History of behavioural data science

Behavioural Data Science is an interdisciplinary field at the intersection of behavioural science, data science, computer science, statistics, engineering and economics. This chapter offers a historical overview of the field’s evolution –from foundational developments in early computation and decision theory to the modern integration of machine learning, artificial intelligence and digital behavioural data. We explore major milestones, methodological innovations and successful applications across health, education, business and public policy. In addition to tracing this growth, we address the complex challenges the field faces, including ethical concerns, data limitations and interpretability of algorithmic models. We demonstrate how these formative tensions shaped the field’s development and continue to inform its future trajectory. The chapter concludes with a forward-looking perspective on how emerging technologies such as generative AI and quantum computing may further refine the goals, tools and ethical frameworks of Behavioural Data Science.