Systems Science 1 — Overview
I have written several blog posts that mention or talk about systems science and systems thinking. And I have created a new category called 'System Science' to aggregate the many various postings that contain references to SS. After several e-mail requests to write more explicitly about systems science I've decided to work up a casual review of the field and provide some basic explanation of why this science is so fundamental to every other form of knowledge. This material is drawn from work I and a colleague here at the University of Washington Tacoma are doing in developing a major (both a BA and a BS degree) in Systems Science. I will be sharing with you the justification for why this kind of degree is so needed in today's world. Most longer-term readers, however, will probably be able to anticipate that justification without much difficulty.
In this first installment I want to briefly outline the various conceptual frameworks that collectively constitute the science of systems (Fig. 1). Some of these frameworks are also traditional subjects within the sciences, such as thermodynamics in physics. Others are perhaps best characterized as perspectives or methodological approaches, such as complexity (also from physics) or conceptualization (from neuropsychology). Complexity theory shows up in brain science and has overlap with thermodynamics. Indeed all of these frameworks overlap, which is why they are rendered as a kind of Venn diagram.
Systems science is unique in that it is really "about" all other sciences! It is not composed of reductionist sub-topics as you typically find in the ordinary sciences. Rather, it is comprised of these frameworks and can be shown to apply to all other subjects of study in all other sciences. It even applies to social sciences, humanities, and especially philosophy. For example, epistemology, the study of knowledge, works by attempting to organize the nature of knowledge and ways of knowing into a systemic structure. Systems science is integrative and holistic, even while providing detailed explanations for why real-life systems are the way they are. The fact that all systems are shown to be comprised of subsystems at a smaller scale (and conversely, all systems are components in some larger system — at least until you reach the boundaries of the universe!) provides compatibility with normal reductive scientific thinking. But the fact that it works both in the downward and upward directions means that system science is also concerned with context and meaning, pragmatics and semantics.
Figure 1. Conceptual frameworks in systems science. Note that all of systems science is embedded in quantitative methods that provide the scientific rigor needed. Even so, the subjects themselves can be reasonably well understood from a qualitative perspective.
The core framework of systems science is the principle of organization and conceptualization. By this I mean that there exists a real property of system organization — what I like to call "systemness" — but that that organization interacts with our brains as observers such that the mind can perceive that systemness in all its various forms. In other words, there is the world out there, organized as a system of systems (of systems ad infinitum) and the mental world (in the head), which is the result of a biological subsystem — the brain — representing those exterior systems. Systems recognize and represent other systems!
The framework that bootstraps the system-recognizing-system process is that of boundaries and interactions, actually inputs and outputs as well as boundary to boundary interplay. We recognize a system by virtue of its having a definable, even if very fuzzy, boundary. Without discernable boundaries there would be no nouns! There would be no objects to classify. Boundaries make it possible to isolate and focus on specifics. They also make it possible to recognize influxes and outfluxes of material, energy, and, as we will see later, messages. They make it possible to distinguish one system in contact with another. And since boundaries can actually be, subsystems in their own right, they can have 'texture', 'character', form and many other qualities that allow us to recognize both classes of systems and specific instances of a class.
Systems in the real world, even conceptual systems like philosophies, change over time, both in terms of their internal organization and their interactions with other systems — their "environment". That is, systems are dynamic; even systems that appear to us to be static in our perception of time change over a long enough time frame. Even with boundaries, all real systems are open to flows as mentioned above. Hence they can incorporate new material or energy, or reorganize as a result of information.
Relationships between systems can change, both in terms of the nature of a relationship and in terms of the real-time realization of a kind of relationship. People can start out being in love with each other and end up, some time later, hating (the opposite development would be more preferred!). Carbon atoms form very definite covalent bonds with other kinds of atoms, but those bonds can be made and broken depending on the energy fluxes around the compounds.
A critical framework for understanding dynamic organization and relationships is provided by cybernetics and information theory. Cybernetics is the science of control, of which the hierarchical control architecture with adaptivity is the highest form, the quintessential representative being the human brain. Information theory helps us understand the nature of messages between systems (and subsystems as components of a larger system) and their organizing effects. Dynamic system organization is the realization of knowledge (held by that system) and is the integration of information received by the system.
Thermodynamics, that is the larger framework of energy flows and work, on the one hand, and the emergence of properties from complexes of subsystems on the other, form bookends, so to speak, on the evolution of ever more complex and higher order systems. The origin and evolution of life on this planet is the paradigm framework for understanding so much about the way the world works. Life on earth seems to go against the grain with respect to the Second Law of Thermodynamics. But we now have a reasonably good explanation of why complex but unorganized systems, under the influence of energy flows, form ever more structured systems. That is, they do until the energy flows are turned off! This latter issue faces our societies today as we are using up the easily available fossil fuels.
That latter point is why systems science is essential to our understanding now, more than ever. We are faced with extraordinary problems in the history of mankind, problems that threaten our very existence if we don't soon come to terms with comprehending how this universe works. I will say more, in a future posting, regarding the motivation for why literally everyone should study systems science, at least qualitatively if not quantitatively.
Which brings me to the last aspect of systems science and these frameworks. The diagram situates systems science within a field of mathematics, computation and modeling. These are the formal, quantitative methods that are ultimately needed to work effectively with systems and to make their study truly scientific. Even so, not everyone will be interested in trying to understand systems through quantitative means. Fortunately there are qualitative ways to explain and understand much of systems science so that even math-challenged folk can grasp the meanings within these frameworks. You don't have to be an auto mechanic in order to drive a car. Neither do you need to be a math whiz to grasp the concepts in systems science. You will need math to do any deep study or design systems using the principles. But for the regular person to come away with an appreciation for systemness and an ability to think systemically all that is needed is a comprehensive way to view and integrate these frameworks.
I hope I might provide some of that in this series of posts.
In the next post in this series, I will present some reasons why I think systems science is actually the 21st century version of liberal education. That is, I will claim, and provide a few examples from real life, that someone with systems science in their hip pocket (or rather their cerebral cortex) has the most universal set of principles by which to understand any specialized field of endeavor. Indeed, someone who has mastered systems science (at least qualitatively) possesses the most transferable kind of knowledge imaginable. A very handy thing to have if one contemplates changing careers often in a fast-paced, complex world.