  Multi-objective evaluation is a necessary aspect when managing complex
systems, as the intrinsic complexity of a system is generally closely linked to
the potential number of optimization objectives. However, an evaluation makes
no sense without its robustness being given (in the sense of its reliability).
Statistical robustness computation methods are highly dependent of underlying
statistical models. We propose a formulation of a model-independent framework
in the case of integrated aggregated indicators (multi-attribute evaluation),
that allows to define a relative measure of robustness taking into account data
structure and indicator values. We implement and apply it to a synthetic case
of urban systems based on Paris districts geography, and to real data for
evaluation of income segregation for Greater Paris metropolitan area. First
numerical results show the potentialities of this new method. Furthermore, its
relative independence to system type and model may position it as an
alternative to classical statistical robustness methods.
