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Richard GonzalezRichard Gonzalez

Center Director, Research Center for Group Dynamics, Institute for Social Research
Co-Director, BioSocial Methods Collaborative
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Integrative Systems and Design, College of Engineering


E-mail: Email Richard Gonzalez
Address: Research Center for Group Dynamics
Institute for Social Research
University of Michigan
426 Thompson Street
Ann Arbor, Michigan 48106
Phone: 734-647-6785

Richard Gonzalez

About Me

I received my PhD in 1990 from Stanford University in Psychology. I worked with Phoebe Ellsworth studying psychology and law and with Amos Tversky studying mathematical modeling and judgment and decision making. I spent seven years at the University of Washington’s Psychology department, a sabbatical year at Princeton University, and have been at the University of Michigan’s Psychology department since 1997. More about me >>


My research interests focus on judgment and decision making (JDM). Given that so many topics in psychology are related to JDM it makes it look as though I work on many different topics. Actually, I see a simple theme across all my research. I am interested in how people make judgments and what influences their decisions and choices. More about Research >>


I’ve been fortunate to work with amazing colleagues, coauthors, collaborators, and students throughout my career. Read about People >>


  • Theories of Social Psychology
  • General Linear Modeling Course
  • Multivariate Statistics
  • Generalized Linear Modeling Course
  • Structural Equations Modeling Course
  • Statistics Animation Page
  • Design Science PhD Program

More about Teaching >>

Recent Posts

Testing prospect theory’s predictive accuracy

We test original and cumulative prospect theories for their ability to predict out-of-sample cash equivalences of three outcome gambles when estimated (trained) on two outcome gambles. The results are surprising in that both theories are systematically off but in different ways suggesting opportunity for additional theory development.

Developing a new approach to modeling the ground reaction forces in elite human runners

Runners are commonly modeled as spring–mass systems, but the traditional calculations of these models rely on discrete observations during the gait cycle (e.g. maximal vertical force) and simplifying assumptions (e.g. leg length), challenging the predicative capacity and generalizability of observations. We present a method to model runners as spring–mass systems using nonlinear regression (NLR) and the full vertical ground reaction force (vGRF) time series without additional inputs and fewer traditional parameter assumptions.