Wikipedia:WikiProject Systems – Wikipedia

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Source: Wikipedia:WikiProject Systems – Wikipedia


Wikipedia:WikiProject Systems

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WikiProject Systems

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WikiProject Systems is a WikiProject which aims to improve Wikipedia’s coverage of theory and practice of Systems and Systems science in Science and Society. As of April 2007, it seems that the articles around Systemsand the Systems sciences were not especially well organized on Wikipedia by comparison with most other specialized scientific disciplines.

This project is an initiative to organize, thematize, personalize and actualize these information on Wikipedia. This project wants to offer a place to work together to initiate and coordinate these efforts.

You can help this project evolve with ideas, comments and suggestion. (Please leave a message).

SCiO Open Meeting and AGM – Summer 2019, London, Mon 8 Jul 2019 09:30-17:30 – four great speakers: Systems and Complexity in Organisation

Source: SCiO Open Meeting and AGM – Summer 2019, London (All Welcome) Tickets, Mon 8 Jul 2019 at 09:30 | Eventbrite

JUL 08

SCiO Open Meeting and AGM – Summer 2019, London (All Welcome)

by SCiO – Systems and Complexity in Organisation


Actions and Detail Panel


Event Information


An open meeting where a series of presentations of general interest regarding systems practice will be given – this will include ‘craft’ and active sessions, as well as introductions to theory.

09:30 – an introduction to the viable system model. Main presentations start at 10:00.

Please note that the AGM will follow on from the open day (for members)

Session 1 (Kerry Turner) – Causal Loop Diagrams: A key tool for Systems Thinking & Practice

Causal Loop Diagrams (CLDs) are a useful tool in the kitbag of any systems practiotioner. They are used to make our mental models explicit so they can be shared, challenged and understood. CLDs enable us to capture the parts, connections and feedback in a system. They can be used to build consensus, agree definitions, identify leverage points and explore consequences of potential interventions. They enable us to share our ideas and communicate our understanding of a system to others clearly and powerfully.

The workshop will introduce the concept of a CLD and explore how the diagrams can be developed and used both individually and in teams. There will be the opportunity to practice developing causal loop diagrams from documents and from observed systems. Participants are encouraged to bring a problem/idea they would like to explore with this approach.

Kerry Turner is passionate about understanding and improving systems. She acquired her skills in systems thinking during her career as a management consultant. She has applied it to a wide range of business problems for organisations around the world. For the last decade she has applied systems thinking to every aspect of her life including horsemanship, swimming, relationships, home economics and health. She has also worked with small organisations who share her values.

Session 2 (Alan Arnett) – Leadership, Complexity and Sensemaking (provisional title)

Awaiting Abstract

Session 3 (Rod Willis) – Dimensions of Strategic Management, Through Time

Many organisations are preoccupied with Strategic Planning and the use of Key Performance Indicators (KPIs) with a clear desire to ‘get to where they want to go’. On this journey, many fall foul of ‘The Tyranny of Meaningless Metrics’ (songs of the Sirens) Worst still, if they do reach the destination, they may discover where they wanted to get to wasn’t the destination actually required to grow or survive after all! (“Alice: Would you tell me, please, which way I ought to go from here? The Cheshire Cat: That depends a good deal on where you want to get to. Alice: I don’t much care where. The Cheshire Cat: Then it doesn’t much matter which way you go. Alice: …So long as I get somewhere. The Cheshire Cat: Oh, you’re sure to do that, if only you walk long enough.” ― Lewis Carroll, Alice in Wonderland)

We have been busy over the years in many Business Schools (BS) teaching/supporting passionate learners how to ‘do strategy’, unfortunately, many seem to have missed some key parameters in the process. To test these assertions, feel free to research Ansoff’s work before you come to this SCiO event. Try to identify what he is known for and let’s discuss and explore together. Even if you think you have a handle on Igor Ansoff’s approach on Strategic Management, we will add another dimension that (as far as I have been able to identify) is not part of his work. When Igor Ansoff’s dimensions of Strategic Management are combined with the Organisational ECO-Cycle (by David Hurst) we start to see something new emerge for Strategy.

