Richard 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
Reviewing the endowment-contrast model of happiness and well-being
If we have an amazing experience, such as an excellent meal or a the dream vacation, when does it become part of our endowment (i.e., another positive tick mark that we accumulate)or a source of comparison against which other relevant experiences are judged and contrasted? In the former case, the experience makes us happier, but in the latter case it can diminish our ability to enjoy future events.
Extending prospect theory to cases where probabilities are not known
We extend cumulative prospect theory to the domain of events, investigate two sources on nonlinearity on decision weights, and propose a two stage model of choice.
Standard errors for parameters in dyadic models
We show how to derive standard errors for a few basic models in dyadic data analysis. The chapter was written for Michael Browne’s festschrift.
Analyzing multivariate dyadic data for exchangeable dyads
This was our first paper on dyadic analysis. In retrospect, one of our main contributions was more pedagogical in that we showed how to get solid intuition about dyadic data and presented a framework in which to derive estimates and their standard errors. The framework illuminated several aspects of dyadic data, including why a correlation of dyadic means is sometimes difficult to interpret. Our results are identical to multilevel models using maximum likelihood estimation.
Understanding circumplex models: An application to vocational interest
In trying to understand an application of the circumplex structure to vocational structure, I struggled with geometric representation in theory development and data analysis. This short paper discusses some diagnostics one could apply to data to test the circumplex structure and provides some thoughts on the role of models in theory development.
Contextual cues influence judgment biases but only if they activate relevant motives
We show how judgment biases can vary as a function of contextual cues that are relevant to an individual.
Why sometimes we choose differently than how we advise
There several reasons why what’s good for the goose is not always seen as being good for the gander. The attributes of a decision may be weighted differently when choosing for oneself compared to when giving advise. We present a simple model and some data suggesting that advice tends to focus on one prominent attribute, but choice for oneself tries to deal with tradeoffs.
How do emotions help us learn and decide?
We developed a new paradigm where participants learn risky contingencies and then make decisions based on what they learned, all while in an MRI machine so BOLD can be measured along with the behavioral data. We find that affective processes play an important role in shaping subjective value in decision making as well as learning.
Supportive relationships can attenuate the appeal of choice
We show how being reminded of particular types of social relationships reduces the appeal of large choice sets.
Teaching concept generation to engineers
We show how our heuristic techniques help engineering designers explore the concept space to achieve more novel solutions and explore the problem/solution space more effectively.
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