Special Cases - Subtypes and Exemplars relevant to Systems Engineering

Biological and Living systems (Miller, 1978) are examples of a large and important class of dynamic open systems, which exchange energy and waste with their environment to maintain themselves, at the expense of increasing entropy in their environment.  

Living systems exploit matter and energy as well as information and knowledge elements. Their behaviour manifests itself through flows of material, energy, and information; but also through collective knowledge that is transferred from generation to generation. Living systems have both conceptual and physical aspects, but they are unique in that their emergent behaviours are associated with learning and adaptation. Human systems are especially accomplished among living systems in their ability to express meaning in the form of complex language and use that to drive emergent behaviours of other physical, conceptual, and living systems to their goals.  

Miller G (1978) Living Systems,McGraw-Hill, New York, 1978

Of numerous ways of defining complex systems, this one seems useful and relevant to system engineering:

A complex system is a system in which there are non-trivial relationships between cause and effect: each effect may be due to multiple causes; each cause may contribute to multiple effects; causes and effects may be related as feedback loops, both positive and negative; and cause-effect chains are cyclic and highly entangled rather than linear and separable. 

The non-trivial nature of the relationships in a complex system make the whole system non-deterministic, ambiguous or chaotic (in the mathematical sense that a very small change in initial conditions may produce a very large change in outcome), even if the individual relationships within the system are well understood.

Complexity as defined above is a property of the system of interest. Complexity is also created in the wider system comprising the system of interest and its stakeholders when the system is not fully understood, and when different stakeholders have different partial understandings of the system and of other stakeholders’ concerns. A major goal of Systems Engineering is to reduce this “perceived complexity” by establishing shared and valid models of the system, in order to improve stakeholders’ knowledge and understanding of the system and its context.

An example of cyclic cause and effect is the biological process of mutualistic symbiosis, in which each of a pair of systems uses the other’s waste as raw material for its own processes. The systems import energy from the environment to sustain the symbiotic processes, so the second law of thermodynamics is not violated. The “Circular Economy” takes this concept and applies it to industrial value chains, to turn them into value loops that are closed cycle apart from import of energy. Waste from one process is the feedstock for the next. If the energy comes from the sun, the value loop can be sustainable as long as energy is available from the sun. 

The INCOSE Complexity Primer (https://www.incose.org/docs/default-source/ProductsPublications/a-complexity-primer-for-systems-engineers.pdf) provides a concise introduction to complex systems.

The difference between Complicated and Complex is discussed in, for example, Snowden and Boone (2007), and the INCOSE Complexity Primer (INCOSE, 2015). Complicated systems can be viewed as knowable and deterministic, and once developed their configuration can be “frozen”; whereas complex systems are not fully knowable or deterministic, may be dynamically reconfigurable, and continue to co-evolve with their environment throughout their lifecycle. 

INCOSE (2015) Complexity Primer for Systems Engineers, INCOSE, 2015

Snowden and Boone (2007), A Leader’s Framework for Decision Making, Harvard Business Review, Nov 2007

Finally, systems covered by Rosen’s (1985, 2012) concept of “anticipatory systems” are ubiquitous in the natural world, and increasingly relevant to SE as we move towards intelligent and autonomous systems. An anticipatory system’s present behaviour depends upon anticipated ‘‘future states’’ or ‘‘future inputs’’ generated by an internal predictive model. The following definition is an interpretation, not Rosen’s original.

An anticipatory system is a physical system that has an internal model of itself and its environment and an internal decision-making function, enabling it to anticipate potential changes in the environment and make appropriate adaptations to be ready for the anticipated change.

Rosen R (1985, 2012) Anticipatory Systems: Philosophical, Mathematical, and Methodological Foundations, 2^ndEd., Springer, 2012