2.1. The Scope and Maturity of Systems Research’s Resources
Systems Research as an activity draws, in addition to knowledge from the specialized science domains, on three kinds of knowledge about systems.
First, we have hundreds of systemic research methods for analyzing a problem situation and devising plans of action, for example Action Research, Systems Dynamics, Operational Research, Soft Systems Methodology, Critical Systems Thinking, etc. [11
Second, the methods draw to various degrees on about a dozen scientific theories about specialized aspects of system organization and behavior: Control Theory, Network Theory, Hierarchy Theory, Communication Systems Theory, Living Systems Theory, etc. [14
Third, Systems Research is guided by a set of about a dozen heuristic principles that represent the collective wisdom of the systems community about the general nature of systems. These have not been formalized, and many variants exist in the literature, but they show some overlap despite differences in how they are formulated. Examples of these principles include statements such as that “systems have properties their parts do not have by themselves”, “systems both change their environment and adapt to it”, and “systems may be parts in several wider containing systems”: see e.g., [15
] (pp. 17–30), [16
] (pp. 33–38), [17
] (pp. 60–71), [18
] (pp. 99–105). These principles form guiding orientations and basic assumptions for conducting Systems Research, and are therefore part of the philosophical component (“Guidance Framework”) of the transdisciplinary field of Systemology, as illustrated in Figure 1
In relation to this generic map of the structure of a discipline, known as the “AKG Model” [19
], Systems Research can be understood as part of the “Activity Scope” of Systemology, and the systemic methodologies and systems theories referred to earlier are part of the “Knowledge Base” of Systemology.
In terms of the maturity of these components, there are a dozen or so robust scientific systems theories and these have been notably useful in systems engineering (e.g., Control Systems Theory and Network Theory). Systemic methodologies are plentiful (hundreds), and at least two dozen are widely used but their development has outpaced the development of systems theories and most are heuristically based and only weakly grounded in scientific systems theory [20
] (p. 311). The systems principles in use are heuristically based and not standardized in terms of their scope and terminology. There are relatively few of them (about a dozen), and individual researchers maintain and develop their own versions (see Table 1
for a representative sampling).
The lack of development and maturity of the systems principles is a key handicap for Systems Research. To show this, I briefly explain the nature and function of principles in science in general and hence in systems science specifically.
2.2. The Nature and Significance of Principles
There are multiple terminologies and perspectives in science and in philosophy on the nature of principles, laws, theories and models. For present purposes, I will follow a perspective called Scientific Realism, which is presently the dominant view amongst metaphysicians of science [21
] (p. 299), and is well matched to the working practice of practicing scientists. Briefly, Scientific Realism posits that a concrete world exists independently of our mental states, that the truth of our theories depend on the nature of the world, and that our best scientific theories are approximately true of the world (see Endnote 2). Within the framework of Scientific Realism, I will follow a model known as the “Principles-Laws-Theories” (PLT) model of modern science [22
]. For present purposes, I will focus only on its notion of principles (I discuss the PLT model more broadly in [23
]. The PLT model represents an early attempt (1996) in the modern resurgence of metaphysics to show how modern science depends on metaphysical principles and how such principles relate to scientific laws and scientific theories. The metaphysics of science has advanced rapidly in the last two decades, but in my view the basic structure of the PLT model is still the most practically useful framing we have of these relationships. Please note that terms such as ‘principle’ and ‘law’ have different meanings in different kinds of disciplines, as discussed in Endnote 3; it is important not to conflate these different uses.
In science, principles are the most fundamental assumptions we make about the nature of the world. They represent what we take to be true in general, and hence fulfill a number of orienting functions, including [22
] (pp. 65–72):
Encapsulating what is deemed ontologically or metaphysically possible or necessary (for example, the “Principle of Sufficient Reason” (which claims that effects have proportionate causes) is a presumption against the occurrence of miracles);
Setting bounds of scientific forms of reasoning (for example the “Principle of the Uniformity of Nature” (which claims that the same causes always produce the same effects) supports reasoning from evidence to conclusions or predictions);
Providing guidelines for doing science (for example the “Energy Conservation Principle” provides a way of checking that all the contributors to a given effect have been identified);
Defining basic concepts (for example, Newton’s so-called “Laws of Motion” are really not laws at all but refined definitions of the notion of a “force”).
