 which is equivalent to the invariance condition that \(pr_1(A\mid E_i) = pr_2(A\mid E_i)\), for each element \(E_i\) of the partition. To maintain that updating goes by Jeffrey Conditionalization is to mandate invariance for each proposition \(A\). This might seem to be required, as Armendt shows that a Dutch Strategy can be constructed against an agent who has a rule for updating that conflicts with Jeffrey’s Rule (Armendt 1980). As with the Rule of Conditionalization though, the Dutch Strategy does not show that there is any problem with having probabilities over a partition, and then abandoning one’s prior conditional probabilities over that partition, that is violating invariance; the problem is with adopting a rule of this sort in advance. As with the simple Rule of Conditionalization, Skyrms (1987b) shows that there is also a converse Dutch Strategy argument showing that following Jeffrey’s Rule avoids a Dutch Book.  It is built in here that Jeffrey’s Rule applies in cases in which credences shift over a partition, but as before if the agent’s learning scenario does not form a partition, Gallow (2019) shows that, as with the Simple Conditionalization, Jeffrey Conditionalizers may be subject to a Dutch Strategy as well. 