 Conditionalization was designed to model an idealized process in which agents accumulate information to hone in on non-self-locating hypotheses whose truth-values remain constant (e.g., the atomic number of lead is 82). The model founders when propositions change their truth-values as the accumulation occurs. Conditionalization wasn’t designed to hit moving targets. Neither, for that matter, was Jeffrey’s (1965) “probability kinematics”; Namjoong Kim (2009) shows that Jeffrey’s update rule fails just as badly as Conditionalization when applied to credences in Lewisian centered propositions. (Of course, decision theory involves not only credence but also utility assignments, and the latter may be self-locating just as easily as the former. Decision theories usually assume that utility distributions remain constant, and so typically have not bothered to offer utility updating schemes. But if we introduce utilities in self-locating propositions—I’d get higher utility from addressing that problem tomorrow than today—then such updating schemes may become needed!)