Deep Learning for Predicting Human Strategic Behavior
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.
The paper deals with modeling human behavior in game theoretic laboratory settings. Contrary to other related works, this work aims to learn human behavioral models without the assistance of domain experts and (more importantly), without the strong game theoretic assumption of rationality. The paper is of moderate to high relevance as it proposes an improvement to the state of the art where current solutions involve carefully handcrafted representations and solutions. As opposed to this work, where the representation is learned through repeated interactions. The setting and problem is very relevant, as it addresses a problem that will face every AI agent in the real world. 
The technical aspects of the work are sound and explained with a decent amount of detail (much appreciated).   The experimental findings lack solid conclusions and need further analysis. Specifically, the experimental setup does not detail what kind of normal form games will be used, the properties of general sum games vary a lot and the implications are profoundly different across classes of games. The results presented do not show much as they are now. With such vague description of the setup 1) results are not reproducible and 2) cannot assess if the proposal generalizes well across classes of games (competitive, cooperative, mixed interest, social dilemmas, coordination/anti-coordination, etc). Furthermore, the analysis of the results is somewhat disappointing. It is very intriguing that the best performing model includes only one action response layer. I wonder if these findings suggest that subjects actually use 1 level models? It would have been informative to comment on how these findings relate to those in behavioral game theory and extend on related results on this field.  In conclusion, the work is interesting and I have no question that it advances the state of the art. But the experimental setup, analysis and conclusions could be improved.   Typos: - 107. "To apply a such a" - 123. "to represent the all the" - 314. "did not need them to achieve set a new "  Detailed questions: - In a footnote you say you ignore salient effects, it would be good if you give some examples of the type of effects. - Section 3. You never explained r is for row player and c for column player. - 226. You say \lambda is a scalar sharpness parameter, why do you need this?