Predicting and Understanding Initial Play
利用机器学习分析矩阵博弈的初始行为规律，通过训练预测算法并结合经济模型，提出混合模型提升预测准确性，对实验经济学和博弈论研究有参考价值。
We use machine learning to uncover regularities in the initial play of matrix games. We first train a prediction algorithm on data from past experiments. Examining the games where our algorithm predicts correctly, but existing economic models don’t, leads us to add a parameter to the best performing model that improves predictive accuracy. We then observe play in a collection of new “ algorithmically generated” games, and learn that we can obtain even better predictions with a hybrid model that uses a decision tree to decide game-by-game which of two economic models to use for prediction.