Everyday human reasoning breaks down as the scale of time and space at play increases. Complex systems thinking gives us a new set of tools to better understand the chains of consequences involved, and make better decisions.
Our everyday lives revolve around well-abstracted systems that have clear cause and effect mechanisms. I flip a switch, the light turns on; the specifics of the electricity grid are irrelevant. I keep money at the bank, the bank gives me interest. I pay for supplies at the store, now I can take them home.
But when it comes to changing systems, our everyday cause and effect intuition fails us. Increasing dollars spent per child at schools doesn’t seem to improve educational outcomes. (Where does the money go?). Countries that impose travel restrictions a few days before their neighbors have exponentially lower fatalities from COVID-19. Teams whose members gel well outperform teams with more accomplished individuals, but lower team cohesion.
Linear changes produce nonlinear results. The whole is not its sum. Our intuition flies out the window.
This essay briefly explores these phenomena through the paradigm of systems thinking. Of particular interest are a class of systems called Complex Systems, which seem to evade reductionist, clean explanations. They’re complex, not because they’re complicated; it’s because even simple yet nonlinear interactions between their components give rise to rich behavior that cannot be understood in terms of any one individual factor (Meadows, 1982). Our goal here is to take a “whole systems” view of why many of these complex systems exhibit resilience to change, by examining their underlying dynamics; more specifically, their attractors.
We start by exploring attractors with the Lorenz system, then introduce causal loop diagrams as duals to differential equations, concluding with the impacts of attractors on climate change policies.
The Lorenz System The Lorenz System is a classic set of three differential equations. It’s a useful model, as while the dynamics of the three components can be succinctly summarized in the three equations, they give rise to highly dynamic, chaotic behavior1 that is a characteristic of many real world systems. Specifically, the equations describe the evolution over time of three variables, and (Lorenz, 1963)....
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