Modelling challenge

Our methods and tools for modelling, optimisation, and control depend heavily on exploiting the structure of the problem. Understanding the relationship and constraints underlying the problem structure enables predicting system behaviour as well as potentially controlling behaviour. Decomposing problem structure, associating first principles with the elements resulting from this decomposition, then recomposing these principles into an overall qualitative or quantitative (mathematical or computational) model are typical steps of systems modelling. This does not always work as we might expect, because we fail in our understanding of the problem, particularly when the problem is complex. Understanding why and developing real insights into complex problems involves what Sage and Rouse (1999) describe as the modelling challenge. Meeting the modelling challenge is complicated by the fact that not all critical phenomena can be fully understood, or even anticipated, based on analysis of the decomposed elements of the overall system. Complexity not only arises from there being many elements of the system, but also from the possibility of collective behaviours that even the participants in the system could not have been anticipated.