Understanding complex systems

Abstract

A system or a model is called complex when it contains such a great number of interconnected variables or elements that it is generally no longer possible to understand its global working without using simplified or condensed forms of the model. With respect to the assumed complexity of a system, simplified models often give rise to some inconsistency and there is a need for guides, or procedures, for a whole understanding of complex interdependent systems which do not imply any loss of information. The proposed approach can also be seen as an extension of the so-called interpretive structural modeling techniques to models embedding many feedbacks between their elements. Briefly summarized, it consists of bringing into focus some particular minimum sets of variables such that, if the variables of one of these sets were fixed by some control, the model would contain no feedbacks. Consequently, it is then possible to define a hierarchical order of the variables of an interdependent system, which is analogous to the hierarchical order given by the reduced graph of a recursive system. An operational computer algorithm is proposed which has been successfully tested with several economic models, some of which contain more than 10 000 circuits. © 1981.