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!
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.
Not to be pedantic - isn't this mathematically well described?
You seem to have broad knowledge, am appealing to your insight :)
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
Exact models are somewhere between impossible and implausibly expensive for a non-trivial system. Approximate or die.
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!
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.
Of course you would have to model it's complexity to know with any certainty.
I bring this up because I believe that at the time catastrophe theory was seen as a "widely-applicable science of systems". Or at least some practitioners tried to sell it as such. This point of view eventually soured to the detriment of catastrophe theory, which cleared out. I don't think this was a good thing: catastrophe theory (the study of the singularities of smooth maps and their consequences to dynamical systems) is an interesting topic with many remaining open questions. But it was seen as cringe that people were, e.g., using Whitney's classification of the generic singularities of planar maps to try to say something about predator/prey dynamics or whatever. Any claim about applications of catastrophe theory was infected with this stink, and so people lost interest in it.
I agree with your general sentiment that chasing "wide applicability" or trying to force a narrative that xyz theory will explain xyz might be hugely detrimental.
I agree my post and many discussions about complex systems, specifically one in an evangelic-type light might be over-optimistic.
We definitely must approach all work on such a theory with careful attempts not to overhype it. My post was an attempt to lay out some interesting possibilities.
We must remain optimistic anyway but I will be more careful in this regard going forward. Thanks again.
Wikipedia suggests it lost a lot of drive in part because AI and computer science split off from it.
Interesting to note that if you look for journals on cybernetics, most papers are closer to EE, Deep Learning and some telecommunications here and there, if that constitutes as a good metric of how much the semantic meaning has shifted from its original identity.
You can get a free PDF of the book here: http://pcp.vub.ac.be/ASHBBOOK.html
> This publication has been made possible largely through Mick Ashby, the author's grandson, who has convinced the copyright holders (the Ashby estate) that they should allow us to produce an electronic version.
I'm of the opinion right now that what we call "design" and "architecture" is really just the science of finding stable habitable zones in high-dimensional problem spaces.
What's cool about Alexander's work is that he makes a great case that this stuff is objective phenomena that can be studied!
I'm planning to write much more about Christopher Alexander on my own blog in the future, but meanwhile I can recommend Dorian Taylor's excellent works:
• https://the.natureof.software
I gave a talk on this subject at DDD Europe this year, so keep your eyes out for "Timeless Way of Software - Taylor Troesh" on their YouTube channel :)
>I'm of the opinion right now that what we call "design" and "architecture" is really just the science of finding stable habitable zones in high-dimensional problem spaces.
Wow! Yes! Agree with this view that all design and organisation is mostly just the most optimal/favorable state for the entire system to be in. What constitutes as favorability might be low free energy, high interconnect, distributedness etc.
May I suggest you to look into the work of Jeremy England in a similar light of self-assembly and optimisation in non-equilibrium states? Some really really interesting takeaways there, me sharing some of my interpretations might constitute as epistemic noise as I'm not sure if I understand each bit of it completely well at a 100%.
There was a great article about him in Quanta, and you might want to check out his talk at Karolinska Institutet.
Thanks for the recommendations, and I'll look out for your talk!
https://en.m.wikipedia.org/wiki/Second_law_of_thermodynamics
The information theory counterpart of entropy seems extremely relevant in describing coordination failures, some forms of stochasticness that aren't necessarily derived from lots of molecules with high degrees of freedoms interacting together. Also might hold high explanatory power in describing why trickle-down/bottom-up and top-down effects are slowly negated and diluted - although I believe this is fuzzy thinking and we need a better tool than just entropy to understand this.
Thanks!
As with any monopoly, the incentives for public school administrators are all out of wack. Adding a CS curricula takes real time and effort (lost summer vacation time, effort required to convince the board/PTA, picking a curriculum, hiring teachers for an unfamiliar topic). It brings with it real risks and headaches (budget issues, vulnerability/ignorance in a new domain, possible failure/embarrassment, board/PTA conflict, dissatisfied students/parents). Meanwhile the benefits are not tangible and the cost of not implementing a new CS curriculum is zero.
