Decision theory is the underlying theory of economics and management to study the judgments and behaviors of human decision-making, and some researchers have won the Nobel Memorial Prize in economic sciences for their outstanding contributions in the field of decision theory. Currently, with the increasing influence of artificial intelligence and data science on decision-making, the theoretical and applied research of decision theory is facing new challenges and opportunities. In this paper, we analyze the contributions of the Nobel Laureates in Economic Sciences in decision theory, and show their main research topics focused on utility theory, social choice theory and behavioral decision theory. Motivated by these classical theories, we focus 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; and develop the data-driven intelligent decision-making research paradigm from the perspectives of “utility learning”, “preference evolution” and “intelligent decision interpretability”. We highlight its differences with the classical decision theory research paradigm. Moreover, we discuss the applications of the data-driven intelligent decision-making in economics and management.