The principles are not independent claims but can overlap or reinforce each other. For example, the Principle of Sufficient Reason can be viewed as a corollary of the Principle of the Uniformity of Nature (or vice versa).
The principles of science are grounding assumptions and hence not provable by science. However, they are provisional and can be challenged and amended. Nevertheless, they are regarded as representing deep truths about the nature of the world, and their formulation and evolution is informed by progress in science. They express what we take to be the conditions for the possibility of the empirical phenomena observed by sentient beings. In this way, the principles of science represent the invisible reality underlying the phenomenal one, and form part of metaphysics rather than science. Taken together, the principles of science characterize the nature of Nature, so we might say that our image of the nature of Nature is the gestalt that reconciles the joint entailments of the principles (rather like the elephant image that reconciles the observations of the seven blind men). These relationships are illustrated in a simplified way in Figure 2
. Changes in the principles can have dramatic consequences for the scientific paradigm, as for example occurred when the Newtonian notion of “mass” was redefined by Einstein’s General Relativity theory.
Principles generally start out as qualitative heuristics based on limited observations, and only later on (typically with great difficulty) become exact, quantifiable, and profound. For example, the (heuristic) Aristotelian notion of a force had been defined simply as a push or a pull, while the (scientific) notion from Newton (involving “inertia”) was quantitative and carried profound implications, leading to the “Mechanical Revolution”.
2.3. The Nature and Significance of Systems Principles
The content of Systems Science is distinct from that of the specialized sciences, but the structure of Systems science is likely to be no different from that of the rest of science. From this brief review we can thus form some idea of the scope and potential of systems principles. We can directly paraphrase the above discussion for the systems case as illustrated in Figure 3
The correspondence between these two diagrams lies in the observation that Systems Philosophy models the systemic nature of the nature of Nature, and Systems Science models the systemic nature of manifest systems. In Figure 3
, the two modelling arrows and the intervention arrow involve Systems Research activities.
Paraphrasing what was said above about our image of Nature, we can now propose the following. Systems principles are the grounding assumptions of systems science, and hence not provable by systems science. However, they are provisional and can be challenged and amended. Nevertheless, they are regarded as representing deep truths about the systemic nature of the world, and their formulation and evolution is informed by progress in systems science. They express what we take to be the conditions for the possibility of the empirically systemic phenomena observed by systems thinkers. In this way, the systems principles represent the systemic nature of the invisible reality underlying the systemicity of the phenomenal one, and form part of systems philosophy rather than systems science. Taken together, the systems principles characterize the nature of systemness, so we might say that our image of the nature of “system” is the gestalt that reconciles the joint entailments of the systems principles. A set of coherent and scientific systems principles would form the core of a foundational general systems theory (designated GST* since 2015 [19
]), and changes in the systems principles could have dramatic consequences for the systems worldview.
As with the principles of science, systems principles can be heuristic or scientific. At present, we have only or mostly heuristic systems principles, and they are not unified. One consequence has been a proliferation of “images” of what systemness entails: the International Encyclopedia of Systems and Cybernetics
] devotes 18 pages to listing variations on the system concept found in the systems literature, and an ongoing Fellows Project in the International Council on Systems Engineering (INCOSE) has reviewed more than a hundred current and historical system definitions in an effort aimed at proposing one that is appropriate for contemporary systems engineering (personal communication from Hillary Sillitto, 22 November 2016). This variety is a clear indication of the incompleteness and immaturity of our present systems principles.
2.4. The Link between the Incompleteness of Systems Principles and Project Risks
In the light of an understanding that systems principles jointly characterize the nature of systems, and the incompleteness of the present set of systems principles, it is evident that our scientific understanding of the nature of complex systems is also incomplete and immature. From this we can easily see how some of the risks faced by complex systems projects derive from this shortcoming in our knowledge about the nature of systems.
For example, it has been argued that significant contributing factors to the failure or underperformance of complex system projects is a lack of methodologies for transferring knowledge and experience across project phases and between projects, and inadequate education in systems thinking [29
]. However, adequately addressing such factors requires a general systems theory, in order to ground objectivity and completeness when capturing knowledge and experience relative to a system of interest, and when teaching students how to think about systemic scenarios.
