Reimagining Economic Theory in the Age of Machine Learning and Big Data

Abstract : The rapid expansion of digital technologies and the growing availability of large-scale data are transforming the way economies operate and how economic questions are studied. As economies become increasingly data-driven, Machine Learning (ML) and Big Data are providing economists with new ways to understand, model, and predict economic behaviour. This chapter explores how these technologies are reshaping economic theory by complementing traditional approaches with more dynamic, predictive, and evidence-based methods. It traces the evolution of economic thought from classical and neoclassical perspectives to modern computational approaches, highlighting the need to move beyond conventional assumption-based models in an increasingly complex digital economy. The chapter also explains the roles of Artificial Intelligence (AI), Machine Learning, Deep Learning (DL), Natural Language Processing (NLP), and Large Language Models (LLMs), and examines their applications across sectors such as finance, e-commerce, healthcare, agriculture, governance, and education, with a particular focus on India's digital transformation. Rather than replacing established economic theories, these technologies strengthen them by enabling real-time analysis, more accurate forecasting, deeper behavioural insights, and better-informed policy decisions. While challenges related to data privacy, cybersecurity, algorithmic bias, and ethical governance remain, the article argues that integrating computational intelligence with established economic principles is essential for building more adaptive, inclusive, and empirically grounded economic frameworks for the digital age.