After years of development in increasingly fracturing sub-disciplines it seems that systems science as an integrated whole domain of knowledge is rising again. For those familiar with the history of systems science you will recall that in the early 1950's after some fairly spectacular developments in fields such as cybernetics, information and communications theory, computation and algorithms, and a more holistic view of biology emerged the concept of general systems theory, or GST (von Bertalanffy, 1969). This theory advanced the notion that everything in the Universe could be understood as having common patterns of organization, structures, and functions regardless of the specific materials or energies involved. Many researchers have investigated the nature of these patterns (and a few more discovered since that time) and have found they exist everywhere as suggested. The promise of a general theory of systems was a way to actually understand phenomena in a deeper way than disciplinary sciences had been able to achieve. But the promise was not to be realized subsequently.
The nature of academia (PhD thesis and tenure requirements) tended to push researchers into increasingly narrow sub-disciplines within the GST domain. Complexity theorists gravitated toward their version of complex systems. Information theorists did the same, as did cyberneticians. Increasingly over the years the venues for sharing results among sub-disciplines became fewer as those for sharing within became dominant. Specialization, which seems to be a natural trait of our species, pulled the rug from under what promised to be a grand way to understand the world and organize our increasing knowledge (as a system!). For many years, even while people still talked about systems and even applied aspects of systems thinking to various disciplines (e.g. systems biology) the field as an integrated domain took a back seat to the disciplines themselves. How many degree programs in systems science can you name? The only one I know of in the US is at Portland State University (there are numerous others in the world but they tend to occupy more peripheral realms of academia).
Even as this dissociation of central ideas in GST progressed, and as the notion of a real science of systems as center to all other sciences failed to take hold, there remained a group of “loyalists”, many of whom descended from the giants of the 50's and 60's, who carried the banner and maintained a vision of what could be if systems science were ever recognized for its real potential more broadly (see below). I had been aware of these groups and some of their work, but had not been able to see the kind of work that I thought would be needed in order to reconsolidate the field and provide a general scaffolding for the knowledge of systems. My main concern was that many of the articles published in some of their journals and other venues still carried a special interest narrowness and I did not see many attempts to try to integrate these into a coherent GST-like framework. Most of all I could not find (and I looked very hard) any truly integrated textbooks on systems science. There were plenty of books on systems science or systems thinking (e.g. a whole series of excellent books by Fritjof Capra, but written for general readers and, in my view, only hinting at systems principles). So I decided to go the lone wolf route and develop a textbook for undergraduate-level, but general science majors. I got a great boost from my colleague Mike Kalton (co-author) and, together we crafted a book that we believe covers the breadth of topics found in systems science and explores many of these to sufficient depth that readers in the sciences should be able to see how some general principles of systemness apply.
With that book published by a major publishing house, I started meeting people in these organizations and exploring collaborations. To my great surprise and relief I discovered a fairly recent effort being carried on in the UK by a group of researchers to revive GST and bring it up to date with solidly grounded principles (I should point out that while I have confidence that those I published are relatively solid, my methods for identifying them were less rigorous than this group's approach). They are looking for a more general theory, what they are calling GST*. We are now in conversations about how to push this agenda forward.
Meanwhile I have pushed ahead with two major book projects. The first is tentatively titled “Understanding Systems: Systems Analysis, Modelling, and Design” in which I lay out the formal language of systems (see below) and the methodology of top-down functional/structural deconstruction. The end result of this process is a complete specification of a system of interest (at an appropriate level of abstraction) in the language which can then be compiled into computer codes and run in model simulation. If the system of interest is an existing or desired human-built one, the process leads to a complete specification for building, i.e. a systems engineering set of specifications. I have completed the formal mathematical framework for the language (actually a formal definition of systemness!) and the introduction of the methodology. But there is a long way to go. More on this later.
The second project is to apply the methods in the book just described to a “starting” analysis of the human social system or HSS. I have already described this in a previous post and will likely have a lot to say about it in subsequent posts. Needless to say it is a massive scale project. My objective is not to do a complete and detailed analysis but merely to show how such an analysis would proceed if following the systems analysis method. My plan is to go deeper into a few subsystems, such as the “Science and Engineering” and “Economic” subsystems, ones where I have some “expertise” or domain knowledge. These will serve as examples to other domain experts on how to proceed in their various areas. I could easily envision the project giving rise to a global cooperative (open source) project, much like a Wikipedia, to collect and organize all knowledge relating to the human condition — that is as long as computers and the Internet are still working!
