In this paper, I turn toward the types of cases regarding which I-CDT and K-CDT come apart. In particular, I present two novel examples, which, I argue, display I-CDT to be superior to K-CDT. The first, the “Chancy Dog Problem,” follows the form of a scenario that is explored in Rabinowitz [2009] and shows I-CDT to give what I argue to be the correct recommendation in a chancy universe, where K-CDT gives the incorrect one. I explain this difference as stemming from the fact that conditionalization shifts credences too much, adjusting beliefs as though the oracle provides more information that it actually does. Specifically, conditionalization leads one to revise one’s beliefs as though the oracle’s information tells you not only what will h...