  The quality of consequences in a decision making problem under (severe)
uncertainty must often be compared among different targets (goals, objectives)
simultaneously. In addition, the evaluations of a consequence's performance
under the various targets often differ in their scale of measurement,
classically being either purely ordinal or perfectly cardinal. In this paper,
we transfer recent developments from abstract decision theory with incomplete
preferential and probabilistic information to this multi-target setting and
show how -- by exploiting the (potentially) partial cardinal and partial
probabilistic information -- more informative orders for comparing decisions
can be given than the Pareto order. We discuss some interesting properties of
the proposed orders between decision options and show how they can be
concretely computed by linear optimization. We conclude the paper by
demonstrating our framework in an artificial (but quite real-world) example in
the context of comparing algorithms under different performance measures.
