[endiangroup]

Having studied complexity from a computational perspective (via Santa Fe Institute and 1st wave Cybernetics) and a natural sciences one (via Dave Snowden and Alicia Juarrero) my preference is to stay away from modelling complex systems particularly complex adaptive ones. There is value in modelling, but heed the advice that all models are wrong. If you want to understand why, take a look at Steven Wolfram's Computational Irreducibility and Dave Snowden's Cynefin framework.

As for your interest in self-assembly and emergence I would highly recommend Alicia Juarrero's Dynamics in Action and Context Changes Everything - they are both tapping biological sciences to update and better inform our views of the world in deeply meaningful ways. The former changes our notion of cause-and-effect as the driving force in complex systems, stepping away from the Newtonian billiard ball frame. The latter expands on it talking about how constraints underpin the actions and dynamics in complex systems.

I'd agree that I'd love to see some convergence eventually in the complexity sciences world - but it is a new science relatively speaking - the divergence is a positive property in my opinion!

Keep up the energy, keep writing and keep researching! I enjoyed your post, it reminded me of the excitement I have for the field as a whole and the thirst I had for very similar questions! I wouldn't of guessed you were a 16yr old had you not stated it. Be prepared to have fundamental views changed and get comfortable with uncertainty!

[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

[AndrewKemendo]

The field of cybernetics covers 100% of this context

The fact that nobody considers cybernetics an actual functional study is an unmitigated tragedy - based on politics - that needs to be fixed immediately

Wiener, etc. laid the whole thing out, gave a very compelling and clear way to approach questions and complexity, and we are just as a field completely ignoring it to our own detriment with this idea that somebody needs to invent it

[omnicognate]

Any recommendations for learning it?

[AndrewKemendo]

My personal approach has been trying to find a general Markov decision process solution to any feeback loop process I can identify.

I first encountered this as the Scientific method where there is an experimental hypothesis testing loop the defines are epistemology

That was intersectional with computers for me trying to learn path finding algorithms in early video game development that I was working on as a early teenager

I read about John Boyd and the OODA loop Which got me interested in process control systems - defined in a mechanical way While still in high school. This led me to the Air Force and studying economics and trying to apply the concept of a processing loop to solve generalized decision-making

Because at the time economics had just turned into a statistical science and cognitive science was new, my economics study really became behavioral economics and so I began to study how humans actually do the “loop” as experimental data production via actuating our effectors and sensors into a prediction network. So my research started becoming around how do we model infant learning in computing architecture and prediction for human action - Frank Guerin and Ben Goertzel became my loose mentors for this direction

It was at that point that I really started to understand the Markov decision process and I got very deep into starting to learn reinforcement learning in the tradition of Richard Sutton starting around 2008.

If you go deep enough into generalizable reinforcement learning agent structures, you wind up in cybernetics because you have an agent, environment, predictive modeling, and physical systems requirement that is best defined in the way that cybernetics allows us to define them.

[skndr]

I recommend:

- Designing Freedom by Stafford Beer

- Brain of the Firm by Stafford Beer

- Thinking in Systems by Donella Meadows

[datadrivenangel]

Exactly. All human systems are sociotechnical, and people within systems respond differently when they know that they're being monitored, and even more differently when they know that the monitors know that they know they're being monitored.

Exact models are somewhere between impossible and implausibly expensive for a non-trivial system. Approximate or die.

[bbor]

What’s not exact about a human brain…? Complex, sure. But I don’t see the difference in kind you allude to

[navaati]

It does not help you having a perfect and exact rule from initial conditions to future prediction... if you cannot have a complete and exact description of your initial conditions.

We might never be able to get this exact description of the internal state of a brain, this is what makes perfect prediction impossible.

[burnished]

As it turns out, pretty much everything. Individual neurons are imprecise and adaptive and they form networks that show chaotic behavior.

Basically I'd turn the question around - what makes you think brains are exact?

[hdarshane]

Thanks a lot! SO many interesting recommendations in your comment. Yes, I agree that divergence is a very positive property right now, it might also allow for even more unexpected cross-pollinations and comparative studies as the pool of studies gets bigger.

Yes, I think I agree that just general non-determinism within such systems makes it impossible to "model" them. But, I believe regardless of how stochastic the behaviors are, there are certain properties that the systems might be optimising for. Long way to go for all of us.

Thanks again!

[bbor]

What do you suggest we do instead of modeling complex systems…? Isn’t that kinda an important part of aerospace, psychology, and meteorology, just to name three random things? Likely misinterpreting; I doubt anyone is anti-modeling in general!

[endiangroup]

To be clear I didn't say "don't model" - models can have value but all models are wrong because the map is not the territory. That's a first order problem, any representation of something is not the thing itself and bears no requirement to continuously and accurately represent it.

Another first order problem is you must chose from all the data that is perceivable what is pertinent to model because you can't perceive and model it all (at least not in complex systems) - that 'is perceivable' and 'choosing what is pertinent' act as filters that rarely are questioned. How do you know that 'what is pertinent' hasn't changed if your only way to reason about the system is through the model itself? How do you know you aren't perceiving something more relevant? Designing models to be sensitive to what we mean and not what we've stated is a very hard problem. The state space of reality is far bigger than we can sense and store.

A second order problem lies in human propensity for low energy states, when given a model or a metric we'll champion it as the truth or the way because it is easier than facing the complexity - but complex systems are crafty they adapt continuously. For example, your boss wants 100% test coverage and a dashboard has been created to report it to them... Fine, we'll test the getters and setters, we'll modify the dashboard code to ignore certain files, we'll write pointless tests that just exercise code but make no assertions... etc. They likely won't check provided the dashboard keeps reporting what they want to see.

Another second order problem is in complex adaptive systems the act of measurement changes the behaviour of the system itself. We know this intuitively, pull out a camera and start recording some strangers who were going about their business.

As for "what should we do instead" - complexity is all about context dependence... so it depends? If you are in a complex adaptive system, get involved! You can't know it all - so have fun with it.

[RobotToaster]

I feel like any such unified theory of complexity would approach the complexity of unified field theory.

Of course you would have to model it's complexity to know with any certainty.