8. Systems Science
Source: ebook ch. 12 (“Systems Science”) and Conclusion.
The ebook closes by zooming out from the model of an individual system to how systems theory relates to science and knowledge more broadly — this chapter, and the whole documentation set, close the same way.
Systems theory as a formal language
Systems theory is a formal language — like mathematics, it is independent of external reference to any particular subject matter, and depends solely on its own internal logical consistency. If that internal logic is consistent, the language “works”; if there are logical inconsistencies in its syntax, it doesn’t — the same is true of any formal language, such as the algorithms that run a computer (a logic error crashes the system).
Science, in its broadest definition, may include such formal languages, but is essentially an empirical endeavor — dependent on some subject matter for its validation. Most people who call themselves scientists spend their time amassing or analyzing empirical data. Formal languages are independent of that; but science works best when it is supported by a formal, mathematical language — mathematical proof is considered the gold standard of scientific validation.
Since the turn of the 20th century, set theory has been the de facto foundation of
mainstream mathematics. Set theory and the reductionist paradigm (see
01-worldview-and-paradigm.md and
02-system-fundamentals.md) are well suited to certain kinds of
systems — modern science supported by this mathematics does a very good job describing the
simple, deterministic systems of the natural sciences (chemistry, much of physics). But other
areas — most notably the social sciences, which must deal with non-deterministic, highly
interconnected, emergent systems — either try to mimic the natural sciences’ methods (as
mainstream economics often does) or are left with very little in the way of a robust formal
foundation to build on.
The fractured nature of reductionist science
Modern science, developed under the reductionist paradigm, has also become highly specialized and compartmentalized. Specialization itself isn’t a problem — but when the knowledge and expertise of one domain become very disconnected from another, science as a body of knowledge risks becoming focused on the trees without seeing the forest. Science serves a function within society, and society needs answers not only to narrow analytical questions but also to bigger ones — about the nature of order and chaos in the universe, or how different domains of knowledge actually relate to each other. The reductionist paradigm, on its own, offers limited means for approaching these bigger questions; if science can’t provide plausible answers, people look elsewhere, and science fails to provide an integrated picture of how the world works.
Systems science as the interdisciplinary answer
This is where systems science comes in — the holistic approach that lets us focus less on
specialized knowledge within specific domains and more on how those domains fit together, often
via the idea of integrative levels (see
05-hierarchy-and-abstraction.md). Systems science is
therefore much more interdisciplinary, and becomes particularly relevant wherever a
phenomenon crosses traditional domain boundaries.
Two examples the ebook highlights:
- System ecology: doesn’t confine itself to biological systems alone, but recognizes the important interplay between human industrial activity, the biosphere, and the abiotic geosphere — one of the most successful areas within the system sciences.
- Sociotechnical systems: the study of the interaction between people and technology. Where modern science has tended to support a technocratic view of the world, systems science crosses the boundary between the technical and the human, recognizing the importance of their interaction.
This points to perhaps systems science’s greatest contribution: for centuries, science has tried to apply the success of modern physics to studying social systems, with limited results. Traditional science rests on an objective view of the world — removing the subjective interpretation of the observer from the model — which works fine for inanimate objects, but there is an inescapable subjective dimension to almost everything humans do. Systems science is philosophically sophisticated enough to engage the difficult questions surrounding this subjective nature of the human condition — questions that domains like psychology, cultural studies, and sociology genuinely require.
Two sides of the same coin
Systems science and traditional (reductionist) science are often cast as opposites, but they are better understood as complementary. A scientific framework powerful enough to describe the world in its full richness will require both:
- the qualitative capacities of systems science, which let us properly contextualize things, and
- the rigorous quantitative methods of analysis, which let us properly compute that information —
with the combination hopefully producing a fuller picture of how the world actually works.
Summary of the whole documentation set
Systems Thinking is, at root, a shift of perspective: from focusing on parts and their properties, to seeing the connections between things and the whole system those parts form. The central move is learning to properly contextualize a part by knowing its function within the whole, how it relates to other elements, and — most importantly — how all of these interactions give rise to the system as such (chs. 1–2). From there, the model is built up: functions, their efficiency, and the distinction between constructive/destructive processes and resources/entropy (chs. 2, 7); the relations between elements, and how synergistic interaction gives rise to emergence (ch. 4); a system’s boundary, which defines its autonomy from its environment (ch. 3); the hierarchical structure that is a ubiquitous feature of every kind of system (ch. 5); and finally, system dynamics — feedback loops and regulation — which describe how systems change over time (ch. 6).