ANTICIPATED UTILITY AND RATIONAL EXPECTATIONS AS APPROXIMATIONS OF BAYESIAN DECISION MAKING*
研究未知转移概率的马尔可夫决策问题，比较精确贝叶斯决策规则与两种近似方法：理性预期近似和预期效用近似。在消费平滑例子中，预期效用近似优于理性预期近似，后者会错误描述风险的市场价格。
We study a Markov decision problem with unknown transition probabilities. We compute the exact Bayesian decision rule and compare it with two approximations. The first is an infinite‐history, rational‐expectations approximation that assumes that the decision maker knows the transition probabilities. The second is a version of Kreps' anticipated‐utility model in which decision makers update using Bayes' law but optimize in a way that is myopic with respect to their updating of probabilities. For several consumption‐smoothing examples, the anticipated‐utility approximation outperforms the rational expectations approximation. The rational expectations approximation misrepresents the market price of risk.