Linking Igor Ansoff and David Hurst’s work has the ability to create a dynamic approach to Strategic Management. We hear many calls for ‘Agile Business Strategies’ yet we are often using approaches that come from a school of thought that is NOT about agile or complex adaptive systems. We live in a world of paradoxes and for strategy, we would seem to be approaching the choice of the blue pill or the red pill. If you can sense Strategic Turbulence all around you, what pill would you decide to take?

To close, we will share the highlights of a Case Study that combined Ansoff and Hurst’s work, creating an adaptive, growing solution in China, please join us.

Session 4 Details tba

Date And Time

Mon, 8 July 2019, 09:30 – 17:00 BST


BT Centre, 81 Newgate Street, London, EC1A 7AJ

OrganiserSCiO – Systems And Complexity In Organisation

Organiser of SCiO Open Meeting and AGM – Summer 2019, London (All Welcome)

SCiO is a group for systems practitioners and is based in the UK, but has members internationally.

Two of the features that distinguish SCiO from other systems groups are that it is focused primarily on systems practice and practitioners rather than on pure theory and that it is focused on systems practice applied to issues of organisation.

It has three main objectives:
Developing practice in applying systems ideas to a range of organisational issues.
Disseminating the use of systems approaches in dealing with organisational issues.
Supporting practitioners in their professional practice.
SCiO is a social enterprise and a not for profit organisation which is owned by its members.

Provenance and Purpose.
Created initally by a network of practitioners in the North of England, SCiO acts as an extra channel for disseminating to others their experience of practical applications, education and research in complex problem solving. The name stands for ‘Systems and Complexity in Organisation’ but can also be thought of as short for the ‘Science of Organisation’.

Over the last sixty years the new disciplines of ‘Systems Thinking’ and ‘Managerial Cybernetics’ have emerged. The new thinking started from the consideration of complex problems faced during the Second World War; then later in the 1970’s the same patterns of thinking emerged with the new awareness of the complexity of ecological problems. The ideas developed and spread into other areas of science and in particular into management. In the last thirty years new insights and understanding have developed in the way to approach apparently intractable problems in many areas.

At this time the terms ‘whole systems approach’ and ‘systems thinking’ seem to be appearing more frequently in published policy documents and guidance on best practice in the United Kingdom and elsewhere, such as in the UK National Health Service; in documents on public health, sustainable communities, in education, in considerations of the environment, and in corporate governance.

The members of SCiO believe that the use of systems thinking and managerial cybernetics can have major impacts on the well-being of our communities, and our business and social organisations.

Improvisation Blog: Bach as an anticipatory fractal – and thoughts on computer visualisation – Mark Johnson


Source: Improvisation Blog: Bach as an anticipatory fractal – and thoughts on computer visualisation


Wednesday, 8 May 2019

Bach as an anticipatory fractal – and thoughts on computer visualisation

I’ve got to check that I’ve got this right, but it seems that an algorithmic analysis I’ve written of a Bach 3-part invention reveals a fractal. It’s based on a table of entropies for different basic variables (pitch, rhythm, intervals, etc). An increase in entropy is a value for a variable “x”, where a decrease in entropy is a value for “not-x”. Taking the variables as A, B, C, D, etc, there is also the values for the combined entropies of AB (and not-AB), AC, BC, etc. And also for ABC, ABD, BCD, and so on.

The raw table looks a bit like this:

But plotting this looks something like this:

What a fascinating thing that is! It should be read from left to right as an index of increasing complexity of the variables (i.e. more combined variables), with those at the far left the simplest basic variables. From top to bottom is the progress in time of the music.
My theory is that music continually creates an anticipatory fractal, whose coherence emerges over time. The fractal is a selection mechanism for how the music should continue. As the selection mechanism comes into focus, so the music eventually selects that it should stop – that it has attained a coherence within itself.

