
Alec Smith
Associate Professor · Behavioral Economics
Virginia Polytechnic Institute and State UniversityAbout
Alec Smith is an Associate Professor in the Department of Economics at Virginia Tech, with interdisciplinary affiliations as a Faculty Affiliate in the School of Neuroscience and the Kellogg Center for Philosophy, Politics, and Economics. He serves as Co-Director of the Virginia Tech Economics Laboratory, where he leads experimental research on human decision-making.
His research lies at the intersection of behavioral economics, experimental economics, and neuroeconomics, focusing on how cognitive and emotional processes shape economic decisions. Key interests include risk perception, social emotions (e.g., guilt, frustration), cognitive load, neural correlates of decision-making, and market dynamics such as bubbles. His work often integrates neuroimaging, transcranial stimulation, and eye-tracking with economic experiments.
The most recent publications reflect a strong trend toward methodological rigor, reproducibility, and the application of machine learning to behavioral data. Themes include replication studies, variability in neuroimaging analysis, and modeling of bargaining and social preferences. His research has been published in high-impact journals such as Nature, Proceedings of the National Academy of Sciences, Management Science, and Games and Economic Behavior.
Smith has collaborated with leading scholars including Colin Camerer, P. Read Montague, and Antonio Rangel, underscoring his integration into the forefront of neuroeconomic research. While no formal awards are listed, his publication record and leadership roles suggest significant recognition in the field.
He advises students and researchers through his lab and departmental roles, contributing to training in experimental and neuroeconomic methods. His work is supported by institutional infrastructure such as the Virginia Tech Economics Laboratory, which facilitates controlled behavioral experiments. Future research directions likely include expanding the use of AI in behavioral prediction, studying pandemic-related behavioral shifts, and further exploring the neural underpinnings of social preferences.
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