Non-strategic Structural Inference (for Initial Play)

We adapt behavioral models developed for predicting human behavior to the task of value estimation. While the traditional approach in the literature is to model non-strategic agents as uniform randomizers, thus treating their behavior as noise, we argue that a rich non-strategic model is better for value estimation. We introduce quantal-linear4, a rich non-strategic model component, and conduct online experiments that demonstrate that integrating strategic models with this non-strategic model improves both prediction of behavior and inference of values compared to more traditional best response models, with Nash equilibrium being the worst at both inference and prediction. We also propose a framework to augment the standard quantal response equilibrium (QRE) with a non-strategic component we call QRE+L0 and find an improvement in value estimation over a standard quantal response equilibrium.