Models of systems are always an extraction and extrapolation of something larger and more complex. But even these models, which are often too big and still too complex, need to be reduced to smaller subsystems. Courtois puts forward certain criteria for successful decomposition.

One approach to decomposition and simplification is to hold certain parameters constant. Another is to neglect certain parameters altogether. In structural engineering the effect of scaling down is well known; many remedies or compensators have been put forward over the years. Courtois examines poor scale separability, rare events and weak interaction, and other influences. The paper draws heavily on applications where their problems have been studies for some time. Courtois gives examples from physics that are particularly relevant.

Certain techniques of analysis are put forward. In my view, the probabilistic models should have the greatest chance of success in the future. Courtois has already published material on this subject (see, for example, [1]) and most readers searching in this area would probably have come across this work.