Martin Reynolds (The Open University). Applied Systems Thinking in Practice (ASTiP) Group. School of Engineering and Innovation. The Open University, Walton Hall, Milton Keynes MK7 6AA, United Kingdom +44 (0) 1908 654894 | martin.reynolds@open.ac.uk | Profile | Publications
From a systems thinking in practice (STiP) tradition I would first like to change the formulation from ‘complexity and systems science’ to complexity science and systems thinking (cf. Reynolds et al., 2016). The revised formulation is important for two reasons in appreciating respective lineages. First, contemporary ideas on complexity including the ‘butterfly effect’ and ‘complex adaptive systems’ are very much rooted in the scientific tradition dating from Warren Weaver’s 1947 paper ‘science and complexity’. Second, contemporary systems thinking should be regarded as a transdisciplinary endeavour inclusive of systems science and complexity science, but far beyond the confines of a scientific discipline (Reynolds and Howell, 2020). Note that systems science and complexity science have many common lineages, including pioneering work around cybernetics in the 1940s. Appreciating the value of complexity science and systems thinking requires in my view attention to the ontological and epistemological dimensions of appreciating complexity and systems.
Complexity as used in complexity science invokes the scientific ontological (real world) premise that everything connects. Ideas of uncertainty and emergence are tied to appreciating reality as an infinite network of interconnections, the effects of which are impossible to precisely predict. Systems science might be regarded as an endeavor to systematically bound such interconnections, recognized by an impartial observer as relevant to a particular situation of interest. By so doing, the ensuing bounded systems might be subject to scientific analysis. In systems science and complexity science, the key epistemological driver is positivism; there being an assumed direct representation between reality and systems (ontological realism; e.g. ‘the’ health system), subject to inquiry from an impartial ‘objective’ observer (scientist).
In contrast, complexity as used in a STiP tradition is an effect of contrasting human perspectives on the framing of interconnections, rather than an effect of interconnections directly. In the STiP tradition ‘systems’ as ontological representations of reality are legitimate, but the representations are always nominal (named by a human ‘observer’), provisional (with boundaries subject to change from other observers), and secondary. Nominal systems such as (i) natural systems (individual organisms, ecosystems, solar system etc.); or (ii) engineered (purposive) systems (mechanical devices ranging from computers to heating systems), are secondary to a primary understanding and active use of systems as conceptual constructs which may be referred to as (iii) human (purposeful) systems. Purposeful systems (where the bounded purposes are subject to ongoing adaptive change) are a powerful tool of contemporary STiP. As distinct from ‘seeing’ reality only as natural or engineered systems, purposeful systems enable such viewings to be tamed within a primary framing of a learning system (as an epistemological construct). Such primary framings enable organisations, and interventions in education, health, etc. to be not only evaluated but (re)designed. The STiP tradition, founded on epistemological constructivism, recognizes complexity as an effect of contrasting viewpoints on reality. Complexity here is a second-order attribute of interconnections in situations of interest – the indirect human framings of interconnections. Complexity in complexity science is a first-order attribute of the interconnections themselves.
The difference is significant for all practitioners in all professional fields. In a STiP tradition, complexity exists in all situations (since no human situation comprises only one perspective). Each individual or group of individuals frame things differently depending on lifeworld experiences including, amongst other demographics, ethnic backgrounds. STiP flushes out the framings of situations in terms of transparent purposeful systems in order to help improve the situations through more meaningful conversation amongst practitioners. With increasingly uncertain times where racism is being called out internationally through the killing of black American George Floyd, it is perhaps worth recalling the founding principle of STiP which takes its cue from C.West Churchman: “a systems approach begins when you see the world through the eyes of another” (1968 p. 23).
Evaluation Uncertainty 
That’s a timely and useful contribution Martin. I’m passing it on to colleagues for their response. Thanks.
It is a very useful post, a better articulation than I have managed to date on why the two should not be conflated into a single discipline. I would make the point that we are seeing distinctions in complexity between those who see it as a metaphor and those of us in the realist camp who increasingly split between computational and anthro-complexity. The latter argues that things like language, abstraction, and so on provide additional layers of complexity to human systems over birds flocking and termite nest building! But that is also trans-disciplinary. I’ve made the connection between strange attractors, tropes (from narrative theory), and assemblage (Deleuze et. al) in both theory and practice but would argue that epistemology is constrained by a realist perspective on ontology. Here the work of Thagard on coherence is important as is other work. I’ve passed this onto colleagues and will link in a blog post.
