As it turns out, the reality-based, science-friendly communities and information sources many of us depend on also largely failed. We had time to prepare for this pandemic at the state, local, and household level, even if the government was terribly lagging, but we squandered it because of widespread asystemic thinking: the inability to think about complex systems and their dynamics. We faltered because of our failure to consider risk in its full context, especially when dealing with coupled risk—when multiple things can go wrong together. We were hampered by our inability to think about second- and third-order effects and by our susceptibility to scientism—the false comfort of assuming that numbers and percentages give us a solid empirical basis. We failed to understand that complex systems defy simplistic reductionism.
On point analysis. Hits many of the themes of this blog, including system thinking, complexity and risk and makes some excellent points that all of us in quality should be thinking deeply upon.
COVID-19 is not a black swan. Pandemics like this have been well predicted. This event is a different set of failures, that on a hopefully smaller scale most of us are unfortunately familiar with in our organizations.
I certainly didn’t break out of the mainstream narrative. I traveled in February, went to a conference and then held a small event on the 29th.
The article stresses the importance of considering the trade-offs between resilience, efficiency, and redundancy within the system, and how the second- and third-order impacts can reverberate. It’s well worth reading for the analysis of the growth of COVID-19, and more importantly our reaction to it, from a systems perspective.
Talk about strategy, risk management or change and it is inevitable that the acronym VUCA — short for volatility, uncertainty, complexity, and ambiguity—will come up. VUCA is basically a catchall for “Hey, it’s crazy out there!” And like many catch-all’s it is misleading, VUCA conflates four distinct types of challenges that demand four distinct types of responses. VUCA can quickly become a crutch, a way to throw off the hard work of strategy and planning—after all, you can’t prepare for a VUCA world, right?
The mistake folks often make here is treating these four traits as a single idea, which leads to poorer decision making.
VUCA really isn’t a tool. It’s a checklist of four things that hopefully your system is paying attention to. All four represent distinct elements that make our environment and organization harder to grasp and control.
We often think that complicated and complex are on a continuum, that complex is just a magnitude above complicated; or that they are synonyms. These are actually different, and one cannot address complex systems in the same way as complicated. Many improvement efforts fail by not seeing the difference and they throw resources at projects that are bound for failure because they are looking at the system the wrong way.
Complicated problems originate from causes that can be individually distinguished; they can be addressed piece by piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems they present admit permanent solutions.
Complex problems result from networks of multiple interacting causes that cannot be individually distinguished and must be addressed as entire systems. In complex systems the same starting conditions can produce different outcomes, depending on interactions of the elements in the system. They cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems they present cannot be solved once and for ever, but require to be systematically managed and typically any intervention merges into new problems as a result of the interventions dealing with them; and the relevant systems cannot be controlled – the best one can do is to influence them, or learn to “dance with them” as Donella Meadows said.
Lets break down some ways these look and act different by looking at some of the key terminology.
Causality, the relationship between the thing that happens and the thing that causes it
Linear cause-and-effect pathways allow us to identify individual causes for observed effects.
Because we are dealing with patterns arising from networks of multiple interacting (and interconnected) causes, there are no clearly distinguishable cause-and-effect pathways.
This challenges the usefulness of root cause analysis. Most common root cause analysis methodologies are based on cause-and-effect.
Linearity, the relationships between elements of a process and the output
Every input has a proportionate output
Outputs are not proportional or linearly related to inputs; small changes in one part of the system can cause sudden and unexpected outputs in other parts of the system or even system-wide reorganization.
Think on how many major changes, breakthroughs and transformations, fail.
Reducibility, breaking down the problem
We can decompose the system into its structural parts and fully understand the functional relationships between these parts in a piecemeal way.
The structural parts of the system are multi-functional — the same function can be performed by different structural parts. These parts are also richly inter-related i.e. they change one another in unexpected ways as they interact. We can therefore never fully understand these inter-relationships
This is the challenge for our problem solving methodologies, which mostly assume that a problem can be broken down into its constituent parts. Complex problems present as emergent patterns resulting from dynamic interactions between multiple non-linearly connected parts. In these systems, we’re rarely able to distinguish the real problem, and even small and well-intentioned interventions may result in disproportionate and unintended consequences.
One structure-one function due to their environments being delimited i.e. governing constraints are in place that allows the system to interact only with selected or approved types of systems. Functions can be delimited either by closing the system (no interaction) or closing its environment (limited or constrained interactions).
Complicated systems can be fully known as a result and are mappable.
Complex systems are open systems, to the extent that it is often difficult to determine where the system ends and another start. Complex systems are also nested they are part of larger scale complex systems, e.g. an organisation within an industry within an economy. It is therefore impossible to separate the system from its context.
This makes modeling an issue of replicating the system, it cannot be reduced. We cannot transform complex systems into complicated ones by spending more time and resources on collecting more data or developing better maps.
Some ideas for moving forward
Once you understand that you are in a complex system instead of a complicated process you can start looking for ways to deal with it. These are areas we need to increase capabilities with as quality professionals.
Methodologies and best practices to decouple parts of a larger system so they are not so interdependent and build in redundancy to reduce the chance of large-scale failures.
Use storytelling and counterfactuals. Stories can give great insight because the storyteller’s reflections are not limited by available data.
If we want to address the complex problem situations that the world is facing, being a smart systems thinker and innovator is not enough. We need to engage in new ways of collaborating that promote continuous, productive and collective learning and innovation. These collaborations require us to learn social skills, build social structures, and adopt attitudes of openness to learning, trust and responsibility, however hard it is to let go of the behaviours and structures that hold us back.
Good article on problem-solving and complexity that is very sympathetic with Donella Meadows Leverage Points. This article and my recent post on creativity are both coming from similar points by stressing many of the same solutions to solving problems.
I liked the discussion on creating the right organization structures to allow problem-solving to happen. As someone who is very worried that can contribute to laying the bricks in Kafka’s castle and the bars in Weber’s Iron Cage, I am always striving to push for better ways of working, of creating structures that both amplify freedom and responsibility, that drive for innovation. Applying basic principles is pretty important to ensure we build for now and the future.