Sunday, May 8, 2016

Mentalese - Machine language for the universe



"There is a fundamental language of systems that provides a way to describe both structures and functions that is universal across any kind of system... 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 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...!

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)... 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.


Systems Science Ascending

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.


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 understand 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!


Bertalanffy, L. von, (1969). General System Theory, George Braziller, New York.