Need to think more. But the power of the computer to visualise things like this is simply amazing. What does it do to my own anticipatory fractal? Well, I guess it is supporting my process of defining my own selection mechanism for a theory!

The Human Use of Human Beings: Cybernetics Pioneer Norbert Wiener on Communication, Control, and the Morality of Our Machines – Brain Pickings – Maria Popova


via Steve Whitla, @swhitla on Twitter

I can’t believe I haven’t seen or posted this here before!


Source: The Human Use of Human Beings: Cybernetics Pioneer Norbert Wiener on Communication, Control, and the Morality of Our Machines – Brain Pickings

The Human Use of Human Beings: Cybernetics Pioneer Norbert Wiener on Communication, Control, and the Morality of Our Machines

“We are not stuff that abides, but patterns that perpetuate themselves. A pattern is a message.”

The Human Use of Human Beings: Cybernetics Pioneer Norbert Wiener on Communication, Control, and the Morality of Our Machines

“Information will never replace illumination,” Susan Sontag asserted in considering the conscience of words“Words are events, they do things, change things,”Ursula K. Le Guin wrote in the same era in her exquisite meditation on the magic of real human communication“They transform both speaker and hearer; they feed energy back and forth and amplify it. They feed understanding or emotion back and forth and amplify it.” But what happens when words are stripped of their humanity, fed into unfeeling machines, and used as currencies of information that no longer illuminates?

Half a century before the golden age of algorithms and two decades before the birth of the Internet, the mathematician and philosopher Norbert Wiener (November 26, 1894–March 18, 1964) tried to protect us from that then-hypothetical scenario in his immensely insightful and prescient 1950 book The Human Use of Human Beings: Cybernetics and Society (public library) — a book Wiener described as concerned with “the limits of communication within and among individuals,” which went on to influence generations of thinkers, creators, and entrepreneurs as wide-ranging as beloved author Kurt Vonnegut, anthropologist Mary Catherine Bateson, and virtual reality pioneer Jaron Lanier.

Norbert Wiener

Wiener had coined the word cybernetics two years earlier, drawing on the Greek word for “steersman” — kubernētēs, from which the word “governor” is also derived — to describe “the scientific study of control and communication in the animal and the machine,” pioneering a new way of thinking about causal chains and how the feedback loop taking place within a system changes the system itself. (Today’s social media ecosystem is a superficial but highly illustrative example of this.)

In a complement to Hannah Arendt’s contemporaneous insight into how tyrants use isolation as a weapon of oppression and manipulation, Wiener explains why, under this model of information systems, communication and control are inexorably linked:

Information is a name for the content of what is exchanged with the outer world as we adjust to it, and make our adjustment felt upon it. The process of receiving and of using information is the process of our adjusting to the contingencies of the outer environment, and of our living effectively within that environment. The needs and the complexity of modern life make greater demands on this process of information than ever before, and our press, our museums, our scientific laboratories, our universities, our libraries and textbooks, are obliged to meet the needs of this process or fail in their purpose. To live effectively is to live with adequate information. Thus, communication and control belong to the essence of man’s inner life, even as they belong to his life in society.

Art by Ralph Steadman from a rare edition of Alice’s Adventures in Wonderland

A pillar of Wiener’s insight is the second law of thermodynamics and its central premise that entropy — the growing tendency toward disorder, chaos, and unpredictability — increases over time in any closed system.

Continues in source: The Human Use of Human Beings: Cybernetics Pioneer Norbert Wiener on Communication, Control, and the Morality of Our Machines – Brain Pickings

Tables of Soyga: the first cellular automaton? Anders Sandberg


April 17, 2014

Tables of Soyga: the first cellular automaton?

Was the first cellular automaton intended to do encryption and/or summon angels?

Normally the history of cellular automata begins with von Neumann’s classical study of self-replicating systems in the 1950s. While clearly influenced by Turing’s discrete automata, this was the first paper using a grid of cells in different states where each cell changed each clock tick according to a fixed, universal rule. Even Stephen Wolfram seems to think so: “Despite their very simple construction, nothing like general cellular automata appear to have been considered before about the 1950s.”

