Some Key Concepts in Systems Theory & Cybernetics
Below is, a brief intro to: Systems Theory!
(And Evolution, which occurs in: Ecosystems.)
If you want to “go deep” into Systems Theory, maybe read this great book:
VON BERTALANFFY, L. 1968. General System Theory: Foundations, Development, Applications, New York, George Braziller, Inc.
But meantime, as a quick intro:
There is a great set of Youtube videos, here:
And this is a cool little video on Complexity:
Also, I made a video of an Excel program I made, to explain Systems Theory:
(And sorry if you knew all that, already…)
And now – some eye-candy. (An oldie, but a goodie!)
Karl Sims – Evolved Virtual Creatures, Evolution Simulation, 1994
And another one (a genetic algorithm): Boxcar 2D
So, Evolution (biological, cultural, and biocultural) operates via Systems Theory, or Cybernetics.
Here’s a cool Youtube video, on another Evolution Simulator:
Evolution Simulator (Part 1/4) – 5 mins – (By: Carykh, 2015 – & with 4M+ views!)
And below is a simple systems diagram of Biological Evolution – as a pair of self-coupled systems: (though – it is really, all just the one super-system).
Self-coupled systems are where the output of each system becomes the input of the other.
For that reason, here is a:
Very Brief History of: Systems Theory & Cybernetics.
And – if you want a very detailed history of Systems Theory, may I also suggest the chapter: Systems ideology in human and social sciences – History and Philosophy of systems and model thinking, information theory and cybernetics – Jouko Sepännen (pp. 180-302) – in, this (excellent) book:
Here is part of the Abstract of that chapter mentioned above:
`The article summarizes major developments in the history of systems thinking, including Bertalanffy and general systems theory (1940), von Neumann and stored program computers (1945), Nyquist (1924), Schroedinger (What is life?, 1944), Shannon’s information theory (1948), and Weiner’s cybernetics (1949), as well as over one hundred other theorists since who have contributed meaningfully to these domains.’
Okay, so – that Abstract was indeed a very brief summary of systems theory and cybernetics.
Here are a few more salient quotes, from that excellent summary or Literature Review of systems theory and cybernetics by Sepännen (1998)…
`The development of systems and model thinking, information and communication theory, cybernetics and computer science offered the necessary conceptual tools and methods to study in precise terms complex systems and phenomena not only in natural sciences and engineering but also in life, human and social sciences including history and culture.’
This one below is a really important point: namely, (as Aristotle noted) – the whole is more than the sum of the parts…
`Systems science and engineering consider both natural and artificial of cultural objects and systems, i.e., man-made things such as the products of art, science and technology… The analytic and conceptual methodologies are especially important in the study of highly complex systems such as living beings, neural and mental functions, language and communication, social and economic systems, societal and political affairs, issues of war and peace, culture and ecology, etc.
Systems analysis is done by breaking down a given object of study or problem into its constituents, parts and factors and by analysing their interrelations and functions as parts of the whole, and in the case of living beings, their actions and interactions and their determinants.
In doing so it is understood that the functions of parts in their systemic context essentially depend on their interrelations, as does the functioning of the system as a whole. This is because all parts are constrained laterally by at least some other parts and from above by the total architecture and organization of the system.
This is important since in their systemic context the parts function in a way different from how they would in isolation from the system, if at all. Consider, e.g. a wooden leg. The principle of context dependence is one of the reasons why systems sciences can correct many failures and mistakes of classical and modern science, especially in their attempts to understand and explain complex phenomena and processes. The cardinal mistake is hidden in the assumption that the whole is equal to the sum of the parts. The principle of “ceteris paribus” or “divide and everything remains unchanged” symptomizes the failure to take properly into account all relevant factors, interrelations and their causal effects and chains which are often complex.
From this negligence follows the impossibility to understand what is called system-effects, i.e. emergent system qualities which are a result of the context-dependence of parts and the architecture of the system as a whole. The whole imposes constraints on the modes of functioning of the parts and forces them to behave differently from how they would otherwise.
If the system is analyzed only in terms of its parts, as assumed in atomism and more generally in reductionism, the system-effects are lost without trace.
Most strikingly, this mistake manifests itself in the failure of classical and modern sciences to explain phenomena of life and mind, which has left room for mystification, speculation, and belief as substitutes for understanding.’
And, this next excerpt below from Sepännen (1998) is really the most important point, namely that:
Evolution is explained by systems theory (!)
`Galileo Galilei (1564-1642) – world system – The Italian astronomer and physicist Galileo Galilei (1564-1642) used the word system in the title of his work “Dialog of the Two Chief World Systems, Ptolemaic and Copernican” (“Systema Cosmicum” 1632), in which he gave his support to the Copernican heliocentric world system. In fear of the reaction of the church authorities he dressed the treatise in the disguise of a dialog between two fictitious figures, `Sagredo’ and `Salviati’.
