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The economist and statistician George P.E. Box, with reference to statistical probability models, notably opined that "All models are wrong - but some models are useful" (Box, 1979). This largely misquoted but nonetheless famous statement, outlined Box's view that decision makers sometimes have to make decisions with incomplete information, which may be only explained by the best available models. As such, any mental or conceptual model which attempts to encapsulate all possible facets of a given system or situation, may be limited in its expression due to real- world complexity or due to subjective over-simplification of the same reality, at the time that the model was formulated. Box's argument is that if the boundaries of a system can be defined as far as possible, then the resulting systems model, can also be restricted to be within realistic and quantifiable bounds. Achieving accuracy within a representation of the real world in this way, becomes increasingly difficult, as the level of complexity of the model increases to accommodate an ever finer granularity of representation. Sterman suggests that any model of the real world should yield a fundamental kernel of knowledge at its core, about the context within which it exists. But at the same time, this model should allow us to understand its imperfections and the limitations of the world it is trying to represent, through the inclusion of human, social, systemic and process flows and other impinging factors (Sterman, 2002). Indeed the inclusion and use of human expert knowledge is the fulcrum upon which such representations pivot (Ford and Sterman, 1998). A natural consequence of this approach, means that if better, detailed or more complex representations of reality are defined, the accuracy of our predictions and quality of decisions should also increase. Any and every technique which can help BPM and MIS researchers, as well as industry practitioners, to uncover and relate tangible and intangible factors, should therefore be pursued. At the junction of the disciplines of management, social, behavioural, political, and more recently, information sciences, the field of Systems Dynamics aims to provide the researcher with such a tool to resolve these questions. Systems Dynamics (or Systems Thinking as it is otherwise known) has existed in the academic as well as industrial R&D community for over 25 years at least (Ackoff, 1974; Bertalanffy, 1976), and has grown to become a standard modelling technique in the armoury of the inquisitive in

Can systems dynamics be effective in modelling dynamic business systems?