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Richard GonzalezRichard Gonzalez

Center Director, Research Center for Group Dynamics, ISR
Director, BioSocial Methods Collaborative, RCGD, ISR,
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Marketing, Stephen M Ross School of Business
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

Articles in Psychology

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.

Cardiovascular health and daily stress among White and Black Americans

Daily stress and cardiovascular reactivity may be important mechanisms linking cumulative life event stress with cardiovascular health and may help to explain racial health disparities. This study assessed links between trajectories of life event stress exposure, daily stressors, and cardiovascular reactivity among Black and White individuals.

Estimating a typical path from GPS data

Finding insights from sensor data such as GPS can be tricky. Commuting between home and work may not always follow the same path as some days there are additional stops for errands or alternate routes taken. We propose an algorithm for extracting a typical path from a collection of trips coded by GPS coordinates.

Predicting satisfaction in romantic relationships

We used machine learning to understand which constructs have greater predictive importance for perceived changes in satisfaction since the pandemic began and satisfaction over the prior week.