Deep learning for predicting human strategic behavior
Full Text: 6509-deep-learning-for-predicting-human-strategic-behavior.pdf
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.
Citation
J. Hartford, J. Wright, K. Leyton-Brown. "Deep learning for predicting human strategic behavior". Neural Information Processing Systems (NIPS), (ed: Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett), pp 2424-2432, December 2016.| Keywords: | |
| Category: | In Conference |
| Web Links: | NeurIPS |
BibTeX
@incollection{Hartford+al:NIPS16,
author = {Jason S. Hartford and James R. Wright and Kevin Leyton-Brown},
title = {Deep learning for predicting human strategic behavior},
Editor = {Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle
Guyon, Roman Garnett},
Pages = {2424-2432},
booktitle = {Neural Information Processing Systems (NIPS)},
year = 2016,
}Last Updated: March 03, 2020Submitted by Sabina P
