In this paper,
we propose model merging as a general solution to these
questions. We formally define model merging based on observational
refinement and show that merging consistent models is a process that
should result in a minimal common refinement. Because minimal common
refinements are not guaranteed to be unique, we argue that the
system modeller should participate in the process of elaborating
such a model. We also discuss the role of the least common refinement
and the greatest lower bound of all minimal common refinements in this
elaboration process. In addition, we provide algorithms for i)
checking consistency between two models; ii) constructing their least
common refinement if one exists; iii) supporting the construction of a
minimal common refinement if there is no least common refinement.