Abstract
The problem of our age is how to go about understanding, deeply, the kinds of complex systems that we need to understand today. Our world is in trouble. Between suffering the unforeseen consequences of technologies that we thought were brilliant (when they were invented) and those of political decisions that seemed right (under the then circumstances), we seem to be witnessing complications in every aspect of societies, the environment, public health, and just about anything you can name.
The problem is complexity. Or, rather, the problem is one of managing complexity, so that we benefit and chaos does not overwhelm us. And in order to manage anything, you need to know it deeply. That is, you need to know how it works (and how it doesn’t work at times) down to a level where interventions (if needed) can be used to keep the system stable.
We suggest that the pathway to deep understanding of any system, no matter how complex, is in front of us. Our guide to the pathway is to apply systems thinking and systems science to how we go about understanding systems. Many will argue that that is exactly what they do. And many people do so to one extent or another. Systems science has been developing for more than half a century and many thinkers have advanced concepts about systemness and many have used those concepts to pursue avenues in the sciences and engineering. There are a plethora of tools and methodologies developed around those concepts. But heretofore there has been no central focus on a set of comprehensive methodologies that could unify the various approaches to using systems science to gain deep understanding. There has not been a systemic systems approach. Many examples of disparate and sometimes contradictory views of systems science, or rather some part of systems science, will be provided throughout the book while still developing approaches to unification.
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Notes
- 1.
This should not be interpreted to mean we have “perfect” knowledge/understanding of these systems. There are probably more details of such systems that physics may yet uncover as they continue to be studied. For example, we might do a better job of connecting the microstates with quantum-level laws.
- 2.
An excellent review of the nature of composition of more complex systems from simpler systems as the deep history of the Universe is by Tyler Volk (2017). His process of combogenesis is the story of how simple systems (like atoms) combine to form more complex composites.
- 3.
Dynamical systems are generally modeled as sets of differential equations integrated over time. Systems dynamics models are based on computer simulations of causal relations between macrostate variables.
- 4.
The capacity of complex systems, particularly ones with nonlinear internals, to undergo fundamental behavioral changes is called “evolvability.” This topic will be taken up below in this Introduction and in several sections of the book.
- 5.
The quotes here, and not on the chemists’ laws, is a reminder that law-like phenomena seem to get rarer as we go from physics up the ladder of complexity to biological and psychological phenomena. There has been a long active debate within the sciences and the philosophy of science as to whether there really are laws in a strict sense, or emergent “rules” that govern the phenomena. See Unger and Smolin (2015), Unger’s chapter 5 in particular.
- 6.
George Klir referred to Thinghood vs. Systemhood (2001). The former is the content of a science—what the thing is—while the latter is the set of properties that defines systemness and is applicable across all sciences.
- 7.
Somewhat similar but still restricted descriptions occur in more general fields such as evolution theory, ecological theory, and reaction theory (to name a few), where the emphasis is on categories of phenomena. Ironically, however, these are the same areas that are currently morphing into systems sciences applied, for example, systems biology!
- 8.
See for example the excellent summary in Rousseau et al. (2018, Chap. 1).
- 9.
Ludwig von Bertalanffy (1901–1972), a mathematical biologist, proposed the idea of a general systems theory (GST) and also the review presented in the reference in the previous footnote.
- 10.
Chapter 5, “Complexity,” in Mobus and Kalton (2014) provides a general overview of various kinds of complexity but settles on Herbert Simon’s hierarchy-based definition (see Sect. 5.2.2). A fuller development of the concept of “Simonian” complexity can be found in Sect. 4.3.3.7 in this volume.
- 11.
Physical chemistry is the interface between these disciplines, so is essentially transdisciplinary. Atoms emerged from the physical forces (strong, weak, and electromagnetic) during the emergence of the Universe (Big Bang).
- 12.
