In A World of Systems – YouTube

Published on 4 Mar 2016

Enjoy “In a World of Systems”, narrated and illustrated by David Macaulay (of “How Things Work”) in collaboration with Linda Booth Sweeney and our team at Donella Meadows Institute. The video makes up one third of an online learning module we are designing for young change-makers who want to understand systems and change them. Sit back and meet systems in our everyday lives, from plumbing to traffic jams to fisheries, based on the work of the renowned systems thinker Donella Meadows!

The Blended Systems Thinking Approach – enhancing understanding to enable regenerative transformation | systemspractitioner – Pauline Roberts

Imagine if there was an approach that could take away the fear that managers feel because they have no idea what improvements to make, where to start or how to get to where they want and need to be…

Source: The Blended Systems Thinking Approach – enhancing understanding to enable regenerative transformation | systemspractitioner

Revealing In-Block Nestedness: detection and benchmarking Albert Sol´e-Ribalta, Claudio J. Tessone, Manuel S. Mariani, Javier Borge-Holthoefer

[I’m posting this because it *sounds* fascinating, and applicable to pattern language, complex adaptive systems stuff, and the VSM. And also in the hope someone who understands the mathematics will explain it to me :-)]

Revealing In-Block Nestedness: detection and benchmarking
Albert Sol´e-Ribalta, Claudio J. Tessone, Manuel S. Mariani, Javier Borge-Holthoefer

As new instances of nested organization –beyond ecological networks– are discovered, scholars are debating around the co-existence of two apparently incompatible macroscale architectures: nestedness and modularity. The discussion is far from being solved, mainly for two reasons. First, nestedness and modularity appear to emerge from two contradictory dynamics, cooperation and competition. Second, existing methods to assess the presence of nestedness and modularity are flawed when it comes to the evaluation of concurrently nested and modular structures. In this work,
we tackle the latter problem, presenting the concept of in-block nestedness, a structural property determining to what extent a network is composed of  blocks whose internal connectivity exhibits nestedness. We then put forward a set of optimization methods that allow us to identify such organization
successfully, both in synthetic and in a large number of real networks. These findings challenge our understanding of the topology of ecological and social systems, calling for new models to explain how such patterns emerge

Source: [1801.05620] Revealing In-Block Nestedness: detection and benchmarking

Seven characteristics of complex systems – Sonja Blignaut blogs Paul Cilliers

Seven characteristics of complex systems – Sonja Blignaut blogs Paul Cilliers

I have been re-reading the work of Prof Paul Cilliers, who truly was a pioneer in complexity thinking.  I came across this summary of the general characteristcs of complex systems in a piece he wrote in 2000.  It is concise and accessible qualitative description of complexity and I thought it would be useful to share here on my blog.

  1. Complex systems consist of a large number of elements that in themselves can be simple.

  2. The elements interact dynamically by exchanging energy or information. These interactions are rich. Even if specific elements only interact with a few others, the effects of these interactions are propagated throughout the system. The interactions are nonlinear.

  3. There are many direct and indirect feedback loops.

  4. Complex systems are open systems—they exchange energy or information with their environment—and operate at conditions far from equilibrium.

  5. Complex systems have memory, not located at a specific place, but distributed throughout the system. Any complex system thus has a history, and the history is of cardinal importance to the behavior of the system.

  6. The behavior of the system is determined by the nature of the interactions, not by what is contained within the components. Since the interactions are rich, dynamic, fed back, and, above all, nonlinear, the behavior of the system as a whole cannot be predicted from an inspection of its components. The notion of “emergence” is used to describe this aspect. The presence of emergent properties does not provide an argument against causality, only against deterministic forms of prediction.

  7. Complex systems are adaptive. They can (re)organize their internal structure without the intervention of an external agent.

From: What can we learn from complexity, Prof Paul Cilliers, Emergence, March 2000

Top Inspiration, Events and News on Systems Change 

since I posted most of the links from the Systems Studio newsletter last month… here’s this months’ newsletter. A lot of rich reading here 🙂




Inheritance Is Moving Beyond Genetics and Epigenetics

Heredity Beyond the Gene

What you pass on to your kids isn’t always in your genetic code.

The idea that genes encode all the heritable features of living things has been a fundamental tenet of genetics and evolutionary biology for many years, but this assumption has always coexisted uncomfortably with the messy findings of empirical research. The complications have multiplied exponentially in recent years under the weight of new discoveries.

Classical genetics draws a fundamental distinction between the “genotype” (that is, the set of genes that an individual carries and can pass on to its descendants) and the “phenotype” (that is, the transient body that bears the stamp of the environments and experiences that it has encountered but whose features cannot be transmitted to offspring). Only those traits that are genetically determined are assumed to be heritable—that is, capable of being transmitted to offspring—because inheritance occurs exclusively through the transmission of genes. Yet, in violation of the genotype/phenotype dichotomy, lines of genetically identical animals and plants have been shown to harbor heritable variation and respond to natural selection.


Conversely, genes currently fail to account for resemblance among relatives in some complex traits and diseases—a problem dubbed the “missing heritability.”1 But, while an individual’s own genotype doesn’t seem to account for some of its features, parental genes have been found to affect traits in offspring that don’t inherit those genes. Moreover, studies on plants, insects, rodents, and other organisms show that an individual’s environment and experiences during its lifetime—diet, temperature, parasites, social interactions—can influence the features of its descendants, and research on our own species suggests that we are no different in this respect. Some of these findings clearly fit the definition of “inheritance of acquired traits”—a phenomenon that, according to a famous analogy from before the Google era, is as implausible as a telegram sent from Beijing in Chinese arriving in London already translated into English. But today such phenomena are regularly reported in scientific journals. And just as the Internet and instant translation have revolutionized communication, discoveries in molecular biology are upending notions about what can and cannot be transmitted across generations.

Biologists are now faced with the monumental challenge of making sense of a rapidly growing menagerie of discoveries that violate deeply ingrained ideas. One can get a sense of the growing dissonance between theory and evidence by perusing a recent review of such studies and then reading the introductory chapter from any undergraduate biology textbook. Something is clearly missing from the conventional concept of heredity, which asserts that inheritance is mediated exclusively by genes and denies the possibility that some effects of environment and experience can be transmitted to descendants.

continues in headline link

Information theory, predictability and the emergence of complex life – Luís F. Seoane, Ricard V. Solé

via Complexity Explorer


Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated with detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated with maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.