But maybe von Neumann was scooped by a few centuries.

Liber Soyga

Liber Soyga is a early modern book. It was owned by Dr John Dee, the Elizabethan scholar, magician and government adviser (check out Leslie A. Rutledge’s entertaining “John Dee: Consultant to Queen Elizabeth I“). He mentioned it in a few locations, but the identity of the work was lost for many years. In 1994 Deborah Harkness managed some nice scholarly sleuthing and found copies at the Oxford Bodleian Library and the British Library (filed under an alternative, non-obvious title).The work contains various pieces of astrology, cabalism, lists of supernatural names and summoning formulas. At the end there are 36 36×36 squares of apparently random letters, labelled by the constellations (twice), planets, elements and the word “magistri”.

An example is the first square of Aries:


This is similar to many other early modern magical books: John Dee is himself famous among hermeticists for his “Enochian squares“. Exactly how the Soyga squares were to be used is apparently unclear; Dee himself tried asking the archangel Uriel if the book was any good but got told that it was above his clearance.

The next step of the story is that the Soyga squares were reverse engineered by Jim Reeds. He found the rule to generate them: a keyword is used to seed the left edge (the word is written downwards, followed by itself in reverse, in the right order, reverse, and so on – in the above example the word is “nisram”). Each cell depends on the cell to the left and above itself: the letter to the left determines how many steps forward in the alphabet to move from the letter above.

L(i,j)= [L(i-1,j)+f(L(i,j-1))] mod 23, if L(i,j) is the letter in row i and column j. f() is an apparently arbitrary list of values. The top row is generated by L(1,j)= [L(1,j-1)+f(L(1,j-1))] mod 23, taking the left letter as the top letter too.

Plotting the result as color rather than letters gives the following pattern:

The Soyga Automaton

The cool thing is that this is essentially a 23 state 1D cellular automaton, where the time is running from the left to the right.The dynamics is strongly chaotic (class 3), producing an apparently random distribution of letters. There is a slight bias because of the top boundary condition: there are two attractor states along the top, one consisting of repetitions of ”dizb” and one of repetitions of “oy”. The oy attractor tends to produce a triangle of repeated “oy”. But even if the keyword is just “a” the pattern is chaotic (as demonstrated in the image at the top).

Using this kind of rule generically produces pseudorandom behavior: nearly any transition table f() will work. Some have triangle slices along the top of repeating patterns, but most seem to approach an even distribution of letter frequency.

That the dynamics is generically random for many-state automata is well known.

It is less obvious that it would also generate an even distribution of states. However, if one looks at the transition table T(i,j) denoting what letter one gets from having letters i and j to the left and above the uniformity becomes more obvious: each row is a circular shift of the alphabet, so the total number of instances of each resulting letter is the same. A random transition matrix would have produced some letters more often than others. There is still the issue that some matrices like these might have subsets of letters permuted to each other (imagine one where even letters are turned into even letters and odd ones into odd), so some keywords would induce an uneven distribution. But presumably ergodicity is generic for this class of automata.

What was it good for?

Why did the originator invent this rule? Reeds compares it to other letter tables from the same era, and it is pretty clear that Soyga is indeed far more advanced than the other tables. Most have very simple repeating or zigzagging patterns. Rutledge notes that some of Dee’s tables had crudely random patterns reminiscent of a person filling them in by hand in an arbitrary way.Most of these tables were intended for magical use: to make talismans or find sacred names in order to perform magical invocations of angels.

However, I wonder if the purpose was cryptographic: the medieval and early modern ciphers made use of tables of rectification. Trithemius Steganographia is mentioned in Soyga, and is itself an apparent treatise on magic that actually does contain cryptographic work.

Hiding a cipher key in the form of a magical table would seem fairly rational as a cover today, but given how much more sensitive magic was back then (it landed both Trithemius and Dee in trouble) it is a bit like using illegal pornography as a way of hiding encryption keys: not exactly a discreet method if somebody pries.