Throughout the book we use the term hierarchy as it is more commonly understood. However, the kinds of hierarchies we will be describing should be understood to actually be “holarchies,” which are similarly structured hierarchies of “holons.” These terms were proposed by Arthur Koestler in his book The Ghost in the Machine (1967). In the current book, the reader will recognize that what will be called “subsystems” are holons and the structural containment tree to be described in Chap. 4 is a holarchy.
- 13.
Alternatively, we call these “transparent boxes,” and black boxes are referred to as “opaque boxes.” The author prefers the latter terms since the former are too strongly associated with machine descriptions and the latter terms are more neutral with respect to substrate media (a living system, for example). Additionally, the latter terms are not associated with specific disciplines.
- 14.
A very good article in Scientific American reports on an important new imaging technology developed by one of the developers of optogenetics. This technology allows the imaging of the living connectome.
- 15.
Several authors have considered higher levels of knowledge such as wisdom—the knowledge of how to use knowledge (Potter 1971)—and higher still is conscience or moral sentiment (Damasio 1994, p. 230). These higher forms of knowledge are relevant to high-order system patterns such as agency and governance, which will be discussed in Chap. 12.
- 16.
Note that it is the political–social framework that prevents these advantages from being shared globally. There is no reason that people all over the world should not benefit from the standard of living provided by engineering, except for political machinations and the fact that the human population growth is out of the normal controls.
- 17.
Complete understanding is a relative term! Complete suggests absolute, and science does not deal in absolutes (except possibly in temperature). The progression in science and engineering is based on “more complete” understanding, which characterizes a process of getting closer to the “truth” but not claiming to have arrived at the ultimate truth.
- 18.
It is hypothesis-like, in that the conjecture says, in effect, the model I have created constitutes the important features of the real concrete system. If the two systems, the concrete and the abstract, both have the same behavior, within some arbitrary degree of accuracy and precision, then the hypothesis is not disproved. Otherwise, it is back to the drawing board.
- 19.
Most models require continuous input data as time passes.
- 20.
We have adopted the term “opaque-box” to replace the conventional “black-box” terminology from reverse engineering. Opaque implies the possibility of transforming the opaque boundary to a transparent one.
- 21.
One implication of this notion is that some form of mentalese (systemese) is present in all brains throughout evolution. In other words, worms’ very primitive brain-like structures process a very primitive version of systems, namely they are “aware” of limited environmental sources (e.g., food in the soil) and have sensory and processing ability to determine the internal states of their bodies and make response decisions that result in overall behavior.
- 22.
In addition, our vision processing module is interoperable with the linguistic module so that visual images play a role in describing models, e.g., flow diagrams, visual maps, etc. We will describe this more fully in Chap. 4.
- 23.
Checkland’s concern for differentiating was largely due to the attempts that were made in the 1960s to apply engineering approaches to the design of “soft” systems like companies or government agencies, where human factors could not be treated as parameters in an equation. At the time, systems engineering was still in a nascent state and a number of systems engineers and mathematicians were attempting to define systems from their perspective alone. This led to what this author considers some premature and limited notions of a definition of system (c.f. Wymore 1967, Chap. 2).
- 24.
As discussed in Chap. 6, this “starting point” of analysis, wherein some questionable user requirements are included in the initial specification of a system, has caused innumerable failures in projects in the past and has led modern systems and software development methodologies to be collectively known as “agile.” These methods essentially abandon getting an upfront right specification of a system and instead adopt an iterative refinement method that includes having the user on the development team so that as the errors of design become apparent, the user member can change their specification on the fly.
- 25.
The idea of a knowledgebase, as opposed to a mere database, comes from Principle 9, discussed in Chap. 2. Knowledge is organized and effective data—models, rather than just aggregations of data. Knowledgebases are accessed differently than databases—by association rather than by indexical search.
Bibliography
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Mobus, G.E. (2022). Introduction. In: Systems Science: Theory, Analysis, Modeling, and Design. Springer, Cham. https://doi.org/10.1007/978-3-030-93482-8_1
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