The behavior of a high-dimensional dynamical system can, very roughly speaking, be divided into two regimes. The first is what one might call the "effective dynamics" regime, in which the complex, high-dimensional dynamics can be well approximated (in the observables that one particularly cares about, at least) by lower-dimensional effective equations or models that emerge from the more fundamental laws of motion, and are easier to understand and analyze. A classical example is the laws of thermodynamics, which can effectively govern (some of the) macroscopic behavior of a large number of interacting particles, due to mixing effects that greatly simplify the impact of most of the degrees of freedom. Another example from physics is Hooke's law, that asserts that an elastic object, such as a spring, exerts a linear restoring force to push it in the direction of its equilibrium. Similar linear restoring force phenomena can be seen across the sciences (such as climate science, biology, economics, or even political science): not as fundamental laws of nature, but as empirically observable laws that emerge from more fundamental ones. Such effective laws can provide a valuable amount of long-term stability, predictability, and simplification to the dynamical understanding of many real-world complex systems. (1/4)

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)

In many aspects of the modern world (economic, political, environmental, technological, etc.), we are arguably transitioning from a predictable regime of effective dynamics to a more uncertain regime without such effective dynamics. As such, empirical laws that were trustworthy in the past, may become significantly less accurate, and can in fact become dangerous to rely upon too blindly. (An example from my own recent personal experience: as the world's climate departs from its historical equilibrium state, the empirical observation that dangerous fires mainly occur in summer rather than winter is no longer safe to assume.) The world is becoming more non-perturbative, more nonlinear, and more coupled; developments in distant countries or demographics, or in distant disciplines, have more impact on one's personal life than before, in ways that cannot be easily described by a small number of macro variables. (Example: the COVID-19 pandemic.) (3/4)

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)

@tao Thanks for saying this as you did. You managed to put some sensible words to a feeling I have been trying to articulate to myself for some time now.
@tao - if they'd only built LA *in* the Pacific, the fires could have been avoided.
@tao What prompted these fascinating musings?
@tao In engineering you also have control non-linear stuff. You typically try to tame this by splitting your device up in predictable modules, using things like a distribution plate to get a uniform flow, etc.
In politics this is a lot more tricky, people want to be in control of their lives. The problem is that other people also want to be in control of *their* lives, which can create conflict. This is a highly non-linear chaotic system. If society is in “peace-harmony” mode, then conflicts tend to get solved, and having other people around is actually beneficial. But in any society, there are always some “criminals”. Being the only criminal in an otherwise peaceful society would be evolutionary beneficial to the criminal (ignoring moral issues). But if there are too many criminals, or other forms of “bad” people, the whole trust system of society can collapse. “Good” people will get increasingly more tempted to also become “bad”. I could go on to talk about war, but maybe I am getting pedantic...
@tao It is as though humans like to live on the edge of chaos, and the more we are able to deal with complex systems, the closer to the edge we move.
Perhaps living on that boundary keeps more doors open than living in a calm linear region.

@TomL @tao

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.

@tao

@tao complements to occam's razor
@tao The emergence of collective intelligence practices is supposed to be able to addresse this. A single human mind will have trouble addressing the increasing world complexity, but gathering diverse world visions to create a more accurate cognitive map of a particular situation will enable the group to make better, more accurate decisions. Grouped with agile and continuous improvment methods should help
@tao This topic is sometimes discussed in cybernetics under the name "law of requisite variety" (or requisite complexity). https://en.wikipedia.org/wiki/Variety_(cybernetics)
Variety (cybernetics) - Wikipedia