Learning Under Ambiguity
探讨当风险与模糊性有区别时的学习问题，通过思想实验指出贝叶斯学习模型的极端性，并提出一个推广模型，允许决策者的信心随学习而变化。投资组合应用表明，模糊性下的信心变化会促进股市参与和投资，而贝叶斯下的估计风险变化则不然。
This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme—it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers' confidence about the environment to change—along with beliefs—as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity vs. changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not. Copyright 2007, Wiley-Blackwell.