One of the most critical subsystems of the HSS is, of course, the biological (and psychological) human subsystem. We are just one subsystem integrated into our cultural cocoons. This one I have already made a stab at. My whole series on the topic of “sapience”, and the subsequent book that I have completed were an effort to analyze the human psyche in an effort to discover why we consistently fail to learn from prior experiences and fail to make good judgements in our endeavours to flourish (e.g. global warming as a result of our persistent demand for power). If you have read any of that work you know that my conclusion was fairly discouraging. I have not yet decided what to do with the book. I have shopped around for a publisher but most responses include comments about how gloomy the conclusion is — where is the hopeful message at the end? Moreover, it might be that it more properly fits into a deeper analysis of the biological human subsystem, that is I should hold it back until the context for it has been better established. I still ponder that idea.
But several developments involving the stature and visibility of systems science in the established science community gives me pleasure if not hope. The systems organizations and research groups mentioned above are gaining some leverage. And why not? Most people who spend any time at all thinking about the major global problems that we face realize that everything is connected (a system) and will have to be addressed systemically. On the engineering side, many large complex product and service producing organizations (except I suppose our governments) have realized the need for a more informed version of systems engineering, that is informed by actual systems science (as all other engineering disciplines are informed by physics, chemistry, and/or biology, etc.) The need for systems thinking is becoming quite prominent. But systems thinking needs more than just casual thoughts about connectedness. It needs an actual science of systems to make explicit the nature of systemness and to provide the principles to guide methods and organize knowledge gained. By the evidence I have seen over the last year, it seems that an integrated systems science is ascending.
The NSF Calls for Systems Themes to be Added to the Next Gen Science Standards
The NGSS is a comprehensive K-12 STEM education standard that seeks to teach science, math, and engineering in a more holistic fashion, especially recognizing issues that are common across fields to increase students' grasping that science is a process rather than just a set of separate subjects. To that end the standards include a call for the inclusion of cross-cutting themes, topics that apply to all fields of STEM. For example, quantitative thinking applies to all of these fields and at many different levels. It is recognized that learning calculus, for example, in the same way that arithmetic is learned — as a standalone subject onto itself — is completely non-viable. However, calculus learned and used in the context of all of the science subjects is much more meaningful since the application is evident at the outset.
Recently the NSF has called for proposals to develop curriculum for some of the cross-cutting themes that they champion and one of them is explicitly systems science or systems thinking (the others are actually implicitly systems related, such as the relations between the vertical views of the sciences, i.e. from physics through chemistry through biology, etc.) Several of the Linz team are working on two related projects dealing with systems literacy in K-12 education. One of them is a proposal to the NSF to incorporate the SS research work of the IFSR (International Federation of Systems Research) and the ISSS (International Society for Systems Science) help inform the specific recommendations for cross-cutting themes in SS. Kalton's and my book is named as using examples from many different sciences to show how systems principles apply to all STEM subjects. It may be that our book will one day see duty in K-12 teacher education programs to prep them for teaching STEM.
Meanwhile the NSF has a strong interest directly in cross-cutting themes across its various directorates. Normally these are silos and administrate grants to disciplinary research with an occasional interdisciplinary approach where two or more directorates cooperate on a single grant program. It seems that these latter kinds of programs are popping up more frequently, especially with the rise of global systemic problems like climate change and energy. There was a meeting which many of the directors attended specifically to hear about how systems science is a transdisciplinary approach that could provide a backbone to organizing research agendas. It is just at the discussion stage but it seems many of the directors found it a very interesting idea.
The Language of Systems
In order for scientists of different disciplines to work in a truly transdisciplinary way they will need a common language to speak. This is a lot like the case of multiple kinds of computer languages being compiled or interpreted on a single architecture, say C, Java, Python, Fortran, etc. all being translated down to the machine code for a single computer. They all look different on the surface, but they all have a deeper sameness at the machine code level. Math is not sufficient because it is not easily interpretable from one discipline to another. It is not directly descriptive of structures, except, possibly for geometry or topology, mostly only functions and relations. Powerful enough if you already know the context of structures in which it is being used, but not outside of that domain.
