This paper studies decision-making 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. We provide a general framework and characterize the asymptotic properties. We apply it to provide a candidate explanation for the sensitivity of consumption to transitory income.