Decision theory is the foundational theory in economics and management for studying human judgment and decision-making behaviors. Some researchers have won the Nobel Memorial Prize in economic sciences for their outstanding contributions to the field of decision theory. Currently, with the growing influence of artificial intelligence and data science on decision-making, both the theoretical and applied research in decision theory are facing new challenges and opportunities. This paper analyzes the contributions of the Nobel Laureates in economic sciences in decision theory and shows their main research topics, which focus on utility theory, social choice theory, and behavioral decision theory. Motivated by these classical theories, our analysis focuses on the transformation of research paradigms in the specific area of decision theory from the perspective of the intersection among artificial intelligence, data science, and classical decision theory. A data-driven intelligent decision-making research paradigm is developed from the perspectives of “utility learning”, “preference evolution”, and “intelligent decision interpretability”. Its differences from the classical decision theory research paradigm are highlighted. Moreover, the applications of data-driven intelligent decision-making in economics and management are discussed.