It turns out that there is a fundamental language of systems that provides a way to describe both structures and functions (I briefly describe some of it in our book) that is universal across any kind of system and conforms to the principles outlined in our book. I am nearing completion of the basic specification of the language and will be presenting my results at the next ISSS conference in Boulder CO this July. This might prove timely as it could help persuade the NSF and others involved in the NGSS as well as educators, that there is hope for finding ways to actualize those cross-cutting systems themes.
The language has a basic lexicon, syntax, and semantics (as well as pragmatics provided by the principles of systems). I have been using formal language theory to develop it so that it will be extensible as future SS researchers may discover additional principles or nuances not now recognized. This language, which I formally call SL, but privately call “systemese”, is like the machine language of the universe. Any system you choose to analyze and model can be described in this language! I hope to find funding to start implementing a set of tools for using the language for doing formal analysis. The beauty of the approach is that the end product of analysis is a compilable program that is the model of the system.
The language does not just cover dynamics (e.g. system dynamics), or agents (agent-based), or evolutionary (e.g., genetic algorithms) models. It incorporates all of the above plus real adaptivity and learning (e.g. biological-like), and real evolvability (as when species or corporations evolve in complex non-stationary environments).
There is one additional aspect of systemese that goes beyond its use in system description and modelling. From my studies of brain functions (regarding sapience) I have ascertained that systemese and mentalese (the language of thought), a concept advanced by philosopher of mind Jerry Fodor, are basically one in the same! That is, our brains, at a subconscious level, use systemese to construct our models of how the world works. Our brains are designed to build models of the world based on our experiences of it. Because the world is composed of systems, we all get a similar result in terms of the models we construct, ergo we can agree on much about the world. Even so we each have different perspectives and very different experiences, even of the same phenomena, so tend to have a high degree of variation in the details of our models. Plus our wetware is notoriously prone to error — we make mistakes in perceptions and judgements. Furthermore, our perspectives are modulated by our affect system and our personalities. Sometimes the latter two can distort our versions of reality. But by and large most people, most of the time, can agree on some fundamental aspects of systems in the world. That is, when looking at real systems in their environments, they see pretty similar things, working in similar ways.The problem is when they attempt to take into account much broader environments and time scales beyond their sensory limits, when they attempt to model things beyond their ken. When they contemplate things like global warming or our own origins they are not really observing experiential inputs to their models. Rather they are reduced to accepting whatever authorities they have come to trust. So those models, the ones that extend beyond ordinary experience, can be extremely varied and all too often incorrect. In addition, when experience is lacking but motivation is strong we have a tendency to just make stuff up that “sounds” right.
But with that exception in mind, I think all human beings think subconsciously in systemese. I am right now working on the translation of systemese (mentalese) into natural language constructs (e.g., the translation of system/process objects into nouns). If this goes well I think I will be able to show that the natural language of Homo sapiens has its roots in systemese that evolved from the earliest brains. Early brains, as are found in cephalopods (squid and octopii) or arthropods (insects, spiders, etc.) appear to be the first to be able to form images of objects and detect relations between objects, including possibly action relations. These brains are adapted to speak a very primitive systemese internally, without conscious awareness. Over the course of evolution systemese developed more nuance with increasing brain complexity. The human brain with its extensive neocortex and prefrontal lobes has an extensive system vocabulary and more refined syntax that has evolved from the primitive versions. We also have an elaborate verbal input/output subsystem which makes it possible to share our thoughts (the conscious ones) with each othr. What I have called systems thinking in my development of the concept of sapience reflects the idea that human brains are in closer contact with this systemese as it frames our other sapience constructs (judgement, moral sentiment, and strategic thinking: See the category Sapience for all that I have written on this subject). However, like all of sapience, the strength of that capability is variable and most commonly low in the population.
After I publish in the literature I will share these ideas with the readers here. We'll see if it flies.
