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.

使用神经网络预测人类玩家在重复战略互动中的行为，证明神经网络可以通过历史记录的序列预测未来行为，同时比行为经济学模型有更准确的预测和更高的经济价值。