I find very much relevant your distinction between a first- and second-order complexity. Would you accept to link the first-order complexity to Weaver’s distinction between disorganised (micro level) and organised complexity (macro level), while attributing a second-order complexity to meso level? Mesoscopic reasoning is presently surprisingly poorly exploited in efforts to explain complex situations – surprising because mesoscopic perspective best explains the essence of both, society and complexity, how to bridge between democratically incommensurable primary concerns with secondary means in conditions of radical uncertainty. Mesoscoipic logic does not arise from compromise or golden middle but from new ontology and epistemology. Ontology of mesoscopic world relies on blindness, it is no more scientific but evaluative, founded on vast emptiness (void in Derrida) between islands of first-order considerations. For the evaluator, blindness is natural adaptation to uncertainties and incompleteness of factual presentations of reality, accommodation to darkness in which the search for complex truth can only take place. Like bats, who see through darkness with ears, not with eyes, the evaluator does not see reality through facts, but through double void, by squaring the indeterminacy of what is claimed to be known objectively, with zero, which is by ignorance of the knower – not really original procedure since it applies Hegelian dialectic method of negation of the negation at its meso level, synthesizing phase. I found very insightful also the philosophy of Charles Sanders Peirce with his distinction between firstness, secondness, and thirdness as three approaches to comprehending the world. His concept of ‘secondness of thirdness’, as it can be operationalised with a square matrix of the third order, seems especially useful for explaining socially complex situations in a mesoscopic way, in an explanatory rich way that can explain complex matters in the simplest way when explaining them at the middle level.
Reblogged this on Systems Community of Inquiry.
very nice piece on a tired old subject!
My take was here: https://stream.syscoi.com/2020/04/21/bringing-together-some-reason-and-old-threads-on-systemsthinking-is-complexity-is-cybernetics/
What I add, Martin, is that I think you are making valid and useful distinctions.
I do not think – and I think the selection of these comments bears this out – that this would generally be recognised as ‘the’ diifference that makes a difference between what people commonly define as ‘systems thinking’ and ‘complexity’. You are, of course (unlike some) careful to delineate and are specific about two traditions: ‘complexity science’ and ‘STiP’.
My point is that you will find self-described ‘complexity’ practitioners who are engaged entirely with what we might tag as ‘perspectival’ complexity (and, amongst them, some, I have no doubt, who would spit and poo-poo if you accused them of *any* proximity to systems thinking). And in the ‘systems thinking’ field, you would find many who are entirely focus on ‘ontological complexity’. That is why I think we need multiple layers of finer differentiation (including this contribution) to make more useful distinctions across and within the interwingled systems-complexity-cybernetics thicket.
One such distinction, for example, might be between those who implicitly or explicitly embrace a mind/world divide, and those who reject dualism (and, perhaps, situate meaning as arising from interaction).
The only person I know who takes a ‘perspectival’ approach to complexity and “poo-poo”s systems thinking is Ralph Stacy and he is hardly “self-described” as a complexity practitioner. Poo-pooing is, of course, a court marshall offense https://www.youtube.com/watch?v=QeF1JO7Ki8E and you are starting to sound a little like Lord Melchett here. A debate is best served if you avoid innuendo and either name names or at least reference what you are talking about The ontological aspect of a complex adaptive system with its understanding of a lack of linear material causality is to my mind critical and is not best served by your attempt to homogenize the field. The fact that a phenomenon was known and described is common but gaining a scientific understanding of its nature represents a shift and the language or the former state simply confuses the new insights and understandings. Quantum mechanics does not invalidate Newtonianism (within constraints) but it is materially different in nature and in practice.
Reposted on the Open Learning Commons at https://discuss.openlearning.cc/t/complexity-science-systems-thinking-systems-sciences/233 . (Further discussion may ensue there. Join us!)
An old uncle once told me that when I do not know clearly what I say, I should bringing in many concepts, notions and ideas into the conversation. This way, the audience may get focused on the detail and have a sense of losing the overall message, or may feel overwhelmed by the sheer collection of themes and give up on contesting them. Above all, he told me, do not make clear definitions, they will leave you in a weak spot; leave things vague, it gives a strong impression of knowing.
If I had to send to an audience clear notions of epistemological and ontological dimensions of systems and complexity, I would not follow my uncle’s advice. There is a simple way to make it understandable, even if some may not agree with me.
I would say that a system is a “being”, so it has independent existence from an observer. Immune systems have been around thousands of years before scientists found a way of demonstrating their existence. The ontological dimension of a system is unquestionable. From the epistemological perspective, systems can be described and classified according to conceptual apparatus that, by their turn, evolve as new ideas are brought into the guided observations.
On the other hand, complexity does not have ontological dimension; it is not a “being”. Complexity appears in the relation between observed and observer. When the observer acknowledges the limitations of the information and knowledge available to understand a certain aspect of reality, the notion of complexity can be used. Complexity is related to the capacity of the observer. An observer facing a “phenomena” (or “entity” or “being” or “sets of relations and transformations”, etc.) of which she/he realizes that there are many known unknowns as well as possible unknown unknowns among the numerous sets of elements and relations being studied, she/he can appropriately say that is addressing complexity. Complexity might disappear, once the observer has figured out the relations and elements she/he was interested in understanding. On the contrary, a system, even after being exhaustively described, does not disappear. That is the ontological difference and the respective epistemological implication for the motivated observers. Complexity can be reduced or increased by acts of observation. Instead, a system remains whatever it might be, independently from its observers; unless, the new observations are incorporated into a self-observing system, a particular feature of some types of systems… a topic for another discussion.