Galileo assumed that the church would be more sympathetic to a discursive elicitation of the novel idea but was mistaken. The next year he was subjected to the inquisition and was shown the torture chamber and forced to recant, and subsequently sentenced to house arrest for life.
The ban remained in force after the death of Galileo and was lifted only 350 years later, i.e. in 1992.
The use of the world system and the ban of the church are noteworthy also in the sense that they anticipated by about 350 years the refutation by science of another central dogma held by the church until today, namely that of the origin of life on earth and the force of life assumed to be of supernatural or divine origin.
This force of life is believed to underlie all living organisms and to be unexplicable in terms of science and natural laws but also received its explanation in terms of systems theory, more specifically the theory of self-reproducing systems, 350 years later.’
So – as promised in the title of this post – here are some key concepts in Systems Theory.
But – just to bring this closer to home – I want to preface all this by pointing out something about Evolutionary Psychology…
Here is a diagram from the excellent book Evolutionary Psychology (Buss 2012).
Namely the above is how Psychology works, in lifeforms.
Instincts and emotions are: algorithms, inside biological systems.
And now, a few more explanatory diagrams are in order, mainly as diagrams are often way better than words.
The following diagrams are from:
At the simplest level, a system can be viewed as a `black box’ – with inputs and outputs.
Note the similarity of the (above) to this earlier diagram (below) – and, this is actually no coincidence:
At any rate, inside that `black box’ (or `system’) are – some components of the system:
Note also, each component is also a system, with INPUTS and OUTPUTS (which may be: information, energy, matter).
The Receptor (see the diagram above) takes in information from the environment, while the Alimentator takes in energy. The Correlator stores information, the Accumulator stores energy. The Homeostat monitors inputs, outputs, and the functioning of the system – and decides what to do next (think of a thermostat, in an oven). The Effector gives the output or reaction on the environment.
This whole process in systems, results in positive – or negative – feedback loops. Or perhaps, homeostasis, where the system input/s, equal the output/s…
(See the video below, if you didn’t see it: above!)
So, that’s kinda: Systems, in a nutshell.
But – as seen above, you can also have self-coupled systems, such as the `Field’ and `Domain’ in any part of culture (movies, novels, songs, videogames, and in science such as Physics, Chemistry, Biology, Maths, etc), but see this post for more on all that (with regard to: Movies).
So – here are some key concepts in systems theory / cybernetics.
Typically, systems have 3 components,
(2) Information, and
(3) Matter (`matter’ can include Materials, or Objects, or Individuals, or Persons). As an organism, You are a system. Composed of 20 main subsystems, and those are composed of many more subsystems.
Below is an example of a system (a cell), from the excellent book The Systems View of Life: A Unifying Vision (Capra & Luisi 2014)
i.e This book is excellent: The Systems View of Life: A Unifying Vision (Capra & Luisi 2014)
I have posted about it, here. If you want more detail.
Another excellent book is:
Here is a diagram of a system, from it:
And a hydrogen atom is one of the simplest systems:
So in systems thinking terms, from the atom, up to the universe, (and, back!) it’s: systems – all the way up – and down. (As opposed to: `turtles’.)
And of course – there’s probably systems at the sub-atomic level and beyond, as well.
Systems Theory – Some definitions (from: various places)
Open vs. Closed systems:
Open = where there is an exchange of energy, information or materials between a system and its environment
Closed = where the system is isolated from the environment, and there is no exchange (of energy, information & materials)
A holon-parton (Koestler 1967, Feynman 1972, and Velikovsky 2014) is something (for example, a system) that is a part and also a whole at the same time. (the term `holon-parton’ is a synthesis of the term “holon” from Koestler 1967 The Ghost In The Machine, and “parton” from Feynman 1972, Photon-hadron Interactions). e.g. an atom is a whole atom – but is a part of a molecule. A cell is a whole cell – but may be part of an organ, etc.
Mobus & Kalton (2014) also talk about parts and wholes, in systems:
`System Rules’, `System Code’, or `Canon’
Rules = a set of `IF > THEN’ rules (and, processes), that apply at that level of the system
Koestler (1964, 1967, 1978 ) called this, the system code or canon.
Here is what Koestler says:
(NB – I have added [-parton] into the two quotes below – JT Velikovsky)
`The set of fixed rules which govern a holon[-parton]’s structure or function we shall call its code or canon.
However, let us note at once that while the canon imposes constraints [`Constraints’ is a rather unhappy scientific term – reminiscent of the strait-jacket which refers to the rules which govern organized activity] and controls on the holon[-parton]’s activities, it does not exhaust its degrees of freedom, but leaves room for more or less flexible strategies, guided by the contingencies of the environment.’
