Costly learning under ambiguity
提出成本贝叶斯学习模型，用菜单选择框架公理化刻画，能解释不确定性厌恶和偏好晚揭晓不确定性的行为，对研究信息获取的学者有用。
In this paper, we propose a general model of information acquisition, Costly Bayesian Learning (CBL). Using menu choice framework, we provide an axiomatic characterization of the model, identify its parameters, apply a comparative statics, as well as study special cases of the model. Unlike many prominent models of information acquisition (e.g., the Rational Inattention model), a CBL agent can exhibit uncertainty averse behavior and prefer late resolution of uncertainty.