I study how boundedly rational agents can learn a “good” solution to an infinite horizon
optimal consumption problem under uncertainty and liquidity constraints. Using an
empirically plausible theory of learning I propose a class of adaptive learning algorithms that
agents might use to choose a consumption rule. I show that the algorithm always has a
globally asymptotically stable consumption rule, which is optimal. Additionally, I present
extensions of the model to finite horizon settings, where agents have finite lives and life …