معرفی
Michael Nagel is a Research Associate at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has been involved in a DFG-funded project since 2021, focusing on advanced statistical modeling and behavioral economic analysis.
- Master of Science in Economics and Finance, University of Tübingen (2020)
- Bachelor of Science in Economics and Business Administration, University of Tübingen (2018)
His research spans Bayesian statistics, high-dimensional data analysis, and behavioral economics, with a particular focus on emotional decision-making in sports contexts and statistical methodology for structural equation modeling. He applies machine learning techniques to social science questions, especially in modeling complex human behaviors under uncertainty.
The two most recent publications highlight his dual focus: one on emotional drinking during soccer matches, linking suspense and surprise to alcohol use, and another on developing alternative Bayesian priors for high-dimensional estimation. These works reflect a strong interdisciplinary trend combining econometrics, psychology, and computational methods in social sciences.
While no formal scientific awards are listed in the provided texts, his work has been presented at notable academic forums including the Cluster of Excellence 'Machine Learning in Science' conferences and the DGPs Congress.
Michael Nagel has advised or collaborated on research projects, particularly within the Methods Center, and is supported by a DFG research grant. He actively contributes to open science through replication packages on GitHub. He is part of the research team led by Prof. Augustin Kelava, focusing on methodological innovation in the social sciences.
He is affiliated with the Methods Center at the University of Tübingen, a hub for advanced quantitative methods in social sciences. The center fosters interdisciplinary collaboration, particularly in integrating machine learning with traditional social science research. His work is aligned with the Cluster of Excellence 'Machine Learning in Science', indicating strong integration into cutting-edge research networks.
