Schematic conflict occurs when evidence is interpreted in different wa
ys (for example, by different people, who have learned to approach the
given evidence with different schemata). Such conflicts are resolved
either by weighting some schemata more heavily than others, or by find
ing common-ground inferences for several schemata, or by a combination
of these two processes. Belief functions, interpreted as representati
ons of evidence strength, provide a natural model for weighting schema
ta, and can be utilized in several distinct ways to compute common-gro
und inferences. In two examples, different computations seem to be req
uired for reasonable common-ground inference. In the first, competing
scientific theories produce distinct, logically independent inferences
based on the same data. In this example, the simple product of the co
mpeting belief functions is a plausible evaluation of common ground. I
n the second example (sensitivity analysis), the conflict is among alt
ernative statistical assumptions. Here, a product of belief functions
will not do, but the upper envelope of normalized likelihood functions
provides a reasonable definition of common ground. Different inferenc
e contexts thus seem to require different methods of conflict resoluti
on. A class of such methods is described, and one characteristic prope
rty of this class is proved.