The Importance of Integration

Complexity poses a greater and greater challenge for the system designer/problem solver. The classic approach to problem analysis and solution calls for the problem and its solutions to be broken apart into their constituent parts until those parts are simple enough to be clearly understood. From there, the parts—whether of the problem or solution—are aggregated into a whole where the function of that whole is understood to be equal to the sum of the functions of its parts. This kind of “analysis”—a word with its roots in the Greek, meaning to break apart—is best suited to the solution of complicated rather than complex problems.

Although our parlance tends to equate the ideas of complicated and complex, they are actually quite different. In the context of systems, complicated refers to systems where the interactions of the parts are deterministic and, therefore, directly predictable. Complex systems are those where the system behaviors emerge from the interactions of the parts. This emergence of behaviors is much more difficult to predict than the behaviors resulting from the simple aggregation of a set of parts.

The complex v. complicated difference is even found subtly in the difference between the respective etymologies of the words. The word “complex” has its roots in the Greek for “woven or plaited together” while complicated stems from words meaning “folded.” The relative inextricability of fibers from woven materials contrasted with the more loosely composed folded materials is a helpful reminder of the essential difference between the two terms. Think of a folded linen tablecloth. Simply unfolding it is much less destructive than undoing the weave. When a complex system is disassembled into its constituent parts, the result is the destruction of the relationships among them and the consequent eradication of the nature of the system behavior. Just as a pile of linen fibers is no longer a tablecloth, the severed parts of a complex system have lost the essential nature of the system itself.

In order to understand a complex system, therefore, it is necessary to take a “systems view” of the whole. Rather than approaching the system only through analysis, we must engage with it synthetically, taking its parts as a whole without deconstructing them. This entails setting the boundary of our view broadly enough to encompass all of the relevant interactions—inside and outside the problem/solution in its context, and making the elements of the system visible within that boundary. In the words of the INCOSE Complexity Primer, “. . . emergence is a collective phenomenon that requires aggregation—emergence will not be observed until the system is considered as a whole.” (A Complexity Primer for Systems Engineers, INCOSE 2016, p. 14, hereinafter Primer.)

(Note: In 2016, INCOSE’s Complex Systems Working Group published an excellent primer on the subject of complexity in systems engineering. It is available for free from the INCOSE online store.)

Taking such a view can be a tricky proposition. Dr. Yaneer Bar-Yam, President of the New England Complex Systems Institute, points out, “It is because we cannot describe the whole without describing each part, and because each part must be described in relation to other parts, that complex systems are difficult to understand.” (Dynamics of Complex Systems. New York: Perseus Books. 1997.) In other words, any description of a complex system is inadequate for systems engineering purposes unless it describes all of the system’s parts which, in turn, can only be described in the context of the whole system. The construction of such a view is a formidable task without the aid of a model.

However, in order for a model to be helpful in reaching the real understanding of a complex system or systems, it must enable the view described above. This need stands in tension with the very process of creating a model. Models are by their very essence a simplified representation of reality. In the case of a complex system, the model seeks to represent the system (and often its environment) with the full knowledge that, “. . . complexity often cannot be simplified away without losing the essence of the problem or possible solutions.” Primer, p.8. As Einstein put it, the model must be “as simple as possible but no simpler” where “as simple as possible” is taken to mean “without destroying the necessary systems view.”

The model, therefore, must depict all the elements and relationships that give rise to the emergent behavior that is causing or will solve the problems being addressed. Not only does it need to capture and expose the relationships among the physical and behavioral elements of the system, but it also needs to include the relationship of the system to its context—not only the environment within which it will operate but also the problem it is addressing (e.g., requirements) and more. In other words, it must be fully integrated. Only by including and relating all of the system context can the model create a picture of the complex system with which to make meaningful predictions about how the system will act and whether or not it can solve the problem being addressed.

Clearly, views of only a single class of elements (e.g., requirements) cannot offer the necessary systems view of a complex system. Likewise, a disconnected set of drawings each depicting a subset of the system elements will be similarly unable to provide the needed perspective. Only a fully integrated model has any reasonable hope of creating the system picture from which to make predictions concerning the behavior of the system and its interactions with its context. Without that picture, the creation of an adequate solution to a complex problem is at risk at best.

The systems engineer facing the challenge of complexity must opt for tools and methods that will create the fully integrated systems view which will allow her to understand and manage that complexity. She must avoid falling into the trap of deconstruction leading to oversimplification. Only then can she make reasonable predictions about the choice of solutions that will lead to success in a complex world.

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