[DevX101]

There's value in studying this stuff rigorously even if you can't predict the exact future state of complex systems. Being able to model whether a system tends towards equilibrium or disequilibrium is enormously valuable.

In economics, George Soros's theory of reflexivity, for example, is a rejection of efficient market hypothesis. The idea here being that price signals can lead to second order effects and market disequilibrium.

In ecology/climate, it's very useful to understand what kinds of perturbations (introduction of cane toads to Australia) are more likely to break equilibrium.

In fluid dynamics, we still don't really understand turbulence, but we can do useful modelling in wind tunnels without grokking the fundamental principles.

As the world becomes increasingly interconnected it becomes even more important to get a rigorous understanding of this science. We might not get to the power of Harry Seldon's psychohistory anytime soon but there's useful value we can gain along the way.

[jebarker]

Statistical Mechanics is another good example here. As I understand it, it's what allows much of the understanding of the very early universe despite us being unable to model it explicitly or observe it directly.

[engineer_22]

> In fluid dynamics, we still don't really understand turbulence,

Not to be pedantic - isn't this mathematically well described?

You seem to have broad knowledge, am appealing to your insight :)

[pixl97]

The regime change from laminar to turbulent flow is chaotic, so looking at a system in bulk will give you a decent description of the system, looking at any one part of the system will give you a random answer. This becomes problematic at systems boundaries, especially in engineering where we would like to understand the safety factor. Simulation can give you a reasonable starting place, but at the end of the day you still need test articles to see how it performs in reality.

For example, would you consider the three body problem well described? Even if it is, the solution goes chaotic rather quickly.

[engineer_22]

Gotcha, thank you, appreciate the insight