معرفی
Prof. dr. ir. Marcel Boumans is a Professor of History of Economics at Utrecht School of Economics, Utrecht University, specializing in the intersection of the history, philosophy, and methodology of economics. He has held visiting positions at Duke University, UCLA, Université Paris 1, and the London School of Economics. His academic leadership includes heading the Applied Economics section and coordinating the double bachelor program in Mathematics and Economics.
- PhD in Economics (1992) and MSc in Mathematics (1988) from University of Amsterdam and Delft University of Technology, respectively.
- Teaching qualifications include Senior Qualification Education (2016) and Basic Education Qualification (2014) in science education.
His research explores empirical practices in economics, focusing on measurement, modeling, and the role of mathematics in shaping economic knowledge. Key works include How Economists Model the World into Number and Science Outside the Laboratory. Current projects include a situational approach to misinformation and a book manuscript titled Shaping the Phenomena.
Recent publications analyze statistical inference, visual epistemology, and the computerization of economics. His 15 most recent articles (2025-2019) span topics like data reliability, econometric controversies, and the cultural history of mathematical tools. These works reflect interdisciplinary engagement with philosophy, data science, and policy studies.
Scientific honors include the KVS Professor of History of Economics (2024–present) and fellowships at Duke University’s Center for the History of Political Economy, NIAS, and Tinbergen Institute. He has served as co-editor of the Journal of the History of Economic Thought and on editorial boards of leading journals.
As a supervisor, he co-promoted dissertations on macroeconomic modeling, identity-based inequality, and policy applications of economic theory. He actively participates in academic governance, including roles in the History of Economics Society and advisory boards for Data Science and the Society for Philosophy of Science in Practice.
