Theories of Learning in Games and Heterogeneity Bias
指出在重复博弈的学习模型比较中，忽略参数异质性而使用混合估计会产生严重偏差，导致强化学习被过度偏好，而随机参数估计可大幅减少此偏差。
Comparisons of learning models in repeated games have been a central preoccupation of experimental and behavioral economics over the last decade. Much of this work begins with pooled estimation of the model(s) under scrutiny. I show that in the presence of parameter heterogeneity, pooled estimation can produce a severe bias that tends to unduly favor reinforcement learning relative to belief learning. This occurs when comparisons are based on goodness of fit and when comparisons are based on the relative importance of the two kinds of learning in hybrid structural models. Even misspecified random parameter estimators can greatly reduce the bias relative to pooled estimation. Copyright The Econometric Society 2006.