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!
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.
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
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.
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.
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!
My PhD thesis was about the extension of smooth functions. In the 1960s, ideas in this area were studied under the umbrella of ideals of smooth functions, and had applications to catastrophe theory. So I spent some time reading about this topic. If you want a reading recommendation, the book "Differentiable Germs and Catastrophes" I remember being good, as well as "Stable Mappings and their Singularities".
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.
Thank you for your very interesting comment and the reading recommendation.
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.
I feel like the real problem here is the way that research has to be sold in order to get funding (I assume the same was true in the 1960s). There is no inherent harm in trying to find a universalizing science, but market imperatives drive the creation of intellectual hypecycles with the associated booms and busts. That can then discredit entire fields.
I wonder if you're looking for cybernetics? The original meaning from the 1940s, not the corrupted science fiction shibboleth. Wikipedia - "The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems...Wiener introduced the neologism cybernetics to denote the study of 'teleological mechanisms'."
Wikipedia suggests it lost a lot of drive in part because AI and computer science split off from it.
Yes. I'm aware.
I think cybernetics has definitely drifted away from what the original scope was, also I think modern interpretations of cybernetics-ish ideas and complex system studies are slowly also incorporating social sciences and economics and so on.
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.
To me, Christopher Alexander's work comes closest to describing "systems science". I recommend Notes on the Synthesis of Form, The Timeless Way of Building, and then skipping to the Nature of Order series.
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:
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 :)
Interesting! Have heard about Christopher Alexander's work, but have never really jumped in. Maybe I should do that now.
>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!
I strongly recommend picking up Carliss Baldwin’s Design Rules. It is a great addition to the thinking done in Notes on the Synthesis of Form, in a more empirical and specific technological context (the advent of the computer).
Without knowing much about what you reference, this remembers me of the second law of thermodynamics [1], which coined entropy as a general concept for understanding many phenomena.
Absolutely.
Broadly, I wonder what leads to schools not adopting emerging fields as part of the formal curriculum. Interesting to note even in 2021, only 51% US k12 high schools were found to have a CS course. Does not seem like a capital problem to me. Is this just inertia or a legibility problem?
I was expecting someone mentioning this very good book. In a time where politics is dominated by populism, this should be part of the school curriculum. Reality is complex. There are often no easy or simple solutions to get a certain number up or down. Even the author of this book writes that being an expert in systems science does not give her the superpower of never being surprised by outcomes. But being able to think in systems is still a very valuable ability. A lot of humbleness and appreciation for complexity can be gained from reading this book.
It would be very useful to have an inheritance system to feed outputs of scientific theories into other theories. Just like how computer programs can access code from included libraries.
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 does the body react to imbalances in some internally-relevant biomarkers?
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)
There is an approach that I like and I think allows one to compress quite a few different description models from different domains. Take a look at this image from wikipedia: https://commons.wikimedia.org/wiki/File:Systematik-Philosoph... You see "Theoretische" and "Praktische" philosophy in the rectangle with a diagonal which probably could mean that you can sort something based on the relation between matter and information, where something is more practical and less theoretical (say 30% practice and 70% theory) add a stimulus-response diagram at the bottom and various information-material circuits inside that allow you to show how something is transformed.
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.
This is the problem I have with any study of "systems". It all just feels like overly ornate verbiage for "hey, this thing kinda looks like this other thing if you squint". It's very difficult to distinguish that imgur image from complete Time-Cube-level quackery. "Volume (more than 3 points) <==> deep cause-and-effect relationships <==> trivalent linguistics (he gives the book to his brother)" ... really? Is this really helping humanity understand anything?
my philosophical take is this all revolves around what constitues "one system"
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"
Every book I’ve read on systems pretty much opens with exactly this point. It is merely the questions you want to ask, or otherwise your scope of meaningful control, that determines what is a suitable definition (or delineation) of “the system of inquiry.”
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 :-)
Great article :). You definitely will be going places.
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".
