Michel Regenwetter


Research Interests

  • Decision Making
  • Mathematical Psychology
  • Behavioral Social Choice
  • Behavioral Economics

Research Description

Individual preferences fluctuate over time and differ among people. Few models of utility and decision making attempt to capture this fundamental fact explicitly. Prof. Regenwetter's primary goal is to model, measure, and predict preference and choice behavior when it is allowed to vary. Random utility models are designed as a modeling language to capture and quantify the ubiquitous variability in choice and preference behavior. Prof. Regenwetter's primary interests can be categorized as falling within three paradigms: probabilistic measurement, social choice, and preference evolution over time. Probabilistic measurement theory reformulates axiomatic measurement structures (e.g., in decision theory) in a probabilistic framework and thereby makes them empirically (and statistically) testable. Social choice theory is the theory of aggregating individual preferences or choices into a social ordering or choice. Dr. Regenwetter's interest in social choice is behavioral. Using random utility models as measurement tools, he evaluates and compares competing social choice functions on empirical data of various kinds. Dr. Regenwetter studies preference change over time via stochastic process models in which random utilities are indexed by continuous time.


Ph.D. Mathematical Behavioral Sciences, University of California at Irvine

Awards and Honors

  • Fellow, Association for Psychological Science
  • Young Investigator Award, Society for Mathematical Psychology

Additional Campus Affiliations

Professor, Political Science

Recent Publications

Zwilling, C. E., Cavagnaro, D. R., Regenwetter, M., Lim, S. H., Fields, B., & Zhang, Y. (2019). QTEST 2.1: Quantitative testing of theories of binary choice using Bayesian inference. Journal of Mathematical Psychology, 91, 176-194. https://doi.org/10.1016/j.jmp.2019.05.002

Regenwetter, M., & Robinson, M. M. (2019). The construct-behavior gap revisited: Reply to Hertwig and Pleskac (2018). Psychological review, 126(3), 451-454. https://doi.org/10.1037/rev0000145

Regenwetter, M., & Cavagnaro, D. R. (2019). Tutorial on removing the shackles of regression analysis: How to stay true to your theory of binary response probabilities. Psychological Methods, 24(2), 135-152. https://doi.org/10.1037/met0000196

Davis-Stober, C. P., & Regenwetter, M. (2019). The 'Paradox' of Converging Evidence. Psychological review. https://doi.org/10.1037/rev0000156

Regenwetter, M., Hsu, Y. F., & Kuklinski, J. H. (2019). Towards Meaningful Inferences From Attitudinal Thermometer Ratings. Decision. https://doi.org/10.1037/dec0000106

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