Tobias Rebholz

Tobias Rebholz is a quantitative psychologist and econometrician currently serving as a Visiting Research Scholar in the Management and Organizations Area at the Fuqua School of Business, Duke University. Previously, he was a postdoctoral research associate in the Social Cognition and Decision Sciences group at the Department of Psychology, University of Tübingen, Germany. His academic journey includes a PhD in Psychology from the DFG-funded Research Training Group 'Statistical Modeling in Psychology' at the University of Tübingen (2023), an M.Sc. in Economics with a focus on Econometrics and Applied Economics from the University of Konstanz (2017-2020), and a B.Sc. in Business Studies and Economics with a major in Psychoeconomics from the same institution (2014-2017).

Dr. Rebholz's research centers on quantitative methods in social cognition and judgment and decision-making (JDM). His methodological expertise spans statistics, machine/deep learning, Bayesian inference, and data science, which he applies to investigate phenomena such as judgment aggregation, wisdom of crowds, joint decision-making, advice taking, multidimensional belief updating, anchoring effects, hindsight bias, and science acceptance. A key theme in his current work is quantifying the informational influence of qualitative information—particularly that generated by large language models (LLMs)—on human judgment processes.

His recent publications demonstrate a growing focus on human-AI interaction, examining how people integrate advice from generative AI systems, the reliability of various cognitive biases, and the development of quantitative models for understanding information sampling. His work often employs advanced statistical modeling techniques to address ecological constraints in real-world decision contexts, with increasing emphasis on the intersection of traditional psychological phenomena and modern AI technologies.

Dr. Rebholz has taught various courses at the University of Tübingen, including 'Heuristics and Biases,' 'Introduction to Machine Learning,' 'R-Programming,' and 'Machine Learning for Data-Driven Decision-Making.' His teaching reflects his interdisciplinary approach, bridging psychology, statistics, and computational methods to equip students with practical skills for behavioral research in the digital age.