Preface to “the book” at https://bit.ly/2yXGImr

A new look at systems thinking, and how we know what we know

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This is a book for enterprise and business architects/analysts, and other business change agents. It is relevant also to teachers of computing, system and software engineering, and people who already consider themselves to be “system thinkers”.

To become a true systems thinker, you have to get your head around some abstract ideas. And when reading what is posted under the heading of “systems thinking”, you need to distinguish between discussion of a) the management and motivation of actors, and b) the modelling of activity systems that actors play roles in.

You also need to understand science. At the social psychology end of systems thinking, some put scientists down for thinking in "hard, systematic, reductionistic, linear" ways rather than "soft, systemic, holistic, non-linear" ways. Actually, scientists look at systems in all these ways.

Some systems thinkers speak using terms in a pseudo-scientific way. Some borrow terms from mathematics and physical sciences (non-linear, fractal, attractor, chaos etc.) and use them with different meanings in discussion of human organizations. Some talk of "second-order" cybernetics, not realizing that classical cybernetics already regarded every system as "soft", as a perspective taken by an observer. Some draw causal loop diagrams and present them as true, without any evidence. Some quote aphorisms that don't apply out of context, or don't bear close examination.

Many use terms with no clear definition. Consider the description of a human organization as a "complex adaptive system". Typically, the meaning of "complex" is undefined and unmeasurable. The term "adaptive" confuses adaptation by changing the state of a system with adaptation by changing the system itself. And supposing the term "system" means "parts interacting in a whole to produce effects one part cannot", its meaning in any specific organization is obscure until there is documented agreement about which parts, interactions and effects are of interest.

The ambiguity of words used in discussions of systems can mask the differences between activity systems and social entities. Without missing a beat, people flip from discussing activity systems representable in causal loop diagrams to discussing problems in human “organizations” (say, to do with the motivation, management, training or culture of employees) with little regard to modelling any particular activity system.

The result of this lax, loose or lazy use of words has been a very large and incohesive body of material. Much posted today under the headings of "systems thinking", "system innovation", "management science" and "complexity theory" is mystical (say, “creating space for the emergence of a fractal organization”) or so ambiguous as to be incoherent.

In addressing ambiguities, this work offers a new model for systems thinking and a new classification of approaches - with a view to having more meaningful and useful discussions about systems of interest.

You won’t find mathematical proofs and evidence drawn from practical experiments. You will find assertions about systems thinking are analyzed critically and logically, and conclusions are illustrated by easily understood examples, such as a ridden bicycle, a game of poker, a card school, nest building, a termite colony, and a business (say IBM).

What does this book do?

First, perhaps above all, this work encourages you to recognize the schism between activity systems thinking and social entity thinking. While both are needed, and they can be pursued in parallel, attempts unify them in a grand general system theory end up being confused and confusing.

The aim is not to help you save the planet or radically transform your business. For both novice and seasoned system thinkers, the aims are to:

· present a concise, accessible and coherent overview of established ideas, with the addition of new insights

· expose issues in current system thinking and practice

· rescue systems thinking from the pseudo-science found today under the headings of “systems thinking”, “complexity science” and “management science”

· to help you avoid “jingle/jangle fallacies”

· to advance system science by promoting a distinction between activity systems and social entities that can help you change either or both.

Any effort to advance system architecture frameworks and systems thinking approaches, separately or together, has to expose and resolve ambiguities. In doing so, this book pursues several lines of thinking, related to even more general views of human cognition and philosophy. This book:

· relates modern EA and BA techniques to scientific foundations, for example in cybernetics and Lean manufacturing

· resolves half a dozen ambiguities in the terminology of modern EA and BA frameworks

· resolves many more ambiguities in 50 years of systems thinking discussion

· reconciles "self-organization" with activity systems thinking

· challenges pseudo-scientific uses of system theory terms and concepts

· counters the narrative of systems thinking as a kind of socio-cultural movement

· advances a type theory that may be used to settle some philosophical debates.4

What’s new?

