The Cambridge Handbook of Behavioural Data Science offers an essential exploration of how behavioural science and data science converge to study, predict, and explain human, algorithmic, and systemic behaviours. Bringing together scholars from psychology, economics, computer science, engineering, and philosophy, the Handbook presents interdisciplinary perspectives on emerging methods, ethical dilemmas, and real-world applications. Organised into modular parts-Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications-it provides readers with a comprehensive, flexible map of…mehr
The Cambridge Handbook of Behavioural Data Science offers an essential exploration of how behavioural science and data science converge to study, predict, and explain human, algorithmic, and systemic behaviours. Bringing together scholars from psychology, economics, computer science, engineering, and philosophy, the Handbook presents interdisciplinary perspectives on emerging methods, ethical dilemmas, and real-world applications. Organised into modular parts-Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications-it provides readers with a comprehensive, flexible map of the field. Covering topics from cognitive modelling to explainable AI, and from social network analysis to ethics of large language models, the Handbook reflects on both technical innovations and the societal impact of behavioural data, and reinforces concepts in online supplementary materials and videos. The book is an indispensable resource for researchers, students, practitioners, and policymakers who seek to engage critically and constructively with behavioural data in an increasingly digital and algorithmically mediated world.
The Cambridge handbook of behavioural data science Preface List of contributors Handbook abstract Introduction: how to read this book Part I. Introduction to Behavioural Data Science: 1. History of behavioural data science: successes and challenges 2. Overview of behavioural data science 3. Behavioural data science: framework and topology of methods Part II. Human Behaviour: 4. Behavioural data science for understanding human decisions, choices, and judgement 5. Psychological theories of decision making under risk 6. Prediction oriented behavioural research and its relationship to classical decision research 7. The ABCs of behavioural influence 8. Word and sentence embedding methods for studying human behaviour 9. Predictive Bayesian Modelling in cognitive sciences 10. Human aspects of AI-related risks: a behavioural data science approach Part III. Algorithmic Behaviour: 11. Generative AI and behavioural data science 12. How successful are existing algorithms in explaining and predicting human behaviour? 13. Emotion and Big Data: The Elephant in the Room? 14. Smart Bots? A Behavioural Approach to Measure The 'Intelligence' of Conversational AI Pre-Chat GPT 15. Chatgpt & CO - exploring conversational abilities of large language models from a behavioural perspective 16. Machine behaviour 17. Modelling choice behaviour using artificial intelligence 18. anthropomorphic learning: hybrid modelling approaches combining decision theory and machine learning Part IV. Systems and Culture: 19. Systems, culture, and human-machine teaming 20. Cognitive networks as models of cognition and behaviour: an introduction 21. Agent-based modelling in social networks 22. Modelling context-dependent behaviour 23. A short primer on historical natural language processing 24. Behavioural data in complex economic and business systems 25. Applications of statistical mechanics and cyber-physical systems to behaviour 26. Systems behaviour for sustainable AI 27. Systems behaviour and experimentation 28. Quantum mechanics of human perception, behaviour and decision-making: a do-it-yourself model kit for modelling optical illusions and opinion formation in social networks Part V. Applications: 29. Applications of behavioural data science 30. Pro-social nudging 31. Social media analytics 32. Quantifying luck 33. Quantifying the connection between scenic beauty and reported health using deep learning and econometrics 34. Money, methodology, and happiness: using big data to study causal relationships between income and well-being 35. Human-data interaction: the case of databox 36. Natural language processing in behavioural data science: using computational linguistics to understand and model behaviour 37. Understanding collective behaviour using online data and mobile phones 38. Burstier events: analysing human memory over a century of events using the New York 39. Behavioural data science in financial services 40. XR, VR, and AR applications in behavioural data science 41. On cryptoasset traders' behaviour 42. Behavioural data science of cybersecurity 43. Behavioural data science ethics and governance pre-AI act: From research data ethics principles to practice: data trusts as a governance tool 44. Behavioural data science ethics and governance post-AI act: responsible approach to network and collective choice modelling Part VI. Concluding Remarks: List of main abbreviations and acronyms Glossary.
The Cambridge handbook of behavioural data science Preface List of contributors Handbook abstract Introduction: how to read this book Part I. Introduction to Behavioural Data Science: 1. History of behavioural data science: successes and challenges 2. Overview of behavioural data science 3. Behavioural data science: framework and topology of methods Part II. Human Behaviour: 4. Behavioural data science for understanding human decisions, choices, and judgement 5. Psychological theories of decision making under risk 6. Prediction oriented behavioural research and its relationship to classical decision research 7. The ABCs of behavioural influence 8. Word and sentence embedding methods for studying human behaviour 9. Predictive Bayesian Modelling in cognitive sciences 10. Human aspects of AI-related risks: a behavioural data science approach Part III. Algorithmic Behaviour: 11. Generative AI and behavioural data science 12. How successful are existing algorithms in explaining and predicting human behaviour? 13. Emotion and Big Data: The Elephant in the Room? 14. Smart Bots? A Behavioural Approach to Measure The 'Intelligence' of Conversational AI Pre-Chat GPT 15. Chatgpt & CO - exploring conversational abilities of large language models from a behavioural perspective 16. Machine behaviour 17. Modelling choice behaviour using artificial intelligence 18. anthropomorphic learning: hybrid modelling approaches combining decision theory and machine learning Part IV. Systems and Culture: 19. Systems, culture, and human-machine teaming 20. Cognitive networks as models of cognition and behaviour: an introduction 21. Agent-based modelling in social networks 22. Modelling context-dependent behaviour 23. A short primer on historical natural language processing 24. Behavioural data in complex economic and business systems 25. Applications of statistical mechanics and cyber-physical systems to behaviour 26. Systems behaviour for sustainable AI 27. Systems behaviour and experimentation 28. Quantum mechanics of human perception, behaviour and decision-making: a do-it-yourself model kit for modelling optical illusions and opinion formation in social networks Part V. Applications: 29. Applications of behavioural data science 30. Pro-social nudging 31. Social media analytics 32. Quantifying luck 33. Quantifying the connection between scenic beauty and reported health using deep learning and econometrics 34. Money, methodology, and happiness: using big data to study causal relationships between income and well-being 35. Human-data interaction: the case of databox 36. Natural language processing in behavioural data science: using computational linguistics to understand and model behaviour 37. Understanding collective behaviour using online data and mobile phones 38. Burstier events: analysing human memory over a century of events using the New York 39. Behavioural data science in financial services 40. XR, VR, and AR applications in behavioural data science 41. On cryptoasset traders' behaviour 42. Behavioural data science of cybersecurity 43. Behavioural data science ethics and governance pre-AI act: From research data ethics principles to practice: data trusts as a governance tool 44. Behavioural data science ethics and governance post-AI act: responsible approach to network and collective choice modelling Part VI. Concluding Remarks: List of main abbreviations and acronyms Glossary.
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