automation and insight, its growing energy consumption, computational demands and infrastructural dependencies increasingly strain ecological and technological limits. This chapter addresses the behavioural and systemic dynamics that shape the sustainability of AI itself – not only in terms of ethics or outcomes, but in terms of its continued viability in a resource-constrained world. Drawing from systems theory, behavioural modelling and computational sustainability, we examine how AI behaves as a system-of-systems, how those behaviours accumulate energy and material costs and what design principles might render AI more compatible with long-term environmental and economic thresholds. By analysing patterns such as runaway model scaling, overfitting to hardware architectures and unsustainable data pipelines, the chapter outlines a Behavioural Data Science approach to understanding and modifying AI’s systemic impact. Through case studies and modelling strategies, it argues that AI’s future must be governed not only by performance metrics, but by its capacity to sustain itself – ecologically, computationally and institutionally – within the planetary boundaries.

This chapter explores how online data and mobile phone technologies have transformed our ability to study collective human behaviour. By analysing vast digital traces – from web searches, Wikipedia usage and social media posts to geolocation and sensor data collected via smartphones – researchers can now observe, model and predict individual and societal behaviours at unprecedented scales and temporal resolutions. This chapter highlights key methodological developments and applications in domains such as public health, economics, finance, mental well-being and urban mobility. Through detailed examples, we illustrate how digital behavioural data reveal patterns of attention, intention and sentiment, offering new insights into how people respond to cultural events, policy shifts and environmental changes. They also address the ethical and privacy challenges posed by the collection and use of granular digital data, especially the limitations of traditional anonymisation techniques. Ultimately, the chapter highlights both the promise and the responsibility that come with leveraging online and mobile data for understanding and shaping collective behaviour in the digital age.

This chapter presents a structured account of the Framework and Topology of Methods in Behavioural Data Science, expanding upon a four-stage methodological process – data collection, data processing, feature engineering and model development – and introducing four foundational conceptual pillars: Hypothesis Testing at Scale, Data Science Modelling for Behavioural Problems, Gap Detection and Hybrid Modelling. These pillars not only represent distinct methodological orientations but also correspond to divergent epistemological and ontological commitments within the field. The chapter provides a comprehensive overview of methods and tools used at each stage of the Behavioural Data Science workflow, illustrates how emerging technologies such as large language models (LLMs) reshape methodological choices and concludes with ethical considerations and future directions. In so doing, it advances a unified topology of Behavioural Data Science methods capable of accommodating both theory-driven and data-driven approaches, while responding to gaps in traditional behavioural science paradigms.

This chapter explores how statistical mechanics and cyber-physical systems (CPS) can be applied to understand, model and predict behavioural phenomena. While traditionally rooted in physics and engineering, these paradigms offer novel approaches to behavioural data science, especially when dealing with collective dynamics, stochasticity and feedback within complex human and socio-technical systems. We explore the conceptual translation of entropy, phase transitions and emergent behaviour from physics to behavioural modelling, alongside the increasing convergence of CPS and behavioural sensing platforms. Through worked examples, such as crowd evacuation, financial panic and human–machine coordination in urban environments, this chapter demonstrates how statistical mechanics and CPS not only extend behavioural theory but also provide practical frameworks for real-time behavioural prediction and intervention. The ethical implications of using such models – especially those with predictive or persuasive power – are also discussed, ensuring the conversation remains grounded in responsible innovation. Building upon these considerations, this chapter also examines the integration of system identification, feedback control and stability analysis into behavioural cyber-physical systems. System identification enables CPS to infer latent behavioural dynamics from observational data, supporting adaptive modelling under uncertainty and partial observability. Dynamic closed-loop architectures use these models to enable real-time adaptation to evolving behaviours with Lyapunov stability theory providing a principled framework for certifying safe and predictable responses. Applications in smart agriculture and defence robotics show how distributed autonomous systems achieve robust coordination, adaptive authority delegation and stable operation under mission-critical conditions. These advances highlight the critical role of integrating control-theoretic principles into behavioural models to ensure the reliability and predictability of behaviour-driven cyber-physical systems.

