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Tom Griffiths

Tom Griffiths


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Tom Griffiths is the Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University (Princeton, US). He is leading The Computer Science of Human Decisions project.

Born in the UK and having grown up in Australia, Griffiths earned a BA (Hons) from the University of Western Australia in 1998, receiving the J.A. Wood Prize for the best student in the Faculties of Arts, Law, and Economics. He came to United States for graduate school, receiving masters degrees in both psychology and statistics in 2002 and a PhD in psychology from Stanford University in 2005. He held faculty positions in the Department of Cognitive and Linguistic Sciences at Brown University (Providence, US) and the Department of Psychology and Cognitive Science Program at the University of California, Berkeley before moving to Princeton in 2018.

Griffiths works on interdisciplinary questions at the intersection of psychology and computer science. His research explores connections between human and machine learning, using ideas from statistics and artificial intelligence to understand how people solve the challenging computational problems they encounter in everyday life. He has received awards for his research from organizations including the American Psychological Association, the National Academy of Sciences, and the Guggenheim Foundation, and is co-author of the book Algorithms to Live By, introducing ideas from computer science and cognitive science to a general audience.

Tom Griffiths | Awards Film

Tom Griffiths | Insights Film

Tom Griffiths's News

The insights of NOMIS researcher Tom Griffiths and colleagues have been featured in an article in The Guardian that explores the changing approach to knowledge acquisition. Griffiths is leading The […]

NOMIS researcher Tom Griffiths and colleagues have shown how they can leverage machine learning to evaluate classical decision theories, increase their predictive power, and generate new theories of decision-making. Their […]

Tom Griffiths's Insights


Effectively updating one’s beliefs requires sufficient empirical evidence (i.e., data) and the computational capacity to process it. Yet both data and computational resources are limited for human minds. Here, we […]

Abstract: Large-scale social networks are thought to contribute to polarization by amplifying people’s biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and evaluate mitigation strategies. Here we show under controlled laboratory conditions that transmission through social networks amplifies motivational biases on a simple artificial
Abstract: Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more information than sparse annotations (like hard labels), they are also more expensive to collect. For example, while hard labels only provide information about the closest class an object