  We propose that the flexibility offered by modern event-generator tuning
tools allows for more than just obtaining "best fits" to a collection of data.
In particular, we argue that the universality of the underlying physics model
can be tested by performing several, mutually independent, optimizations of the
generator parameters in different physical regions. For regions in which these
optimizations return similar and self-consistent parameter values, the model
can be considered universal. Deviations from this behavior can be associated
with a breakdown of the modeling, with the nature of the deviations giving
clues as to the nature of the breakdown. We apply this procedure to study the
energy scaling of a class of minimum-bias models based on multiple parton
interactions (MPI) and pT-ordered showers, implemented in the Pythia 6.4
generator. We find that a parameter controlling the strength of color
reconnections in the final state is the most important source of
non-universality in this model.
