System Dynamics: What, When, and How


System dynamics is a technique for business and policy simulation modeling based on feedback systems theory. It was invented in the late 1950s by Jay Forrester, a pioneer in engineering and computer design. Since then, SD has developed as its own field, distinct from the larger fields of operations research and management science to which it is related.

SD unites social and behavioral science with the nitty-gritty details of planning and accounting, and requires the careful design and construction of original models with many interacting variables.  Although SD modeling is technically demanding, the logic and results of a good SD model are neither esoteric nor hard for decision makers to understand.  And although SD models are sophisticated, they are also compact enough to run instantly on a laptop computer, permitting a whole series of alternative assumptions and scenarios to be tested quickly and thoroughly in interactive strategy development sessions.    


SD is used by organizations facing high-stakes decisions and seeking an integrated view of the major forces that can affect key outcomes years or decades into the future. It helps these organizations to better weigh the pros and cons of various options they have been considering or might consider. An integrated, strategic view is needed when the various options have multiple consequences in the short term and long term, including the reactions of all affected parties, and the reactions to those reactions.  In such cases, conventional planning approaches (e.g., using spreadsheets) fall short of what is needed to properly consider how things are likely to play out over time.  

SD modeling may show that an option that looks good at first glance may in fact be likely to hit roadblocks or resistance that negate its impact or even make things worse. It may also show that an option that initially looks weak or too costly is likely to become a winner as its good points build upon themselves and its limitations diminish. By looking at not only primary effects but also secondary and feedback effects, SD models can improve our ability to make smart choices that will stand the test of time.      


SD models are custom built to be of greatest value for the particular question at hand.  The modeler works closely with the client to determine what the key action and outcome variables are and at what level of detail they need to be represented. A useful model is one that considers all of the important variables but leaves out the extraneous ones.  It allows testing of all the current and possible decision options, even those that may be unusual but worth considering.  And it is built in close consultation with the client in steps, with the results of one step indicating what the next step should be.     

Although no model can look into the future with complete accuracy, some models are more reliable and trustworthy than others.  Reliable SD modeling requires science, craft, and diligence, and even small slips can compromise the results.  It is crucial to seek historical time series data for as many of the model's outcome variables as possible, and proper numerical estimates for as many of the model's input assumptions as possible.  A model must be encircled and saturated by such evidence to be considered trustworthy.

But most assumptions will have some level of uncertainty, which is why thorough sensitivity testing is important.  The purpose of such testing is to determine whether changes in assumptions, within their ranges of uncertainty, can affect conclusions regarding the relative impacts of strategic decisions.  The nature of dynamic systems is such that the uncertainty in most assumptions will not matter.  In the cases where it does matter, one may (a) look for additional existing data to reduce the uncertainty, (b) do more detailed modeling of the parameter in question, or (c) indicate that more research needs to be done on the parameter for a more definitive answer.