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Understanding human decision-making is a key focus of behavioral economics, psychology, and neuroscience with far-reaching applications, from public policy to industry.
Recently, advances in machine learning have resulted in better predictive models of human decisions and even enabled new theories of decision-making. On the other hand, machine learning systems are increasingly being used to make decisions that affect people, including hiring, resource allocation, and paroles.
These lines of work are deeply interconnected: learning what people value is crucial both to predict their own decisions and to make good decisions for them. In this workshop, we will bring together experts from the wide array of disciplines concerned with human and machine decisions to exchange ideas. Read more here.
Keynote Talks
Keynote Talks
Modeling Human Decision-Making: Never Ending Learning
Sarit Kraus (Bar-Ilan)
New Perspectives on Habit Formation from Machine Learning and Neuroeconomics
Colin Camerer (Caltech)
Policing, Pain, and Politics: Diagnosing Human Bias and Error with Machine Learning
Emma Pierson (Cornell)
Evaluating and Improving Economic Models
Drew Fudenberg (MIT)
Choices and Rankings with Irrelevant Alternatives
Johan Ugander (Stanford)
Integrating Explanation and Prediction in Computational Social Science
Duncan J. Watts (UPenn)
Jennifer Trueblood
Vanderbilt
Alex Peysakhovich
Angela Yu
UC San Diego
Ori Plonsky
Technion
Tal Yarkoni
Daniel Björkegren
Brown
Anca Dragan
UC Berkeley
Karen Levy
Cornell
Hima Lakkaraju
Harvard
Ariel Rosenfeld
Bar-Ilan
Maithra Raghu
Irene Chen
MIT
Organizers
Organizers
Annie Liang
Northwestern
Thomas L. Griffiths
Princeton
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