Whenever a new text in a young field appears, it is a cause for celebration, for it signals a growth and dynamism rewarding the work of the field's pioneers and current practitioners. When the new text grows from work geographically remote from the original works in the field, even today in this age of instantaneous global communication it suggests a healthy spread of the tools, techniques, and perspectives of the field.

Here, in this work by Kambiz Maani and Bob Cavana, we see the ideas that first shimmered at MIT in Cambridge, Massachusetts, around 1960 now spread halfway around the world, about as geographically distant as one can get from Cambridge. The spread does involve some translation and alteration -- in style, examples, and applications with a decided New Zealand flavor. That development is all to the benefit of the system dynamics community and the wider private sector, government, and academic communities we seek to serve and support.

This text is the most recent in a line of works tracing directly back to Jay W. Forrester's classic Industrial Dynamics (1961). That book is still remarkable for the completeness of the vision and approach it laid out. In the forty years since its publication, the field has grown too big for its original industrial name and purview -- not industrial dynamics, or R&D dynamics, or urban dynamics, or even business dynamics, but "any system" dynamics. It is worth noting here that the word "system" in the name causes some confusion in identifying the methodological and philosophical ancestry of the field. The name "system dynamics" comes not from systems analysis or general systems theory -- as the misnomer "systems dynamics" might suggest. Rather, the four grandparents of the field, as described by Forrester enduringly in 1958, are computer technology, computer simulation, strategic decision making, and feedback thinking. It was a fortuitous mix, for the engineer's notion of feedback connects seamlessly with the circular causal complexity that strategic thinkers encounter. And the interconnected, interdependent worlds that strategy and feedback complexity imply confound mental simulations. Today, every system dynamics application, even the qualitative, reflects this fourfold ancestry: practitioners strive to use computer technology to trace reliably through simulated time the dynamic implications of strategic decisions in complex feedback systems.

Here in these pages the reader will find a very modern overview of the main elements of the system dynamics approach. It is within the last ten years that bridges have been built between system dynamics practitioners, scenario planners, and the soft systems modeling and soft operations research communities. This is one of the first texts to make these bridges available to people new to system dynamics, to enhance the conceptual phases of system dynamics modeling with the incorporation of helpful problem-structuring approaches taken from SSM (soft systems methodology) and soft OR (operations research).

Here also will the reader begin to experience the power of the system dynamicist's continuous perspective on the behavior of complex systems. Although a discrete view, focusing on separate events and decisions, is entirely compatible with the field's characteristic endogenous feedback perspective, the system dynamics approach emphasizes a continuous view. The continuous view strives to look beyond events to see the dynamic patterns underlying them. Moreover, the continuous view focuses not on discrete decisions but on the policy structure underlying decisions. Events and decisions are seen as surface phenomena that ride on an underlying tide of system structure and behavior. It is that underlying tide of policy structure and continuous behavior that is the system dynamicist's focus and the source of strategic policy insights.

There is thus a distancing inherent in the system dynamics approach which the careful reader will observe in these pages -- not so close as to be confused by discrete decisions and myriad operational details, but not so far away as to miss the critical elements of policy structure and behavior. Events are deliberately blurred into dynamic behavior. Decisions are deliberately blurred into perceived policy structures. Insights into the connections between system structure and dynamic behavior, which are the goal of the system dynamics approach, come from this particular distance of perspective.

What are the kinds of insights one can hope to find from this perspective? First and foremost, identifying the stock-and-flow/feedback structure at the heart of a complex dynamic problem can reveal the endogenous "within system" sources causing or exacerbating that problem. Dynamics are, at least partially, a consequence of endogenous system structure. Uncovering those endogenous sources of behavior in any given system is empowering, for it enables actors in the system to focus on effective policies within their own control. Second, the feedback perspective provides powerful understandings of the frequently observed phenomenon that complex systems are "policy resistant." Well-intentioned policy initiatives frequently are found to be either ineffective or even counterproductive. The system dynamics approach reveals that complex systems have myriad adjustment mechanisms, usually embedded in negative (balancing) feedback loops that naturally counteract and compensate for well-intentioned policy initiatives, just like a car's cruise control naturally compensates when the car encounters a hill. Policy resistance becomes a natural and expected phenomenon in complex, interdependent systems, a phenomenon that can be understood and dealt with. A third kind of insight is related: We often find in complex social or environmental problems that what works in the short run usually fails to provide long run benefits and may even make things eventually worse. "Worse before better" behavior can be understood in feedback terms, and those understandings can lead to more robust policies that yield more lasting benefit.

It is these sorts of understandings and insights that one strives for as one approaches a difficult problem using the tools and perspectives presented here. The going will not be easy, partly because the problems we hope to solve are far from easy and for any one of them there are many perspectives to meld, and partly because the arts of building insightful models that really help people think are difficult to acquire and to practice. I commend to you the journey Kambiz Maani and Bob Cavana have begun for you.


George P. Richardson
Professor and Past President, System Dynamics Society
The Rockefeller College of Public Affairs and Policy
University at Albany - State University of New York
October 1999