Solutions
Over the years this blog has been dedicated to the analysis of the human condition from a systems perspective. I have asked questions ranging from biophysical realities (as in real economics) to how the mind works, and what might consciousness be. Along the way I have offered a few of the answers I have developed (my beliefs) to some of those questions. I have spent no small amount of text analyzing the plight of the human condition regarding the major global problems that confront us and have concluded that in every case they are related to one another and are of our own making. In a comment on my last post Don Stewart asked if maybe I had the answers but just hadn't mentioned them. A few times I did explore things like living styles (permaculture with low energy footprint) that would be more sustainable than what the current BAU style could be. But as often pointed out to me in comments and actually quite obvious to me already, these cannot be solutions to the problems everyone (or the vast majority) want to have solved. The problem they want solved is for all the problems to go away through some magic of technology (e.g. alternative energy) so that they are not inconvenienced in their lifestyles. They do not want to give up their lifestyles, dependent on lots of high power energy so they won't even explore the options of living on far fewer resources (where the goal is still to live as comfortably as possible). And so they are doomed to suffer the loss of those resources the hard way, through depletion, and without adequate preparation for how to live.
On several occasions I have stated that the solution is the one the Universe has always had for systems that are out of control — the rise of negative feedback loops amplified such that the system is brought low (or destroyed completely) so that something new can evolve in its place. The physical (material) resources aren't used up; the metals and other minerals will still be available in abandoned cities. But the energy resources in fossil fuels will be gone, requiring that any future evolution must be based on real-time solar input as was the case for billions of years prior. The biosphere and humanity in particular are about to go through an evolutionary bottleneck event. And then the world starts over, with or without a hominid ape population.
That isn't good news for us. It is just the way the Universe works. The real problem we should be trying to solve is: Can our genus make it through this bottleneck with a viable breeding population, and if so, what will it take in the way of preparation? I can't easily address the first part. But I can work on a little piece of the second part, namely to consolidate our hard won knowledge of systems science and encode it into a medium that stands a chance of making it through the bottleneck. The book, my subsequent work on systemese, and leveraging on the current interest in systems science/thinking by the NSF (and other organizations) might be useful in this regard. The idea is that if there is a future Homo, say 5,000 years from now, they might be able to start from a better position of understanding the world than we did coming out of the Pleistocene. This is my version of acting on faith and with purpose!
However I have begun to realize I cannot do this work alone. I could use some help with both book projects and setting up an open source project for the development of an SL compiler and graphic front-end/knowledgebase back-end. I am lucky in starting to work with some new colleagues in various parts of the world, at least on the systems literacy and research questions. But I will need to enlist many others, especially younger people, who can grasp this vision and are ready to enlist in the effort. My email box is open!
References
Bertalanffy, L. von, (1969). General System Theory, George Braziller, New York.
So since this is a formal system, how does it account for the implications of Godel's theorem and of the field of non-Computational Mathematics? In other words the inherent limitations of Formal Systems and systems that are prove-ably intractable to algorithms.
Posted by: Pedro Lopez | May 08, 2016 at 06:53 AM
How is this different from a universal turing machine?
http://www.dna.caltech.edu/courses/cs129/caltech_restricted/Turing_1936_IBID.pdf
Posted by: Kevin Baas | May 08, 2016 at 07:34 AM
..or Mathematica's programming language? http://www.dna.caltech.edu/courses/cs129/caltech_restricted/Turing_1936_IBID.pdf
Posted by: Kevin Baas | May 08, 2016 at 07:39 AM
(sorry bad ling .. no edit button...) https://reference.wolfram.com/language/guide/LanguageOverview.html
Posted by: Kevin Baas | May 08, 2016 at 07:40 AM
I should have anticipated something like this. Call it QE Ascending (in hits).
Someone posted this to slashdot (/.) a computer "science", hacking website. The community is huge so as a consequence as of this morning 10:00 PDT I've gotten over 2000 hits! Kevin and Pedro's comments above are mild (and polite) versions of the comments to be found there. If non-computer readers are interested in, shall we say, criticisms, go to: https://developers.slashdot.org/story/16/05/07/187215/researcher-writes-a-machine-language-for-the-universe
As one who has taught computer science students for nearly 25 years I am not at all surprised at some of the blunt if not vitriolic comments found there. The most frequent complaint I hear from students is how they could not understand what they read in the textbook or the exam questions. I have conducted several reading workshops over the years to try and find out why these students (and it is the vast majority of them) were having trouble. It turns out to be simple and somewhat scary. They never learned how to read. For meaning that is.