Koestler also writes:
`Every level in a hierarchy of any type is governed by a set of fixed, invariant rules, which account for the coherence, stability and the specific structure and function of its constituent holon[-parton]s.
Thus in the language hierarchy we found on successive levels the rules which govern the activities of the vocal chords, the laws of grammar and above them the whole semantic hierarchy concerned with meaning.
The codes which govern the behaviour of social holon[-parton]s, and lend them coherence, are written and unwritten laws, traditions, belief systems, fashions.
The development of the embryo is governed by the `genetic code’.
Turning to instinctive activities, the web which the spider weaves, the next which the blue tit builds, and the courting ceremony of the greylag goose all conform to fixed, species-specific patterns, produced according to certain `rules of the game’. In symbolic operations, the holon[-parton]s are rule-governed cognitive structures variously called `frames of reference’, `associative contexts’, `universes of discourse’, `algorithms’, etc, each with its own specific `grammar’ or canon.
We thus arrive at a tentative definition: the term holon[-parton] may be applied to any structural or functional sub-system in a biological, social or cognitive hierarchy, which manifests rule-governed behaviour and/or structural Gestalt-constancy
[The `or’ is necessary to include configurations in symbolic hierarchies – which do not manifest `behaviour’ in the usual sense].
Thus organelles and homologous organs are evolutionary holon[-parton]s; morphogenetic fields are ontogenetic holons; the ethologist’s `fixed action-patterns’, and the subroutines of acquired skills are behavioural holon[-parton]s; phonemes, morphemes, words, phrases are linguistic holon[-parton]s; individuals, families, tribes, nations are social holon[-parton]s.
[[NB] – Orgs as defined by Gerard (1957), represent a sub-category of holon[-parton]s confined to material systems.]’
At any rate, back to more key concepts in systems theory:
Boundary = the outer limits of the system (think of: a cell wall)
Entropy & Negentropy:
Entropy = The Second law of Thermodynamics (things moving towards decay, chaos, or disorder)
Negentropy = in a way, the opposite of the 2nd Law, (namely, movement towards increased order, and growth and development of a system)
Linear causality = simply, linear cause and effect. (“If you cut us, do we not bleed?” etc.)
Nonlinear causality = some, or all parts, affect some – or all other parts – of the system, more or less simultaneously
Homeostasis – as a term has two main uses:
(a) as an attained state – where the system does not change – even though the environmental conditions may change
(b) as a process: where the homeostat in a system acts to minimize changes in the system
Morphostasis & Morphogenesis:
Morphostasis = stability in the morphology (structure and state of the system)
Morphogenesis = change in the morphology (structure and state of the system)
Feedback: can be Positive or Negative…
Positive feedback – when inputs are high, outputs are high (or, amplification. e.g.: Think of Jimi Hendrix’ guitar-feedback squeal, that gets louder and louder until the actual speaker blows up 🙂
Negative feedback – when inputs are too high, and so the inputs are reduced, and so are the outputs (acting like: a brake)
For example, a governor in a steam-engine (or in any kind of engine) works this way: when there is too much flow, the valve closes down to let less flow through, and vice versa, when there is too little, a valve is made to open, to allow more flow through.
Where the new `whole’ possesses properties that differ from the `parts’ (or, sum of the parts)
A system capable of both maintaining, and reproducing itself. (e.g. such as DNA & cells, in biology. Or, the domain/person/field interaction system of say Movies, in culture.)
What are some key concepts in Evolution? (i.e., as per Charles Darwin and the Modern Synthesis…)
`The evolutionary process requires variation, differential survival and reproductive success, and inheritance.’
So – Evolution (the evolutionary algorithm of selection, variation and transmission-with-heredity) works via systems theory or cybernetics.
The evolutionary algorithm (selection, variation, transmission) works on various levels of all the systems…
Below are some levels, in Biology:
(note: each level is a holon-parton)… and operates via the 3 laws of holarchies.
I suggest that selection in Evolution takes place on all the levels from organic molecules, up to communities – and, not just at the level of DNA (molecules) – or of gene complexes (macromolecules). i.e. Multilevel Selection Theory (or, MLS for short).
Below (once again) is Biological Evolution as a system:
(I have just kept the units as `Organisms and Genes’ to keep the diagram simple.)
And here is Cultural Evolution, as a system:
Memes are: ideas, processes, products in culture (see: Csikszentmihalyi, Creativity, 1996).
Selection can occur as natural, sexual, and artificial (see Chapter 1 in The Origin of Species, 1859), and, unconscious. There is also: intentional selection (see Novacene, Lovelock 2019)
And – here are some levels (or, holon-partons) in Culture.
(Specifically in this instance, in the domain of Movies – or, Film).
