In many cases, the effective dynamics essentially decouple many of the degrees of freedom, allowing one to study simpler subsystems almost in isolation, with only a few bulk variables remaining to represent all the exogenous factors. (This is for instance the case in modern economics, in which a complex economy of a large number of independent agents can be decoupled, as an initial approximation, into independent microeconomic systems, interacting with some background macroeconomic variables such as inflation, interest rates, or unemployment.)
However, there is also complementary regime of "no effective dynamics", where the hypotheses on the system state that permit simpler approximations to the dynamics to be effective break down (particularly over longer time scales), because some key variables become non-perturbative, or so correlated with other variables that statistical laws such as the law of large numbers are no longer accurate. For instance, if one pulls a spring too far from its equilibrium, then the internal structure of the spring can be impacted, and the restoring force can become nonlinear, or cease to exist entirely (or in more plain language, the spring can break if pulled too hard). (2/4)
Meanwhile, the trend in recent years has been to prefer models of the world that are as simplified or low dimensional as possible - in particular, short enough to describe in a meme, short text, or short video to circulate on social media. For instance, regarding the Los Angeles fires, there were several comments on such media suggesting that one simply use the water from the nearby Pacific Ocean to douse the fires, suggesting an oversimplified firefighting model in which complex issues of logistics, wind, salt corrosion, etc. were reduced to the mere proximity of any large source of water, regardless of quality or supporting infrastructure. As H. J. Mencken wrote back in 1920, "Explanations exist; they have existed for all time; there is always a well-known solution to every human problem—neat, plausible, and wrong."
Complex dynamics are going to require complex solutions, in which simplified models are still partially used when appropriate, but with as much awareness of their limitations as possible; long-standing assumptions are tested and updated as new information about the current state of the world comes in; and human expertise and perspectives on the many different aspects of the problem are combined in a constructive fashion. These are all achievable practices - indeed, in my own field of mathematics, they are all completely standard parts of our workflow. Despite recent trends, I do hope that they will also be adopted more broadly. (4/4)
In biophysics, one finds that this is a property of life itself. We describe effective models assuming some separation of scales, but when checking numbers, we keep finding they're not well separated. So living systems always sit at saddle points, and I guess that's what makes them robust because in the course of evolution, they've acquired the necessary control on how to switch between a range of effective models.
Not sure, but possibly superorganisms of social animals also do.
As you say, @TomL, human society also seems to like to live on the edge of chaos. Afaik, earlier societies didn't have the necessary concepts to predict outcomes even of rather simple dynamics (eg, #currency and #inflation in the Roman empire or Louis XV France). Now that we understand that, we invent new systems (eg #subprimes) that bring more complexity/nonlinearity.
Not sure how to understand this when it comes to interacting with hugely complex systems like the biosphere, though.