Rules of Thumb versus Dynamic Programming

This paper studies decisionmaking with rules of thumb in the context of dynamic decision problems and compares it to dynamic programming. A rule is a fixed mapping from a subset of states into actions. Rules are compared by averaging over past experiences. This can lead to favoring rules which are only applicable in good states. Correcting this good state bias requires solving the dynamic program. The authors provide a general framework and characterize the asymptotic properties. They apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.