It is actually even worse than that. A simple question: Why do so many software development projects still end up failing in some ways to meet 'user' requirements? Answer: IT people have difficulty understanding anything that isn't computation, and most of life isn't (in the strict sense - see below). It isn't just reading that is a problem, it is also just not comprehending non-technical language well enough.
The slashdot community is a great example of the kind of siloed disciplinary approach that has been a source of the failures of science and engineering to recognize the systemic nature of the world and the problems that causes. I suspect a large majority of them think that computer science (and related mathematics) is THE only legitimate domain of interest and that all questions eventually come down to computation. Ergo, most of the comments (such as above) are challenges framed in computer science and mathematics concepts.
My brief description of SL was taken (quickly) out of context, namely that I am talking about the language of building models of systems, not explaining, for example, quantum phenomenon (unless, for example, loop quantum gravity (LQG) turns out to be "right" because there is a lot of systemness about that theory!) Therefore, Pedro and Kevin, your questions are misguided in this context. For example, Gödel's incompleteness theorems still (always) apply in the case of pure mathematics, but so what? Most mathematicians and especially the applied version (of which this is an example) carry on somehow. No computer stopped working after Gödel proved his results. Similarly asking about differences in SL versus a UTM misses the point. Kevin, look at: Why Interaction is More Powerful than Algorithms by Peter Wegner for an expansion of the notion of computation beyond Turing. There are things systems do that are not technically Turing computable. Also, Wolfram's "A New Kind of Science" looks an awful lot like an attempt to explain the universe with cellular automata. But he doesn't seem to have made much progress beyond that original idea.
The idea of finding a language which is capable of describing and modeling complex dynamic systems in a way that is comprehensible to both ordinary human beings and computers should actually be acceptable to computer scientists (most of my colleagues don't have any problem with it at all). That I am proposing to develop a language that is fundamental to our minds and translatable into a formal structure should come as great news. Unfortunately the slashdot community (the most vocal ones anyway) read into the blog what they wanted and reacted with derision. Fortunately it won't matter insofar as my work is concerned.
George
Posted by: George Mobus | May 08, 2016 at 12:26 PM
The slashdot community can be very nasty and immature. It is a reflection on them - not your ideas.
Posted by: Cliff | May 08, 2016 at 12:55 PM
As a regular slashdot reader (and a computer scientist) I can assure you the majority of those comments are missing the point of this work.
I look forward to seeing what you put forward in July.
In all truth, a simple language (with good tools) that can express systems and provide a model to explore them, would put a lot of software people out of work :)
Posted by: Lucas Jordan | May 08, 2016 at 06:36 PM
I am impressed by your boldness and top-down method, which I believe is the better way to find out how things work. One of my favorite learning principles is, if you get stuck, it's usually because you are zoomed in too closely to see the all-important context within which a given problem is situated. Walking up and down the ladder of abstraction is good, but sometimes, you have to break new ground because the ladder itself doesn't go high enough to organize a pattern of particulars.
There, in abstractions of abstractions, the air is thin and ideas often vanish without great care and effort. But that's where all the great discoveries happen historically.
Don't worry about scarce resources being civilization's main negative feedback force. It isn't. That's more or less foolish propaganda aimed at destroying creative minds like yours. I would blame oligarchical usury as the main historical challenge facing mankind. Folks in that club spend a lot of time making resources scarce for the rest of us, so they can rule us or use us in one way or another. What they fear most is folks who can see that scarcity is a false construct, whose inversion is faster rates of unfolded human potential. So many existing resources are there because someone discovered them and found ways to use them. We have to generate and act on more powerful and profound ideas and discoveries in order to stay ahead of entropy or those who drag our collective feet as we try to advance.
Historically, the bottleneck to progress is not so much mother nature's reality or scarcity or resources, but bullshit accepted as fact by the people and their politicians.
