Dynamic choice under ambiguity
研究模糊性敏感决策者的动态选择，通过将决策树偏好而非行为作为原始变量，在动态不一致下仍能获得无歧义的行为预测，并利用Sophistication假设刻画一致计划，为任意决策模型和更新规则提供福利比较。
This paper analyzes dynamic choice for ambiguity-sensitive decision makers. It demonstrates that unambiguous behavioral predictions can be obtained, even in the face of dynamic inconsistency, by taking the individual's preferences over decision trees, rather than acts, as primitive. In particular, it leverages preferences over trees to formalize Sophistication as an assumption about the way individuals resolve conflicts between preferences at different decision points. The main result employs Sophistication to provide a behavioral characterization of Consistent Planning (Strotz, 1956). The analysis accommodates arbitrary decision models and updating rules, and delivers unambiguous welfare comparisons. As an example, a characterization of prior-by-prior Bayesian updating and Consistent Planning for arbitrary maxmin-expected utility preferences is presented. The resulting sophisticated MEU preferences are then used to analyze the value of information under ambiguity; a basic trade-off between information acquisition and commitment is highlighted.