It has also been argued that systems theories are increasingly inadequate for supporting complex systems design. Recent studies claim that the rising complexity of engineered systems has eroded systems engineers’ ability to predict the outcome of design decisions [31
], leaving them without a principled basis against which they can check unexpected system behavior [30
]. A general systems theory, based on more complete and more scientific principles, would be helpful for identifying how the specialized aspects of systems, as represented in current systems theories, interoperate to produce system-level behaviors.
Establishing a scientific theory about the nature of systems is therefore arguably the key requirement for future success in complex systemic design, intervention and governance.
2.5. The Potential and Limits of of Heuristics
The argument developed here should not be taken as dismissive of the value of heuristic principles, or to denigrate the systems principles we have so far. Heuristic principles necessarily pre-date scientific ones, and they are articulated precisely because they have significant practical value [33
]. As Sir Geoffrey Vickers noted, “Throughout almost the whole of human history, technology has progressed with an uncanny ignorance of the scientific principles which were guiding it” [34
]. Technologies based on heuristic principles achieved astonishing sophistication even in ancient times. Well-known examples of high technological achievements pre-dating the scientific revolution include colloidal copper ceramic glazes (China, 10th century CE), crucible (wootz) steel (India, 3rd century CE), composite bows (Mongolia, 1700 BCE), geopolymers (Egypt, 2500 BCE), megalithic engineering (France 4700 BCE), and ultrafine polishing of corundum-rich stones (China, 6000 BCE).
The power of heuristics, however, creates two hazards. First, as John Warfield noted, “When technology leads, as in the computer age, it is inevitable that practices not embedded in scientific foundations will evolve to a status of dominance. When this occurs—and in fact it has—it is also inevitable that some time will elapse before these practices will give way to new practices founded in science” [35
]. This is evident in the extent to which systems methodologies are multiplying while little attention is given to advancing the foundational scientific understanding of systemness.
Second, effective heuristic principles tend to limit the imaginations of scientists and technologists to human-scale phenomenology, so that for example control of fire led heuristically to technologies such as pottery, smoke-cured meats, baked bread, convective underfloor heating and gunpowder. All these applications fell under the broad idea of things changing due to the application of heat. However, once fire was understood scientifically in terms of chemistry and electromagnetism it opened routes to inventions unimaginable from thinking about fire phenomenologically, such as computers, lasers, mobile phones, heart pacemakers, micro-wave ovens, radio, MRI scanners, and radar astronomy.
The transition from craft-based technology to science-based technology happens when a way is found to transition from heuristic to scientific principles for grounding the approach to action. Principles characterize the nature of a phenomenon, so at the outset they are necessarily based on experiences, and hence we always start with qualitative rules of thumb (heuristics). To become scientific, the observations have to be framed in a way that is quantifiable and significant for scientific exploration. For example (returning to an example briefly mentioned before), the concept of a “force” was heuristically defined by Aristotle as a push or pull, meaning a force is what causes something to move. This followed naturally from observing donkeys pulling loaded carts, where the cart moved when the donkey pulled and stopped when the donkey did. The idea that forces cause motions was the dominant view of the nature of a force from Aristotle (ca. 350 BCE) to the time of the Galileo (1600 CE). This model, however, created many puzzles, such as why arrows carried on moving after departing the tensed bowstring, and what caused the planets’ continuous motion. The scientific breakthrough was due to Newton, who introduced a new notion of a force as causing a change in the state of motion of an object, quantified as F = m × a. This definition of a force was scientifically profound as it expressed the concept in relationships between exactly quantifiable concepts and carried many testable implications, hence opening the way for the “Mechanical Revolution”. This example can easily be multiplied from the history of science, for example our contemporary scientific notion of the conservation of energy replaced Lucretius’s heuristic principle (dating from ca. 75 BCE) that “nothing comes from nothing”, and the scientific notion of relativistic space-time introduced by Einstein replaced the heuristic observation that everything that happens occurs at some time and at some location.
The great untapped potential of the systems perspective is evident in the observation that our best notion of systemness is pretty much still the phenomenological one we inherited from Aristotle, namely that the whole is more than the sum of the parts. Modern systems researchers have extended this via further heuristic principles, but these do not make profound conceptual shifts nor bring quantification and testable consequences, and although they paint a richer picture than Aristotle’s notion they have also created a fragmentation of the systems concepts in use.