The Trouble with Systems Thinking

By Gerrit Van Wyk.

The grid Flood and Jackson use for Total System Intervention (TSI), unintentionally exposes a major dilemma system thinking faces.

The basic premise is the different systems methodologies work best in specific contexts, hence if you know the context, you can plug in the most appropriate methodology and solve the problem. The grid is distributed along two axes, the complexity of the system confronting you on one, and the social complexity involved on the other. What it implies is problem contexts in principle are identifiable, and they fit into categories, which assumes a mechanistic, or clockwork perspective of reality, and therein lies the conundrum.

The system’s nature is divided into simple/closed, and complex/open. Open systems have few components and interactions, are deterministic, organized, follow natural laws, are stable, parts are not purposeful, and the system is separated from the outside with a boundary, in other words, assume a reductive, mechanistic, clockwork perspective of reality, or ontology. Hence Operational Research (OR), RAND type systems analysis, systems engineering, Forrester, Kauffman, and Senge’s System Dynamics (SD), and Mason and Mitroff’s Strategic Assumption Surfacing and Testing (SAST), are all essentially mechanistically based. Complex systems have multiple components and interactions, are non-deterministic, evolve over time, have purposeful parts, and are not separated from what’s outside it, hence assumes a complexity perspective of reality. The paradox for Von Bertalanffy’s General Systems Theory, Tavistock’s socio-technical systems, and Beer’s Viable System Diagnosis (VSD), is they assume a complex system ontology, but reductionist, simplistic perspective of human social interaction, and for Ulrich’s Critical Systems Heuristics (CSH), that it assumes a reductionist, simplistic perspective of systems, and complex social dynamics. You can’t have it both ways. Ackoff’s Interactive planning (IP), and Checkland’s Soft Systems Methodology (SSM), assume a complex perspective of systems, and complicated perspective of human social dynamics, which crosses the bridge halfway. In short, systems approaches and methodologies philosophically are all over the place, and Flood and Jackson’s grid shows there are no systems methodologies, or approaches for both system and human social dynamics, which to me is a foundational problem with systems thinking.

Many practitioners of systems thinking describe systems, at least by implication, in terms of complex phenomena, and therein lies the problem, contradiction, or horns of a dilemma. You can know mechanistic systems by reducing it to its parts, everything in the system is measurable, you can know the purpose of a mechanistic system by adding the parts together again, components work together predictably, and you can accurately predict the future behavior of the system. Planners, like scientists in a laboratory, stand apart from what they investigate, like TSI, and they can control outcomes.

If you reduce a complex system to its parts, interconnections are lost, which are needed for the properties emerging from its interaction. To quote Douglas Adams, if you try and take a cat apart to see how it works, the first thing you have on your hands is a non-working cat. The parts of a system affect other parts in unpredictable ways, actions have consequences elsewhere, it can take a while before you see the consequences, what happens before matters, actions escalate in a non-linear way, the system is often stable and chaotic at the same time, and the further your prediction goes in the future, the more unpredictable it becomes. Not only can’t you control complex systems, as a systems thinker you are part of what you investigate and therefore unpredictably influence interactions and the outcome.

You can’t have it both ways, your perspective of systems must either be complex, as the systems idea suggests, or mechanistic and reductive. You can know things about systems through reduction and analysis, but that tells you nothing about systems as complex phenomena.

Despite the fact the systems idea was heavily influenced by biology and ecology, systems almost inevitably involve humans. Traditionally, we treat the added complexity as a black box, see for example Beer’s Viable System Model, but it is the way humans act, behave, etc., that critically determines what happens. We seldom open that black box when a systems approach fails, but if you do, almost inevitably the reason is the biological, behavioral, and social complexity humans add. In my experience, ignoring that complexity is not a good strategy.

Looking at comments to posts from the systems community from a helicopter perspective, suggests some interesting trends. On the one hand, in some cases we are still locked in silos of single methodologies, and look to the gurus who designed them for wisdom and certainty. If systems are complex, one need to look at them from multiple perspectives, may need multiple approaches, even during the same investigation, and can’t predict ahead of time which ones you may need. What’s needed emerge from the interaction, which is fundamental to complexity. That’s the problem with something like TSI.

On the other, there are many models to get a handle on complex systems, but the value depends on whether the model is designed to assign situations and problems to categories, the Cynefin model for example, which is still mechanistic. Complex phenomena are not easily assigned to categories, and likely show simple, complex, and chaotic behavior at the same time. System dynamic models can add useful perspectives when used to describe narratives, or stories of a situation, both more often are used as predictive tools, which is mechanistic. It’s impossible to know whether you included all relevant information to model, and from a complexity perspective, a single omission can have major consequences. Churchman pointed out there is a role for experimentation and measurement during inquiry, but also that one must be aware of its problems and shortcomings, and what assumptions they are based on.

Comments also show how deeply a mechanistic approach to reality is unconsciously embedded in our social world and thinking, and how difficult it is to escape it. Which folds into our understanding of complexity. Complexity can be an ontology, or perspective of reality, or, if you take a mechanistic perspective of reality, knowledge about aspects of that reality. The difference has significant implications for how we perceive systems, believe how they operate, and how we can approach phenomena systemically.

A mechanistic approach circles back to methodologies, Russian doll hierarchies like Miller’s living systems, and models and categories. A complexity as ontology perspective, creates a whole different set of challenges. To begin with, even when we reduce things to what appears to be simple, they are not, and it is just a useful perspective. The periodic table currently has 108 elements, but once you start combining even a handful, like in the human body, the level of complexity logarithmically increases. It is useful to reduce that complexity again to several organs and structures, but even that gives us a very limited perspective of what goes on in a human body. A mechanistic approach to body systems leaves out an enormous number of interconnections and interactions, which may be useful, or lead one astray. Hence which perspective of complexity you start with and the assumptions you make matters a lot in terms of how you approach phenomena as systems.

Systems thinking plays out within an arena of human social complexity. What I’ve been talking about until now is somewhat academic, and a little dry, but it leaves out the systems arena, in which power and politics play out, in which there are groups with their own norms and values, competition between subgroups, inclusion and exclusion, changing fashions, and much more. There are role expectations to being labelled as a systems thinker, which folds back into image and self-image, social hierarchies, and so on, which becomes somewhat visible to an observer observing the systems game from the bleachers.

No matter which way I look at it, my encounter with thinking in terms of systems and complexity fundamentally change me, and the way I look at life and reality. My mentor told me it’s not an easy path, and he was right, but I don’t regret taking it. I also know the way I think about systems and complexity is different from the majority position, and that it has social implications for me. I am very aware of both the strengths and shortcomings of the systems approach without being joined at the hip with a preferred methodology or approach, which can be a good thing, or a bad one.