Complexity is not something people are trained to deal with, but it is fundamental now. Learning how systems operate is fundamental. But thinking “in systems” does not come normally to people. Our deepest assumptions favor breaking things down into manageable pieces – solving problems – managing processes – one at a time. In a system, the whole is greater than the sum of its parts. So in places, it makes sense to start looking at and dealing with the whole place. Our common experience says education isn’t taken care of in the schools, crime isn’t solved in the police department, health isn’t secured in the doctor’s office. But our habits and our shared approach is to fix things by working within silos; and in a complex systems this no longer produces the needed results. Competence that reaches across boundaries is one piece of a new approach; and failure to cross boundaries is a people problem.
For clarity, here is a simple explanation of system. A pile of sand is not a system. If you remove a sand particle, there is still a pile of sand. However, a functioning car is a system. Remove the battery and the car is no longer have a working car. A system is two or more elements in mutual interaction. And mutual interaction – rather than a simple one-way cause-and-effect relationship is what makes systems tricky. Tinker with something over here, and it changes something no one was even looking at over there. This is because everything in a system affects everything else. No one, no group, no country has the ability to anticipate all the interactions, so working within the system, across boundaries is one key to achieving better outcomes. The amount of diversity in an operating system creates is tied to the ability of a system to operate properly.
Many more fields are engaged in systems thinking, and without formal study, more and more people are learning about systems through an understanding of ecosystems. The roots of the Digital Age began with the field of cybernetics, an interdisciplinary study after WWII. Cybernetics provides a means for examining the design and function of any system and cybernetics and systems theory are considered synonymous. In all fields of science, complexity and systems thinking are now foundational.
Our hundreds of years of fruitful, successful thinking: reducing things to their parts – breaking down parts and their operations – perhaps ultimately to mathematic formulas, in short the scientific approach, has provided huge benefits to the world justifying the prestige and firmness of this intellectual tradition. It works. But it doesn’t work very well for describing or trying to manage complex systems.
Starting with this, the virus in Wuhan became a pandemic very rapidly through the patterns of global airline connections, where the number of people and the variety of destinations to which they travel, has never been possible on this scale until the Digital Age. To see the patterns, scientists used computer models to track the spread of the virus based on the ticketing databases for airlines. “Alessandro Vespignani of Northeastern University calculated that the countries of highest risk of importing a case of Covid-19 were in Asia, followed by North America and Europe – that is exactly how the virus traveled,” says science writer Debora MacKenzie. “Victoria Colizza of Sorbonne University in Paris calculated that the African county most likely to import a case was Egypt, followed by Algeria. Those countries, in that order, got Africa’s first cases. The fact that the world is a complex system helps explain how this pandemic happened,” she says.
In a complex system there is mutual interaction connected through feedback loops. Looking at the rapid transmission of Covid-19, these feedback loops are largely and transportation loops (airlines full of people with places to go) and communications loops (official communications, media, social media, data and data reporting). People who model systems understand how to look at feedback loops as key indicators of how the system is functioning. They use active measurement (in feedback loops) to understand systems change (planned or unplanned) like the infection rates and death totals state by state and many other useful data analyses. MacKenzie, author of Covid-19, The Pandemic That Never Should Have Happened and how to stop the next one, explains this about connections within a system, “A loose network absorbs shock; a tightly coupled one transmits it.” And tightly coupled systems are the result of the quest for efficiency. The more efficient the system, the less likely it builds in redundancy, a key component for resilience.
It took some time to recognize patterns, and this too is common, our habit is to continue to operate seeing things as they were, until a new pattern becomes clear. Once aware, then it is possible to understand more, how things now operate, risks to be attended to, what needs to be responded to. With Covid-19 the system failed because it continued to operate with the same purpose and governing ideology it had pre-pandemic, not fully recognizing the new pattern and risks.
Nobel Prize-winning economist Joseph Stiglitz says “the only countries that have been successful are countries where the government has played a very important role.” In the U.S. he sees the unshakable belief in ‘markets’ as ‘problem solvers’ as a problem in itself. He says that government isn’t the answer to every problem, but markets are not perfect either, and holding on to that simple pattern puts society at risk.
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