For public school administrators (as with all process owners) it's far easier to simply repeat what they did last year.
As problems get more complex this is the main headache -keeping everyone in the same boat rowing in same direction.
On what basis do you say this? What exactly do you mean?
I don't mind that the above claim is cynical. But I think it is (a) wildly overconfident and (b) poorly reasoned. Check your biases. Also check your pain points -- would I be crazy to guess that you've become jaded about student's ability to learn, think, and/or care about education?
Next, consider a specific scenario so we're not talking past each other. Let's say 5% of high schools decide to teach Thinking in Systems. Say they get a grant so that someone experienced (in the book and subject matter) comes in and teaches for a few weeks as a special topic (at no cost to the school).
Now, think statistically and empirically. What kinds of effects will there be on students? If you are intellectually honest, you'll have to ask questions, maybe even gather some data. If you put some effort into thinking, you probably won't conclude there will be zero effect.
Chiara Marletto and David Deutch developped such a system, called constructor theory. It is build up from constructors, which are like little computer programs that describe what they refer to as different "tasks". And these tasks are counterfactual operations.
How systems regulate themselves (and also perpetuate themselves) is the question behind cybernetics, and in the organisational context, management cybernetics. The problem generalises way beyond biology. The information theoretic and control implications are fascinating.
(I’m writing a book on the subject; ping me if interested)
Interesting! Will ping you!
1. Ashby's introduction, but that's from the 60s.
Foundational, but is limited to 1st order cybernetics.
2. E. W. Udo Küppers, A Transdisciplinary Introduction to the World of Cybernetics (2023): https://link.springer.com/book/10.1007/978-3-658-42117-5
Provided a broad picture and basic intro to the history, central concepts, and various involved thinkers involved.
3. Stuart Umpleby's 6 hour lectures on the Fundamentals and History of Cybernetics (2006): https://www.youtube.com/playlist?list=PLB81F4FC0EDC4DECC
Provides a historical overview and introduction to applications of cybernetics in social sciences.
Crucially, Umpleby's history mentions the exact reason why Cybernetics never quite took off in America (well, aside from the fact that it was just difficult to fit into a disciplinary box). Heinz von Foerster, head of the Biological Computer Laboratory, did not want to provide a b.s. military justification for Dept. of Defense funding after the Mansfield Amendment -- (which, was passed in response to student protests of divestment from the Vietnam War).
There is an development of this idea from some author which leads to something like https://imgur.com/a/AmF7AJe and currently the author tries to find the connection between syllogistics, Boolean algebra, Euler-Venn diagrams, and more. You can take a look at https://www.youtube.com/@Syllogist/videos Many of the recent videos have English subtitles It's hard to describe the whole idea at once, but maybe someone will have the courage to learn something from it.
That is, it may be less formal at the beginning, but then a craftsman may be found who will do everything quite strictly.
Nevertheless, now this author is developing the theory of syllogistics and there are quite practical results, for example, that the logic textbooks for lawyers can be translated into boolean algebra and made more precise.
on one level, something is a subsystem of a larger supersystem
on another, it's all the one and only system. but why wouldn't the components be systems in their own right?
and sure, it's all about the 'appropiate' level of abstraction. but my point is that any "science of systems" must give a working theory of levels; or at least say something on how to grapple with this. it's not sufficient to leave it as "that aspect is an art"
If that seems confusing, circular, or unsatisfactory, consider reading the work of the Pragmatists for the eye-opening revelation that this is what your brain is doing 100% of the time to 100% of your sensory input in order to make any sense of anything whatsoever.
Your perception is intrinsically linked to what you can do with that perceptual data. You separate a system from its components the same way you separate a rock from dirt, one piece of dirt from another, or soil from a tree: you do it based on what’s useful to you in the moment.
