Spring 2017 Symposium
The Field Committee in Decision Science includes faculty members and doctoral students who have a central interest in theories, models, and phenomena in human decision making.
Please join us on Friday, April 21, 2017, from 9 a.m.-2:15 p.m. in 1100 Tawes for our Decision Science Field Committee Symposium where we will have 3 prominent guest speakers:
- Michael Platt, University of Pennsylvania
- Jennifer Trueblood, Vanderbilt
- Anuj Shah, University of Chicago
Lunch will be provided. If you are planning to attend the event, please RSVP by sending an email to email@example.com. Space is limited!
Michael Platt, University of Pennsylvania
Professor of Marketing
Professor of Psychology
Professor of Neuroscience
Michael Platt’s lab tries to understand how the brain makes decisions. They are particularly interested in the biological mechanisms that allow people make decisions when the environment is ambiguous or complicated by the presence of other individuals. Techniques such as eye-tracking, brain imaging and genomics are used to answer these questions. Research interests include Control of Action, Decision Processes, and Individual Differences and Behavior Genetics.
Full profile: psychology.sas.upenn.edu/people/michael-l-platt
Anuj Shah, University of Chicago
Associate Professor of Behavioral Science
Anuj K. Shah studies the psychology that arises from facing resource scarcity. In one line of work, he studies how being short on money and time affects decision-making. In another line of work, he studies how behavioral science can help shape interventions to reduce crime and violence. He is a member of the Scientific Advisory Board at ideas42, a social science research and development lab which uses scientific insights to design innovative policies and products.
Full Profile: www.chicagobooth.edu/faculty/directory/s/anuj-k-shah
Jennifer Trueblood, Vanderbilt
Assistant Professor of Psychology
Jennifer Trueblood's research takes a joint experimental and computational modeling approach to study human judgment, decision-making, reasoning, and memory. She is interested in understanding (1) how people make decisions when faced with multiple alternatives, (2) how dynamically changing information affects decision processes, (3) how people reason about complex causal events, and (4) how different perspectives, contexts, and frames can lead to interference effects in decision-making and memory. To address these questions, she develops probabilistic and dynamic models that can explain behavior and uses hierarchical Bayesian methods for data analysis and model-based inference.