Soyga might have been a method of generating new tables that were far more random than the Trithemius table. It produces an uniform distribution of letters with nearly no pattern. Take the first 23 rows and columns as a tabula recta and you have something that would be far more resistant to cryptoanalysis.

But given that the Vigenère cipher was viewed as uncrackable, was there a perceived need for anything else? I suspect that the urge to invent new encryption methods has always been strong: if you have a cool idea based on your own field of expertiseyou will suggest it (after all, if you cannot break it, it must be unbreakable!).

In fact, the use of a transformation of the previous column seems to be like an autokey cipher. The first real autokey cipher was suggested ion 1556 by Cardano in De Subtilitate, but the first useful on was invented in 1564 by Giovan Battista BellasoVigenère published one in 1586. Liber Soyga was mentioned by Dee in 1583. Could the Soyga automaton be the result of somebody working on an autokey method, perhaps getting the bright idea of applying it again and again to itself? It would seem to fit into the time.

Of course, the border between cryptography and angelic communication might have been blurry. Maybe the tables were seen as both: sufficiently advanced cryptography is indistinguishable from magic.

Posted by Anders3 at April 17, 2014 12:06 PM

Going Critical — Melting Asphalt – Kevin Simler


Systems Dynamics may have some limits, according to spruikers of complexity theory as superior to systems thinking – still interesting, though 🙂


Source: Going Critical — Melting Asphalt


Going Critical

by Kevin Simler
If you’ve spent any time thinking about complex systems, you surely understand the importance of networks.
Networks rule our world. From the chemical reaction pathways inside a cell, to the web of relationships in an ecosystem, to the trade and political networks that shape the course of history.
Or consider this very post you’re reading. You probably found it on a social network, downloaded it from a computer network, and are currently deciphering it with your neural network.
But as much as I’ve thought about networks over the years, I didn’t appreciate (until very recently) the importance of simple diffusion.
This is our topic for today: the way things move and spread, somewhat chaotically, across a network. Some examples to whet the appetite:
  • Infectious diseases jumping from host to host within a population
  • Memes spreading across a follower graph on social media
  • A wildfire breaking out across a landscape
  • Ideas and practices diffusing through a culture
  • Neutrons cascading through a hunk of enriched uranium
A quick note about form.
Unlike all my previous work, this essay is interactive. There will be sliders to pull, buttons to push, and things that dance around on the screen. I’m pretty excited about this, and I hope you are too.
So let’s get to it. Our first order of business is to develop a visual vocabulary for diffusion across networks.

A simple model

I’m sure you all know the basics of a network, i.e., nodes + edges.
To study diffusion, the only thing we need to add is labeling certain nodes as active. Or, as the epidemiologists like to say, infected:
This activation or infection is what will be diffusing across the network. It spreads from node to node according to rules we’ll develop below.
Now, real-world networks are typically far bigger than this simple 7-node network. They’re also far messier. But in order to simplify — we’re building a toy model here — we’re going to look at grid or lattice networks throughout this post.
(What a grid lacks in realism, it makes up for in being easy to draw 😉
Except where otherwise specified, the nodes in our grid will have 4 neighbors, like so:
And we should imagine that these grids extend out infinitely in all directions. In other words, we’re not interested in behavior that happens only at the edges of the network, or as a result of small populations.
Given that grid networks are so regular, we can simplify by drawing them as pixel grids. These two images represent the same network, for example:
Alright, let’s get interactive.
The network below has playback controls at the bottom. Press the ▷ button to watch the activation spread, or step through one moment at a time:
In this simulation, an active node always transmits its infection to its (uninfected) neighbors.
But this is dull. Far more interesting things happen when transmission is probabilistic.

