Anticipated utility and rational expectations as approximations of Bayesian decision making

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.