Biology and Culture evolve together… So, there is biocultural evolution.
And – Why am I telling you all this..?
Because – it explains why some movies become canon, and others do not. (See also: Willie van Peer 1997 on `Two laws of literary canon ‘ – and these are laws which also apply to movies – and to science, and in fact all domains in culture.)
This also explains Why the top 20 RoI Movies became the top 20 RoI Movies.
And, for more, see this post on the Holarchy of StoryAlity Theory…
And also, this (short) post, on A hierarchy of memes, in Practical Memetics. (Actually I see that website is currently down as on 02019…)
This all also explains, Why the DPFi (Domain, Person, Field interaction) systems model of creativity (Csikszentmihalyi 1988-2014) is so important, for understanding how creativity works in bioculture… Namely: creativity in both the Arts and the Sciences, and Engineering, in fact all domains in culture.
i.e.: Natural, Artificial, Sexual, and Unconscious selection in the Sciences and the Arts/Humanities, and even in Languages.
I therefore suggest that: the systems model of creativity (Csiksentmihalyi 1988-2014) is the same mechanism as evolutionary epistemology (a la Sir Karl Popper 1963, 1999, DT Campbell 1960, 1965, 1974, DK Simonton 2010, Gontier 2006, 2014, etc).
In fact, in academia, it is evolutionary epistemology.
It is: How We Know, What We Know.
And, for the 3 laws of holarchies, see also: The Holon-Parton Structure of the Meme, the unit of culture.
And for a good summary of `Applied Evolutionary Epistemology’ by Dr Nathalie Gontier, see these 2 x videos:
Also here is a good explanation of Complex Systems – from R. A. Meyers (Ed.), 2009. Encyclopedia of Complexity and Systems Science (p. v). Larkspur, CA: Springer.
`Complex systems are systems that comprise many interacting parts with the ability to generate a new quality of collective behavior through self-organization, e. g. the spontaneous formation of temporal, spatial or functional structures. They are therefore adaptive as they evolve and may contain self-driving feedback loops. Thus, complex systems are much more than a sum of their parts. Complex systems are often characterized as having extreme sensitivity to initial conditions as well as emergent behavior that are not readily predictable or even completely deterministic. The conclusion is that a reductionist (bottom-up) approach is often an incomplete description of a phenomenon. This recognition, that the collective behavior of the whole system cannot be simply inferred from the understanding of the behavior of the individual components, has led to many new concepts and sophisticated mathematical and modeling tools for application to many scientific, engineering, and societal issues that can be adequately described only in terms of complexity and complex systems.
Examples of Grand Scientific Challenges which can be approached through complexity and systems science include: the structure, history and future of the universe; the biological basis of consciousness; the true complexity of the genetic makeup and molecular functioning of humans (genetics and epigenetics) and other life forms; human longevity limits; unification of the laws of physics; the dynamics and extent of climate change and the effects of climate change; extending the boundaries of and understanding the theoretical limits of computing; sustainability of life on the earth; workings of the interior of the earth; predictability, dynamics and extent of earthquakes, tsunamis, and other natural disasters; dynamics of turbulent flows and the motion of granular materials; the structure of atoms as expressed in the Standard Model and the formulation of the Standard Model and gravity into a Unified Theory; the structure of water; control of global infectious diseases and also evolution and quantification of (ultimately) human cooperative behavior in politics, economics, business systems and social interactions. In fact, most of these issues have identified nonlinearities and are beginning to be addressed with nonlinear techniques – e. g. human longevity limits; the Standard Model; climate change, earthquake prediction, workings of the earth’s interior, natural disaster prediction, etc.’ (Meyers 2009, p. v)
And see OneZoom, an interactive evolutionary tree of life.
And noting: The Blind Watchmaker (Dawkins 2015)
See, why Evolution IS a blind watchmaker:
See also the ISSS wiki
And here is the ISSS logo: (click for an explanation)
And, see also this book chapter:
Hey – and here it comes again: (it was reprinted in other works, a few times)
StoryAlity #152 – The Holon/Parton Structure of the Meme, or The Unit of Culture. In the book Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction (2019)
One more cool video:
– Comments always welcome.
High-RoI Story/Screenplay/Movie and Transmedia, Researcher & Writer
(& Evolutionary Systems Theorist)
The above is (mostly) an adapted excerpt, from my doctoral thesis: “Communication, Creativity and Consilience in Cinema”. It is presented here for the benefit of fellow screenwriting, filmmaking and creativity researchers. For more, see https://aftrs.academia.edu/JTVelikovsky
JT Velikovsky is also a produced feature film screenwriter and million-selling transmedia writer-director-producer. He has been a professional story analyst for major film studios, film funding organizations, and for the national writer’s guild. For more see: http://on-writering.blogspot.com/
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