Abstract
We consider the problem of predicting human players’ actions in repeated strategic interactions. Our
goal is to predict the dynamic step-by-step behavior
of individual players in previously unseen games.
We study the ability of neural networks to perform such predictions and the information that they
require. We show on a dataset of normal-form
games from experiments with human participants
that standard neural networks are able to learn functions that provide more accurate predictions of the
players’ actions than established models from behavioral economics. The networks outperform the
other models in terms of prediction accuracy and
cross-entropy, and yield higher economic value.
We show that if the available input is only of a short
sequence of play, economic information about the
game is important for predicting behavior of human agents. However, interestingly, we find that
when the networks are trained with long enough sequences of history of play, action-based networks
do well and additional economic details about the
game do not improve their performance, indicating
that the sequence of actions encode sufficient information for the success in the prediction task