Agent-based modelling (ABM) in social networks offers a powerful framework for simulating individual behaviours and emergent collective patterns within dynamic and interconnected populations. This chapter explores the conceptual foundations, methodological innovations and behavioural implications of ABM in the context of social networks, with a particular focus on the modelling of communication, influence, cooperation and contagion. Drawing from the fields of behavioural science, network theory and computational social science, the chapter presents ABM as a generative tool for understanding how micro-level decision-making rules produce macro-level phenomena. It also critically evaluates the increasing use of AI-driven agents – including large language model (LLM)-powered agents and synthetic personas – in simulating realistic and context-sensitive behaviours within artificial societies. The chapter engages with ethical and methodological challenges, including representation, explainability and the problem of behavioural validity. A worked example is included, illustrating how agent-based simulations can be applied to study misinformation diffusion and norm formation in online social networks. Ultimately, the chapter argues that agent-based modelling in social networks not only advances Behavioural Data Science methodologically, but also fosters new forms of interpretive insight into the dynamics of collective behaviour in an age of digital mediation.

The recent surge in conversational AI has opened up new avenues for the application of Behavioural Data Science, with Generative AI, particularly LLM models such as ChatGPT, representing a promising platform for analysing human behaviour. This chapter provides an overview of the role of Generative AI models in Behavioural Data Science, highlighting their strengths and limitations. The chapter begins by introducing the principles of Generative AI models and their potential applications in Behavioural Data Science. It discusses the advantages of using Generative AI models to study human behaviour, such as their ability to analyse large volumes of unstructured data and their capacity to learn from interactions with users. The chapter then describes the different ways in which Generative AI models can be used in Behavioural Data Science, such as analysing sentiment, predicting behaviour and generating insights from user interactions. It discusses the challenges associated with using Generative AI models, such as the need for accurate training data and the potential for bias in the model. The chapter also addresses the ethical concerns associated with the use of Generative AI models, such as privacy violations and the potential for unintended consequences. It discusses ways to address these concerns, such as implementing transparency and explainability in Generative AI models. The chapter concludes by discussing the future of Generative AI models in Behavioural Data Science, highlighting the potential for interdisciplinary collaboration with other fields such as psychology and sociology. It emphasises the need for continued development and refinement of Generative AI models and their associated methods for studying human behaviour. This chapter provides an overview of the role of Generative AI models in Behavioural Data Science, highlighting their strengths and limitations. It serves as a valuable resource for researchers and practitioners in the field who are interested in utilising Generative AI models to analyse and understand human behaviour.

This chapter provides an overview of Behavioural Data Science and categorises it into three strands: Human Behaviour, Algorithmic Behaviour and Systems Behaviour. The Human Behaviour strand seeks to understand and predict human behaviour using large datasets in a wide variety of applications. The Algorithmic Behaviour strand seeks to improve the performance of algorithms by studying how algorithms behave as well as how they process data about humans and systems, predicting future patterns. The Systems Behaviour strand studies how humans and algorithms collaborate in complex systems. The chapter also highlights the challenges and limitations of Behavioural Data Science, including ethical considerations and potential biases in data interpretation. It concludes with a discussion of the future of Behavioural Data Science and its potential for further advancements.

Cybersecurity remains one of the most technically complex domains of contemporary risk management, yet much of its vulnerability stems not from systems themselves but from the behavioural patterns of their users and adversaries. This chapter introduces a Behavioural Data Science perspective on cybersecurity – an approach that foregrounds the cognitive, social and affective processes underlying digital decision-making and threat response. Moving beyond the dominant paradigm of technological hardening, this chapter explores how insights from behavioural science, combined with computational modelling, can illuminate the conditions under which individuals make secure or insecure choices. It develops the concept of behavioural cyber-risk modelling, investigates the role of human–machine asymmetries and critiques static compliance-based regimes in favour of adaptive, context-aware systems. By synthesising empirical research and theoretical developments, the chapter proposes a new agenda for cybersecurity rooted in the nuanced realities of human behaviour, data-driven learning and interdisciplinary engagement.