 DB  is based on a well-known result in probability logic, which is usually stated in terms of “uncertainties” (see Adams 1998 for more details; for a helpful overview, see Hájek 2001). Define the uncertainty of a proposition \(A\), \(u(A)\) as \(u(A) = 1-cr(A)\). Put in this way,  DB  says that the uncertainty of the conclusion must not exceed the sum of the uncertainties of the premises.  DB  can be seen to share a number of important features with standard probability theory. Plug in \(0\) for \(n\) and you get that one should assign \(1\) to any logical truth. Plug in \(1\) and you get that one’s degree of belief in the premise of a valid single-premise argument should not exceed your degree of belief in the conclusion. The idea underlying  DB  is that uncertainties can add up and therefore need to be accounted for when we are trying to determine how the logical relations between our belief contents should affect our degrees of belief in those contents. Even if my uncertainty about each of a large number of premises is next to negligible when taken individually, the uncertainty may accumulate so as to make the conclusion highly (perhaps even maximally) uncertain. It is for this reason that  DB  gets us around the Preface Paradox; in the Preface case the number of premises is sufficiently high for the conclusion to admit of a very low credence.