Man expands his population potential with each new discovery, and with each upward leap to a higher density energy sources. This is even more true of cultural and political innovations which recognize this principle as the primary organizing element. So there's your first recursion. :-) This is why you can't model man like you model rabbits. Animals will never discover Fusion technology, no matter how long you run the simulation. There is a qualitative difference, or unique principle within human minds, related to an ability to engage in highly abstract thought. This unique difference relative to animals is difficult to pin-point, yet efficiently expressed by our rise to a population of billions over our nearest cousins in the animal kingdom, and also by the formation of complex political economies on a planetary scale. Soon we will establish colonies on other worlds. In computer science terms, its like animal minds are NP or NP Complete "level", and human minds are NP Hard "level".
Godel became famous for showing some hard limits to formal systems, and he hung out with Einstein at Princeton. I don't think you should "so what" away Godel, in the sense that the things you are trying to model probably are going to run smack dab into Godel's area of expertise.
It smells like you need to invent a new kind of computer, (likely non-digital or at least non-sequential) before you can model Mind or the Universe. But I do encourage you to investigate such things, and let us hear about it.
Like you say, the field is so subdivided into specialties, few are paying attention to the super-abstractions, and I am willing to bet many important discoveries will come from them. I think perhaps being limited to digital machines as they are, is not the best place to start necessarily, and this is yet another example of specialization potentially suffocating most of the fields computer science and system theory.
Just look at any field, say biology: Mankind is trying to figure out how DNA works using mostly digital methods. We still don't have a clue as to how the whole DNA system is organized, just relatively disconnected islands of knowledge with an expert in a lab coat standing on each one waving to the others.
If need is the mother of invention, then you are in the right place, but you might have to reinvent the computer before going after the things you want to model.
Finding an alternative computational device in itself would be an awesome adventure. Maybe that's how it has to proceed naturally anyway, define the blockade, and then find a way to breach it. I will be following your work, and I wish you luck.
Posted by: Doug Mayhew | May 08, 2016 at 09:42 PM
As a side note, I am very interested in interactive computing models, and I found the paper you linked to in your post above on that topic very insightful, esp in regard to interactive models ability to exhibit behaviors beyond the reach of algorithms. Almost sounds like an echo of the power of Plato's Dialectic, "2 way discussion seeking insight", having superior power to penetrate into the nature of things.
https://en.wikipedia.org/wiki/Dialectic
Short of inventing a new type of computer, perhaps you can experiment with the Erlang computer language to form the communications boiler-plate infrastructure to enable many thousands or even millions of isolated interactive instances across a cluster of machines very efficiently. It seems to fit the model of "interactions via messages only" quite well and can be scaled to enormous sizes in a computing cluster. I would like to see what folks in your league of understanding could do with such a beast. Erlang is relatively simple to set up, too. I don't know how it's design would limit your scope of investigations, but the article about interactive computing sounded very Erlang-ish to me. Maybe your language could be made to "ride on top" of Erlang for execution, assuming you need zillions of agents and supervisors, etc, without bogging down the resources too much. Just a thought.
Posted by: Doug Mayhew | May 08, 2016 at 11:00 PM
Does emphasising computational methods or machines confirm the 'homo callidus' versus 'homo sapiens'idea behind much of this blog's message?
Posted by: Contributor | May 09, 2016 at 12:05 AM
George Mobus wrote: "The slashdot community is a great example of the kind of siloed disciplinary approach that has been a source of the failures of science and engineering to recognize the systemic nature of the world and the problems that causes."
Slashdot has many vices, but definitely not that one.
Dismissing the /. community as a "computer "science" (why the quotes?) and IT silo" is far besides reality. Biologists, physicists, mathematicians, philosophers (I'm a philosopher of science and logician myself, active on Slashdot since 1998), historians, sociologists, geographers, linguists, etc... do participate actively in discussions. It is a site where inter-disciplinary dialogue has been going on for almost two decades.
Posted by: Koen Lefever | May 09, 2016 at 04:08 AM
Thanks to Cliff, Lucas, & Doug for supportive comments. Doug, I have your e-mail so will respond via that channel when time permits.
@Contributor,
Since I was corrected once on the spelling (I do not know Latin) it is Homo calidus, not callidus. And I really don't know what you are getting at.
Which brings me to two points re: these comments and those on /. 1) I will not respond to single sentence supposed zingers as these demonstrate considerable lack of critical thinking. You need to explain your criticism not just blast a one-liner thinking that is sufficient.