Complexity science is the major remaining terra-incognita of our era. The allure of seeking to break into such a genuinely new domain is strong. The intellectual (and not only) rewards would obviously be beyond compare. The dimensional extremes probed by "standard" micro and macro physics are increasingly into diminishing returns. Thousands of people, gargantuan budgets and devices etc. but in a macro sense, rather disappointing progress: Our mental toolkit and understanding of the world in 2024 is not that different from that the Einstein/Bohr era circa 1924. It has been an era of fleshing out the details, magnificent and more productive than any previous period of history, but it saturated without turning things upside-down.
All the while, complexity is all around us, even inside us. Mysterious, defying attempts at description, let alone explanation. One can setup experiments for next to nothing. Complexity is very "accessible". So why is it still a sketch of field rather than an actual field?
If the constraints are not external then they must be internal (cognitive limitations, blind spots etc). For sure we lack mathematical tools. But maybe we lack even an adequate set of relevant pre-mathematical notions, these vague but powerful concepts that precede the sharper analytical tools and elaborate equations.
One think is for sure: Very smart people have tried very hard and if you are going to see further you must (at least) climb on their shoulders :-)
why everything exists in a holonic sense i.e. a "whole" in its own is composed of many wholes themselves, and goes on to partake in a bigger "whole".
http://www.autodidactproject.org/quote/kant_CPR_architectoni...
http://staffweb.hkbu.edu.hk/ppp/ksp1/toc.html
I’m writing a book on all this atm, so feel free to hmu anytime if you want someone to bounce ideas off of! It’s a profitable time to be a philosopher of science, that’s for sure
There's also a developing community at https://www.systemsinnovation.network/, where there are also many (subscription) resources.
The articles, books, and guides available (free) at https://thesystemsthinker.com/ are also worth a look. This mostly pertains to system dynamics rather than any other traditions, but it's a great resource for understanding complexity.
Complexity by M. Waldrop https://commoncog.com/learning-from-waldrop-complexity/
The Systems Bible by J. Gall (This one is an odd one but it is good for developing a sense of humor) https://novelinvestor.com/notes/systemantics-how-systems-wor...
To me, it can be distilled down to human decision making... how flawed we are in terms of cause/effect, how logical fallacies are so powerful. Ultimately we discovered that the Scientific Method was a tool for us to overcome these flaws. Hopefully there is a similar tool out there in the ether which can help us to navigate complexity with similar confidence.
I think that tool might be just any general framework that gives a better idea of what any small microscopic actions might lead to at a high-level. We definitely cannot qua(nt/l)ify how actions seep through given free-will and a general fringe-ness to us. We can still at least identify most probably scenarios without much difficulty. I believe behavioral economics or even epistemic studies do a good job of identifying general trajectories, by virtue of a fairly high sense of reducibility given human behavior and the benefit of hindsight respectively.
Indeed the human mind by itself isn't really trained to think beyond maybe one or two orders of implications that actions hold. Hindsight and somehow modelling agentic behavior by understanding incentives might do the trick.
Thanks again!
I'll add a word of caution though. I'm most familiar with systems theory applied to biology. Biology is, in my opinion, the pinnacle of complexity. However, it's less well acknowledged that it's also very, very complicated. This is important because it means that we have very incomplete knowledge of the base components of any biological system. Like we still don't really know the basic biochemical function of most proteins. Hell, we only just got a partial view of what most proteins even look like (in isolation) via AlphaFold. Measuring the number of all of the proteins in a single cell is effectively impossible with current and near future technology. Any feasible solution for this would probably be destructive, meaning that true time-series measurements are also impossible. These details of what we know and what we can (or can't) observe matter quite a lot, not only because they are the sort of raw matter of a systems theory, but also because they are the levers that we have to use to manipulate the system. There are only about 1000 proteins that we know how to reliably bind molecules to. There are (probably, we're not sure) more than 50k different proteins, if we include isoforms. So, all that to say, we have very incomplete knowledge of biology and very incomplete control of cellular behavior.
This isn't meant to discourage you! Instead, I think there's a tremendous opportunity for systems theory to be really useful (especially in biology) if it becomes a practical, routine analysis like statistics. But, for that to happen, we have to keep in mind the limitations and specific details of the system we're dealing with.
Really like your thoughts!