You’re gonna love philosophy!! This is covered most definitively and scientifically by Hegel’s Science of Logic, but that’s like super advanced high level philosophy so you might not want to start there lol. Either way best of luck, I totally agree with your general thesis! You’ll be happy to know that, in general, this has already been solved by Kant, Hegel’s idol - even tho people have forgotten in the meantime.
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
The Open University runs a Master's program in systems thinking (which I'm currently studying). There's a free primer course called 'Mastering systems Thinking in Practice' that gives a good overview and is full of references for further reading in the field: https://www.open.edu/openlearn/science-maths-technology/mast...
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.
This is a wonderful post and articulates something I've also struggled with over the years - and I'm much older than 16!
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.
Great post, you're clearly on the right track. I totally agree that there is a major gap in modern theoretical understanding of how and why complex systems emerge. Breakthroughs in understanding the physical/informational processes that underlie complex adaptive systems could be immensely useful.
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.
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 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
This is an incredible perspective coming from a 16 y/o.
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.
Thank you so much for the kind words! I'm definitely a little ignorant with respect to systems biology efforts. I have an okay-ish idea of the work of someone like Uri Alon, it's definitely not enough. What resource would you recommend for me to jump into? Uri Alon's course? I believe I lack many pre-requisites to take it.
[+] [-] endiangroup|1 year ago|reply
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|1 year ago|reply
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.
[+] [-] AndrewKemendo|1 year ago|reply
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
[+] [-] datadrivenangel|1 year ago|reply
Exact models are somewhere between impossible and implausibly expensive for a non-trivial system. Approximate or die.
[+] [-] hdarshane|1 year ago|reply
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|1 year ago|reply
[+] [-] RobotToaster|1 year ago|reply
Of course you would have to model it's complexity to know with any certainty.
[+] [-] woopwoop|1 year ago|reply
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.
[+] [-] hdarshane|1 year ago|reply
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.
[+] [-] bokoharambe|1 year ago|reply
[+] [-] jprete|1 year ago|reply
Wikipedia suggests it lost a lot of drive in part because AI and computer science split off from it.
[+] [-] hdarshane|1 year ago|reply
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.
[+] [-] surprisetalk|1 year ago|reply
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
• https://doriantaylor.com
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 :)
• https://www.youtube.com/@ddd_eu
[+] [-] hdarshane|1 year ago|reply
>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!
[+] [-] llamaimperative|1 year ago|reply
[+] [-] Helmut10001|1 year ago|reply
https://en.m.wikipedia.org/wiki/Second_law_of_thermodynamics
[+] [-] dav|1 year ago|reply
https://www.goodreads.com/book/show/3828902
[+] [-] hdarshane|1 year ago|reply
[+] [-] vaylian|1 year ago|reply
[+] [-] hdbv6|1 year ago|reply
As problems get more complex this is the main headache -keeping everyone in the same boat rowing in same direction.
[+] [-] betagam|1 year ago|reply
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.
[+] [-] asplake|1 year ago|reply
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)
[+] [-] hdarshane|1 year ago|reply
Interesting! Will ping you!
[+] [-] vitalnodo|1 year ago|reply
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.
[+] [-] feoren|1 year ago|reply
[+] [-] ysofunny|1 year ago|reply
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"
[+] [-] llamaimperative|1 year ago|reply
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.
[+] [-] openrisk|1 year ago|reply
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 :-)
[+] [-] bbor|1 year ago|reply
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
[+] [-] dannyfraser|1 year ago|reply
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.
[+] [-] EncomLab|1 year ago|reply
[+] [-] sergius|1 year ago|reply
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...
[+] [-] bentt|1 year ago|reply
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.
[+] [-] hdarshane|1 year ago|reply
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!
[+] [-] bglazer|1 year ago|reply
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.
[+] [-] hdarshane|1 year ago|reply
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.
[+] [-] samrus|1 year ago|reply
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
[+] [-] epgui|1 year ago|reply
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.
[+] [-] hdarshane|1 year ago|reply
[+] [-] unknown|1 year ago|reply
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[+] [-] techbro92|1 year ago|reply
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[+] [-] unknown|1 year ago|reply
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