2) The vast majority of the comments on /. and those at the start of this thread indicate that readers (or should I say perusers) failed to grasp that this language is about modelling not about a deep description of everything. I used the term 'universal' only to emphasize that the language should be able to model any identifiable system (a thing that embodies the principles of systemness). Models are always necessarily reduced and compact descriptions of that which they model. As such they are not expected to explain absolutely everything in some reductionist view (such as a presumed GUT). Rather they are practical ways to anticipate the future through a partial explanation of how a system works. However, the more detail they can provide, the more we can say we 'understand' that which is modeled. My claim is (or will be soon) that SL provides a much more 'complete' way to describe systems. It is NOT a way to describe the universe down to the Planck scale.
@Koen,
Granted you may be right. I've somehow missed those conversations. Perhaps I should have been clearer in specifying that portion of the /. community responding to my blog (or rather to the article in /.) They are clearly of the IT persuasion and many have enough knowledge of CS to make specific barbs.
My question is: What major new ways of addressing the interesting interdisciplinary problems have emerged from said conversations? If any, perhaps I should join!
Posted by: George Mobus | May 09, 2016 at 09:40 AM
The universe itself is constrained by limits similar to the Godelean incompleteness, so a modeling language like SL would not suffer from having such limitation, if it achieves it by an accurate abstraction. For example, naturally occuring (?) event sequences can cause black holes to form: the universe can give rise to patterns that eventually violate its own integrity. A modelling framework that did not have this characteristic would not be accurately represent systems of this type.
Posted by: dk | May 10, 2016 at 04:55 AM
Hello, George.
Happy to help in anyway possible. I developed a basic class for PhD students in Management for Walden University, read your book (ok, some of it), and will be attending the SFI summer session on sustainable cities. My background is in software engineering and systems.
Posted by: Dave Gould | May 10, 2016 at 09:04 AM
@dk,
Yes, the issue of incompleteness or consistency is, in my opinion, irrelevant since no model ever comes close to representing the real thing. I assume this is what you mean by an "accurate abstraction," one that reflects reasonably the system behavior within the limits of accuracy and precision with which we can measure it in the first place. I have no idea about black holes - not my area of expertise.
------------------------------------
@Dave G.
My UW e-mail is [email protected]. Drop me a line and we can take this off-line.
George
Posted by: George Mobus | May 10, 2016 at 09:09 PM
I could be wrong, but I believe the term "negative feedback" is being used incorrectly, both in the article and in the comments.
https://en.wikipedia.org/wiki/Negative_feedback
Based on the context in which the term is being used here I believe "negative consequences" would be more appropriate. My assumption being that the intent is to evoke a catastrophic collapse of society after a period of unsustainable exponential growth.
Here is an easy way that I have found to remember the difference between positive or self-reinforcing feedbacks and negative or self-limiting feedbacks: Picture a water tank with a float valve (not unlike those found on the back of most flush toilets).
If the float valve is designed to restrict the flow of water then you have a classic negative feedback, the higher the water level the slower the tank is filled, the optimal level is gradually approached, and a very stable state is eventually reached when the tank is full and the water is shut off.
Now imagine a float valve that is just the opposite, designed to increase the water flow as the water level rises. This is a classic positive feedback which leads to exponential growth of the water level. The higher the water level the more the valve opens up, before long the water overflows the tank even as the now wide open valve continues to gush water. This is a highly unstable state and the resulting catastrophic flooding of the whole house is an extremely negative consequence of a positive feedback loop.
I dunno, maybe I'm missing something, but by just about any measure the catastrophic collapse of industrial civilization and resulting die-off of the population is an extremely negative consequence of the positive feedbacks dominant in human society for at least the last couple centuries now, and in no way resembles the stable, goal-seeking behavior of negative feedbacks.
Posted by: Jerry McManus | May 14, 2016 at 11:32 PM
Not to belabor the point, but upon further reflection it occurred to me that the other term that would be appropriate in this context is "negative exponential" otherwise known as "exponential decay"
https://en.wikipedia.org/wiki/Exponential_decay
In other words, roughly the same dynamic as exponential growth, except in reverse. A given stock is reduced by half at each interval instead of doubling at each interval.