Indeed, lack of time-series observability makes it harder for us to find general patterns or causal events.
Definitely agree that biology is the pinnacle of all complexity - IMO something like macroeconomics or human behavior within set systems (society, politics, etc.) is fairly reducible to a very small and finite set of incentives that agents optimise for (food, shelter, status, acceptance, etc.).
Given this, Non-linearity and stochasticness still adds up to a general nature of non-determinism for the entire system.
With Biology on the other hand is extremely more complicated to study as - correct me if I'm wrong - it's still hard to realise what agents in systems are optimising for. reduction of free energy? reproduction? general homeostasis? etc. and then all these play varying roles in diff contexts, and then we'll still have to figure out how/why self-assembly and "wholes" emerging from smaller "wholes" (... ad infinitum) actually happens.
Really fuzzy thoughts but I believe There is some merit in exploring reducibility and observability from a time series perspective while considering effects of synchronity/asynchronity of observability and later how much we can desirably steer systems. Really fuzzy but I hope to work on this a bit more.
Thanks a lot for your very interesting comments! Not discouraged at all, love your view on systems theory being a "routine analysis" like statistics, i.e. a very generally applicable layer or meta-science that's an entirely new way to see things, which I should've articulated better in my post.
I'm mostly thinking individual cells in a multicellular organism (i.e. lung cells in a person). It is indeed very hard to understand what they are optimizing for. Obviously, the organism as a whole is under selective pressure, but I'm not sure how much an individual cell in a given organism actually "feels" the pressure. Like, they undergo many cell cycles during one organism's life, but they're not really evolving or being selected during each cell cycle. Of course, this isn't always true as tumors definitely display selective pressure and evolution. But for normal tissue, I prefer to think of cells as dynamical systems operating under energetic and mass flux constraints. They're also constrained by the architecture of the interactions of the genes and proteins in the cell. All that adds up to something that looks a lot like evolutionarily optimized phenotypes, but I think that might be a bit deceptive, as the underlying process is different. It's not at all clear to me though. You're really getting at some deep questions! You might find this paper interesting in that regard:
https://www.nature.com/articles/nmeth.3254
Regarding reducibility and observability of time series, you might also find work from James (Jim) Sethna's lab at Cornell interesting. The math can be a bit hairy, but I think they do a pretty good job at distilling the concepts down so that they're intuitive. The overall idea is that some complex systems have "sloppiness", like some parts of the system can have any kind of weird, noisy behavior, but they don't change the overall behavior that much. Other parts of the system are "rigid", in that their behavior is tightly connected to the overall behavior.
https://arxiv.org/abs/2111.07176v1
You ought to get yourself connected with some folks at the Santa Fe Institute, if you haven't already. I know one affiliated professor, let me know if you want an introduction. At the very least, if you like podcasts, check theirs out. It's called "Complexity" and it's quite good.
I totally agree; it all boils down to math. Linear algebra formed the foundation of a lot of what we have achieved today, including computer simulations and AI, but now society is demanding problems that aren't based in linear algebra but in game theory, as the author describes. So we need to study game theory, that's what the next period of accelerated advancement will be based on
For what it’s worth, it took me spending 12+ years studying biochem and adjacent topics at university, to reach a very similar perspective.
The one criticism I’d make here (and tbh it’s unfair to expect more from the author) is that there has been a lot of work done towards this already. There are many systems biology textbooks, a much greater number of systems papers, and even entire journals on the subject. So I would reframe the observations slightly: there is a lot of prior work, and we need to double down on it and cross-pollinate it more.
This is especially tricky in the field of bio / biochem, which feels like it requires memorizing a million facts before one gets to do any real thinking and reasoning. This unfortunately tends to filter out a lot of more mathematically-minded people who are stimulated by puzzles, which I think is a shame.
For what it's worth, I really enjoyed this textbook. I'm not sure you will find in it what you're hoping to find, but I hope it sparks even more curiosity; it's fairly advanced, probably more targeted at late-BSc or MSc/PhD students: https://www.amazon.com/First-Course-Systems-Biology/dp/08153...