Systems Breed Perplexity
Once a problem is recognized as a “Problem” it undergoes subtle metamorphosis. Experts in the “Problem” area proceed to elaborate its complexity. They design complex Systems to attack it. This approach guarantees failure, at least for all but the most pedestrian tasks.
The problem is a Problem precisely because it is incorrectly conceptualized in the first place, and a large System for studying and attacking the Problem merely locks in the erroneous conceptualization into the minds of everyone concerned.
What is required is not a large System but a different approach. Trying to design a System in the hope that the System will somehow solve the Problem, rather than simply solving the Problem in the first place, is to present oneself with two problems in place of one. ~ John Gall, Systemantics: The Systems Bible
There are several key features of complexity that people are not willing to face. Not willing to face them, people appeal to systems thinking, which merely breeds more problems. It’s like expecting to tame the situation by pouring water onto a gremlin. So the first point to remember is that a complex situation is not a system! We might call the situation an open dynamic system, but that just hedges the point.
Complexity is better thought of as a state of affairs. There are multiple points of agency, all of which have complex feedloops that cannot be modeled as a system. For each moment in time, the state of affairs leads to multiple futures, many more than we can see or sometimes, even imagine. Therefore, the second key point is to remember we should be prepared to be surprised.
The Forking Feedloops!
In the arena of multiple futures, we should not be surprised by a complete reversal of our expected outcome. As John Gall says, with sincere irony, Systems oppose their primary functions— they kick back! When we model a system, we map relations into feedback (negative, regulators) and feedforward (positive, amplifiers) relations. However, in complex situations, the feedloops fork. Which means that interactions can flip their interdependent effects from positive to negative. This is a key marker of complexity, and the third point: we should remember that the feedloops will fork.
So far we have derived a closer meaning of complexity as a state of affairs with evolving multiple futures and complex feedloops that fork. A fourth key feature is that the forking feedloops can become recursive. This leads to global transitions (phase shifts) which often steer toward homeostasis by becoming either binary (polarized) or entropic (disordered).
A different approach?
When facing a state of complex affairs, we need something different than systems approaches. Some of these seem counter-intuitive to systems thinking, because they are!
Allow the situation to reveal its own truths. When we apply a systems lens, we tend to dismiss truths as merely being “problems in the system” instead of vital information. Therefore we try to fix the system we believe in, rather than adjust our beliefs to the truths of the matter.
Choose simple, powerful actions when facing multiple futures. Too much reliance on systems thinking tends to make us try to solve the problem “all at once” which a grand strategic vision. Of course, this is bound to go awry, when the state of affairs is already complex, but in this case, we get little or no information from our failure. If instead, we take one clear course of action, not in order to solve a problem, but in order to reveal key cues in the situation, we may still fail, but we won’t fail to learn. And if our choice is wise or lucky, we will have nudged the situation toward a preferred future.
Model your course of action clearly, but be willing to change course readily. The grandmasters of chess prepare their opening strategy with a great deal of precision. But the best GM’s continuously scan the board as it evolves into new potential states and adjust accordingly.
Systemize or formalize your action plan in order to be able to learn as you evolve. Sensemaking in complex situations is retrospective. You connect the dots from here back to where you’ve been. Without an accurate snapshot of where you are, and a shared understanding of the steps taken, you will not be able to learn from the journey.
What you learn retrospectively should not be used to posit causal properties of “the system.” The benefit is to create tacit understanding of the journey you are on— more like collective storytelling, than systems thinking