In the case of human society, if we reversed the exponential growth of the last two centuries which had a doubling time of approx. 40 years, then we would get an exponential decay with a half life of approx. 40 years.
Starting about now, if projections such as the ones in the Limits to Growth report are correct, then in about 40 years we will have half the population, half the industrial output per capita, half the food per capita, and half the services per capita that we do now.
See, for example, the current situation in Venezuela, or the current situation just about anywhere in Middle East / North Africa, or..., well, you get the idea.
Posted by: Jerry McManus | May 15, 2016 at 08:59 AM
Now that we have that sorted, lets play a fun little thought experiment. Imagine for a moment just how different our world would be right now if negative feedbacks had indeed been dominant in human society for the last couple of centuries.
For example, we know that human population has been increasing exponentially due to the well known positive feedback inherent in population dynamics: The more people there are the more babies are born. What if instead, say around 1900 or so when the population was still roughly 1 billion, the world agreed that four billion people was the highest the population could ever go and still have some expectation of all future generations living in relative comfort and security.
Well, that would obviously require a negative feedback: The more people there are the fewer babies are born. Instead of the inherently unsustainable, potentially chaotic, and almost certainly catastrophic exponential growth of the last century we would have instead seen the population gently ease up to the goal of 4 billion people on our lovely little planet and then level off. Not because people are not having any babies at all, but because people know and understand that every year there is such a thing as enough babies.
Here's another example, we know that pollution in the atmosphere is increasing exponentially due not only to our growing population, but also because of the growing standards of living that have been bought with a one time trust fund, deposited millions of years ago, of fossil sunlight stored in the Earth's crust.
What if instead, say around 1900 or so when the steam engine was well established and internal combustion was just starting to take off, the world agreed that 400ppm of carbon dioxide (or its equivalent?) in the atmosphere was the highest it could ever go and still have some expectation of all future generations living in relative comfort and security.
Well, that would obviously require a negative feedback: The more carbon emitted by human activity resident in the atmosphere then the less fossil fuel that could be burned. Instead of the inherently unsustainable, potentially chaotic, and almost certainly catastrophic climate change that we are seeing today we would have instead seen CO2 levels gently ease up to the goal of 400ppm on our magical little planet and then level off. Not because people are shivering in the cold and dark, but because people know and understand that there is such a thing as enough "blankets", and they got busy a long time ago figuring out how to live comfortably without the benefit of burning copious quantities of fossil sunlight.
It's startling to think that such a small change in the dynamics of human society, from positive feedbacks to negative feedbacks, could have such a profound effect on not only our world but also on the world of countless future generations to come. I believe Buckminster Fuller called this "Trimtab" after the steering mechanism on large ocean going vessels:
https://www.brainpickings.org/2015/08/21/buckminster-fuller-trim-tab/
And, let's not forget, Donella Meadows brilliant and life-altering essay on leverage points:
http://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/
Oh, and BTW, someone do me a favor and let Ugo Bardi know that he also consistently misuses (I would even say abuses) the term "negative feedback". Thanks!
Posted by: Jerry McManus | May 15, 2016 at 10:03 AM
@Jerry Mc.
Thank you for your thoughtful post.
For clarification the model I am talking about is one in which initially positive feedback loops dominate pushing growth (e.g. population growth due to interactions between birth rates and population - exponential growth). But as those positive feedbacks drive the variable(s) upward, the mechanisms underlying them "feed" the rise and ultimate dominance of negative loops, e.g. deaths due to overpopulation.
So there is no real misapplication of the terms here. I've been writing about this for so long I assume readers will recognize my shorthand references. The general model of positive (growth) followed by the rise of negative (peaking and possible degrowth) is generally understood. I cover it in my Principles of Systems Science book. It is also the basis of Ugo's work.
George
Posted by: George Mobus | May 15, 2016 at 12:53 PM
Thanks George,
I won't bother splitting hairs, you say "degrowth", I say "exponential decay", obviously they both mean the same thing.
I had hoped you (and Ugo) would be a little more rigorous, especially considering the importance of the subject, but C'est la vie, as they say.
Posted by: Jerry McManus | May 15, 2016 at 02:56 PM