ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games
Greg d, Hala Murad, Kevin Leyton-Brown, James R. Wright
Abstract
Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A system called GameNet represents the state of the art for predicting human behavior in the setting of unrepeated simultaneous-move games, combining a simple "level-k" model of strategic reasoning with a complex neural network model of non-strategic "level-0" behavior. Although this reliance on well-established ideas from cognitive science ought to make GameNet interpretable, the flexibility of its level-0 model raises the possibility that it is able to emulate strategic reasoning. In this work, we prove that GameNet
BibTeX
@inproceedings{aaai2026_elementarynetano,
title = {ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games},
author = {Greg d and Hala Murad and Kevin Leyton-Brown and James R. Wright},
booktitle = {AAAI 2026},
year = {2026}
}