 Start with a Lewisian approach to self-locating states. Decision theory works with numerical degrees of belief—or “credences”—rather than full beliefs. Following Lewis, we may distribute credences over a space of centered worlds (or properties) rather than uncentered possible worlds. On this approach, when John learns that he is the messy shopper, his credence in the centered proposition associated with “I am the messy shopper” increases dramatically. The Bayesian updating rule called “Conditionalization” handles this transition easily. For instance, we can model John as conditionalizing on that centered proposition, sending it from a middling or low degree of confidence in his mind to certainty.