Continues in source: Going Critical — Melting Asphalt


Redesigning Health Care with Insights from the Science of Complex Adaptive Systems – Crossing the Quality Chasm – Paul Plsek, 2001 – NCBI Bookshelf

A classic of its kind

Source: Redesigning Health Care with Insights from the Science of Complex Adaptive Systems – Crossing the Quality Chasm – NCBI Bookshelf


Crossing the Quality Chasm: A New Health System for the 21st Century.

Show details

Institute of Medicine (US) Committee on Quality of Health Care in America.
Washington (DC): National Academies Press (US); 2001.

Appendix B

Redesigning Health Care with Insights from the Science of Complex Adaptive Systems

Paul Plsek

The task of building the 21st-century health care system is large and complex. In this appendix, we will lay a theoretical framework for approaching the design of complex systems and discuss the practical implications.


A “system” can be defined by the coming together of parts, interconnections, and purpose (see, for example, definitions proposed by von Bertalanffy [1968] and Capra [1996]). While systems can be broken down into parts which are interesting in and of themselves, the real power lies in the way the parts come together and are interconnected to fulfill some purpose.

The health care system of the United States consists of various parts (e.g., clinics, hospitals, pharmacies, laboratories) that are interconnected (via flows of patients and information) to fulfill a purpose (e.g., maintaining and improving health). Similarly, a thermostat and fan are a “system.” Both parts can be understood independently, but when they are interconnected, they fulfill the purpose of maintaining a comfortable temperature in a given space.

The intuitive notion of various system “levels,” such as the microsystem and macrosystem, has to do with the number and strength of interconnections between the elements of the systems. For example, a doctor’s office or clinic can be described as a microsystem. It is small and self-contained, with relatively few interconnections. Patients, physicians, nurses, and office staff interact to produce diagnoses, treatments, and information. In contrast, the health care system in a community is a macrosystem. It consists of numerous microsystems (doctor’s offices, hospitals, long-term care facilities, pharmacies, Internet websites, and so on) that are linked to provide continuity and comprehensiveness of care. Similarly, a thermostat and fan comprise a relatively simple microsystem. Combine many of these, along with various boiler, refrigerant, and computer-control microsystems, and one has a macrosystem that can maintain an office building environment.

A distinction can also be made between systems that are largely mechanical in nature and those that are naturally adaptive (see Table B-1). The distinctions between mechanical and naturally adaptive systems are fundamental and key to the task of system design. In mechanical systems, we can know and predict in great detail what each of the parts will do in response to a given stimulus. Thus, it is possible to study and predict in great detail what the system will do in a variety of circumstances. Complex mechanical systems rarely exhibit surprising, emergent behavior. When they do—for example, an airplane explosion or computer network crash—experts study the phenomenon in detail to design surprise out of future systems.

TABLE B-1. Mechanical Versus Naturally Adaptive Systems.


Mechanical Versus Naturally Adaptive Systems.

In complex adaptive systems, on the other hand, the “parts” (in the case of the U.S. health care system, this includes human beings) have the freedom and ability to respond to stimuli in many different and fundamentally unpredictable ways. For this reason, emergent, surprising, creative behavior is a real possibility. Such behavior can be for better or for worse; that is, it can manifest itself as either innovation or error. Further, such emergent behavior can occur at both the microsystem and macrosystem levels. The evolving relationship of trust between a patient and clinician is an example of emergence at the microsystem level. The AIDS epidemic is an example of emergence that affects the macrosystem of care.

The distinction between mechanical and naturally adaptive systems is obvious when given some thought. However, many system designers do not seem to take this distinction into account. Rather, they design complex human systems as if the parts and interconnections were predictable in their behavior, although fundamentally, they are not. When the human parts do not act as expected or hoped for, we say that people are being “unreasonable” or “resistant to change,” their behavior is “wrong” or “inappropriate.” The system designer’s reaction typically is to specify behavior in even more detail via laws, regulations, structures, rules, guidelines, and so on. The unstated goal seems to be to make the human parts act more mechanical.

Continues in source: Redesigning Health Care with Insights from the Science of Complex Adaptive Systems – Crossing the Quality Chasm – NCBI Bookshelf

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