Updateful decision theories change the probability distribution used to evaluate actions over time. Updateless Decision Theory (UDT) does not, instead always maximizing a priori expected utility. This works well in well-defined decision problems with high probability in the prior, but in some senses, does not learn. Open-Minded Updatelessness seeks to combine the advantages of updatelessness and updatefulness by allowing some changes in probabilities, without fully updating on evidence.