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Poster
Deep Learning for Predicting Human Strategic Behavior
Jason Hartford · James R Wright · Kevin Leyton-Brown

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #182
in Tuesday Posters »

Predicting the behavior of human participants in strategic settings is an important problem in many domains. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant's cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across different input and output dimensions by using matrix units rather than scalar units, and show that its performance significantly outperforms that of the previous state of the art, which relies on expert-constructed features.

Keywords: Deep Learning or Neural Networks · Game Theory and Econometrics · (Other) Cognitive Science

Author Information

Jason Hartford (University of British Columbia)
James R Wright (University of British Columbia)
Kevin Leyton-Brown (University of British Columbia)

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