COVID-19 responses focusing on complex systems
Long-time readers of UGM’s briefings will recall that we’ve been advocating the value of taking a ‘complexity approach’ to business for well over a decade. To be perfectly frank, it initially felt like an uphill battle. People were very comfortable with traditional rational-linear approaches, made popular and accessible during the past century, for use in industrial (machine- and factory-based) contexts.
Given the focus on rational-linear approaches in education, to prepare people for their working future, it’s little surprise that we are drawn to those familiar concepts and approaches in business. For example, knowing that if we want to produce twice as many widgets, we simply allow twice as long or double our production capacity. This stems from a proportional (linear) relationship to which a logical approach is easily applied for different outcomes. Want two, double. Want four, quadruple. Simple.
Most recently, thanks to the novel corona virus, we’ve been bombarded by the mostly unwelcome outcomes of events unfolding in non-linear ways. A ‘non-linear system’, in maths and science, is one where the change in output is not proportional to the input. In this kind of system, twice the time or double the production doesn’t necessarily equate to twice as much output. This is truly mind-boggling for those grounded in rational-linear thinking.
But, non-linearity occurs a lot more often than most appreciate and therefore accommodate. Everyday complex systems (which describe our modern world) have non-linearity as one of their characteristics.
You’ve no doubt pondered some of the COVID-19 ‘curves’ that have been broadcast. Projected rates of infection, hospitalisation, ventilator requirement and death, based on types of response taken to fight the virus. Like many others, you may also have wondered why those rates varied among jurisdictions. In Australia, states and territories are compared. Farther afield, we read daily announcements and comparisons of the differing outcomes for various countries. Lots and lots of curves. At once, many similarities and many differences. Another characteristic of complexity.
At its core, complexity occurs because numerous factors interact, frequently through inter-relationships that are difficult to pinpoint and challenging to predict. We may understand part of a system based on its history but, as with ASIC’s mandatory warning in financial systems, we must be aware that “past performance is not a reliable indicator of future performance”. You may have heard or read that caution many times before but not given it much thought. Now, apply the warning to most of your world, made of systems within systems. All of these are complex and carry the same caution.
We live in a VUCA world
The phrase VUCA initially described conditions in conflict: Volatile, Uncertain, Complex and Ambiguous. First usage coincided with the emerging field of complexity and chaos theory, where researchers from a wide range of disciplines were searching for better ways of explaining their own domains of interest than through the dominant rational-linear approach.
While VUCA is the acronym that has taken hold, it would probably be more accurately represented with C as the first letter. Complexity is at the core, with volatility, uncertainty and ambiguity among its key attributes. Since we’ve already looked briefly at complexity, let’s take a quick look at the other terms.
Volatility in our COVID -19 world
Volatility is about change in the system – both rate of change and impact of any particular change. In the pandemic we have both high rates of change and quite variable outcomes from prospective initiatives. Journalist Jessica Irvine wrote “Pity the authors of economics textbooks. Every day of the COVID-19 crisis, it becomes necessary to rip out a new page and start again”. We should get used to new courses of action being taken, based on the emergent data of yesterday. Last week’s is well past its use-by date.
Uncertainty in COVID-19 contexts
Predictability is low. Who would have imagined toilet paper would become a proxy for uncertainty and fear? In some places, there is a huge increase in sales of firearms and ammunition. And we haven’t even mentioned the medical uncertainties connected with the pandemic, such as: who will get it; how will it impact individuals; what vaccine will work; how long will that take? Surprises are a matter of course – thankfully, some will be positive.
Recently, you may have heard or even had some of the following thoughts: “I have a few cold/flu like symptoms, do I have the virus?” “Can I get tested?” “Do the numerous cruise ships anchored off Australia’s coast, without crew and no port to call home, have claim to scarce ICU beds and ventilators – ahead of mainland Australians if it came to that?” All of these and many others illustrate the ambiguous challenges of this COVID-19 context.
An unintended consequence of COVID-19
The pandemic is a complex system. Hopefully, one useful outcome of the ubiquity of COIVD-19 will be to generate breakthrough thinking about how rational linear approaches fall short, as well as why and how to employ non-linear approaches to managing our VUCA world, including beyond COVID-19.
PRACTICAL IDEAS TO APPLY IN YOUR BUSINESS
Consider VUCA in your own context
- Reflect on some of the problem solving scenarios you face in your business. What are the differences in approach when challenges have multiple, interdependent variables (complex problems) compared with instances where answers are quite obvious?
- How heavily do you depend on rational-linear thinking? It has a place, but there are many more contexts in your VUCA world that are better served by a non-linear, change agility approach. For example, how much data is viewed through a ‘cause-effect’ type of lens (a sure sign of rational-linear thinking).
- In the context of problem solving, how resilient are you and your people? Do you promote and support an experimental approach which, as part of the approach, has both success and failure as outcomes? How well is failure tolerated?
- When solving complex problems, to what extent do you probe the assumptions behind the data you’re using to support your complex decision making? How alert are you to interconnectedness between data that might be hidden?
- How certain are you about the predictions or forecasts being made? Predicting weather, for example, is considered scientifically accurate up to 10 days ahead.