In my first year at university, I studied psychology, human biology and the history and philosophy of science (from Descartes to Popper). I went on to complete a degree course in psychology, then started work as a programmer. Over those few years, I was struck by the entanglement of ideas in that mix of disciplines. It has taken me decades to disentangle them as far as I do here; and I hope this will help readers think more clearly about systems thinking,

This book is underpinned by five new or newly presented ideas.

1. Drawing a clear distinction between social entities and activity systems helps us recognize and resolve ambiguities and confusions in modern systems thinking.

2. Classifying causality into four kinds helps us to characterise what makes social entity thinking different.

3. Representing “change” as a three-dimensional phenomenon helps us to think more clearly about what it means to model and design changes to systems.

4. Separating meta system from system – allowing one actor to play a role in each - helps us to reconcile activity system thinking with “self-organization”, and gives us an alternative to second order cybernetics.

5. Using an "epistemological triangle" to distinguish models from what they model, and relate information to the phenomena it corresponds to, gives us a practical and useful alternative to the classic semiotic triangle.

There is a lot to take in. Some readers find the writing style is dense or terse. But if you stick with it, your patience will be rewarded. Readers have said: “The most important work on EA and applied System Theory today” and “Makes EA more powerful, coherent and usable.”

The structure of the book

The book is structured into several parts. Since it serves a wide range of readers, and not every reader will want to read it in a linear fashion, here’s some advice on how to navigate the book.

· If you are new to systems thinking, read the first one or two chapters in each part, then return to what interests you.

· If you are a professional enterprise or business architect, start with “EA as systems thinking”, then return to read what interests you.

· If you are a seasoned systems thinker, skip around as you see fit.

· If you are interested in the evolution of intelligence and communication, psycho-biology, mathematics or philosophy, you should read the final part.

Systems thinking foundations

This part begins by resolving many ambiguities in the terminology used by systems thinkers. It proceeds to outline the classic ideas of social entity thinking and activity systems thinking. It concludes with an extensive analysis of abstraction in system description, and the dynamics of activity systems.

Management cybernetics

This part distils general principles from Ashby’s cybernetics, which addresses the storage and transmission of information, in memories and messages, to describe and direct the state of things. It discusses the application of cybernetics to management science, by Clemson and Beer.

Resolving confusions

This part further addresses confusions and ambiguities in discussion of “systems thinking” and “complexity science”. It discusses five big confusions, concerning system, purpose, behavior, holism and change. It goes on to discuss how Meadows tried to generalize from the distinct ideas from activity systems and social entity thinking, and points to ambiguities in “complexity science”.

EA as systems thinking

This part is not so much a “how to” method as an exploration and explanation of where systems thinking ideas are best used in and alongside any EA framework or method you already know, such as TOGAF or BIZBOK. It shows how distinguishing activity systems from social entities leads to a more consistent and coherent view of the whole systems thinking field. It distinguishes agile systems from agile system development. It discusses “self-organization” and a way of approaching “wicked problems”.

A philosophy for system science

The earlier parts of this book are largely about the description of reality that occurs in the modelling of systems. This final part discusses the acquisition, communication and verification of information from a more psycho-biological perspective. It discusses how we know what we know, and describe things by typifying them in memories and messages. Three points of note.

· That the universe exists in a mind-independent way is the presumption of all modern science. It is implicit in the difference between age of the universe, and the age of planets on which life can exist.

· The fact that human (and other animal) experience of the world involves forming descriptions of real-world phenomena (including models of systems) in memories and messages is well explained as a side effect of biological evolution. It is implicit in the "good regulator theorem" of Ashby and Conant.

· There is no presumption in biology that descriptions or models are complete or wholly accurate, only that, often enough, they help the actors who use them to survive and replicate.

With the help of a new epistemological triangle, the final part explores some implications of the ideas above for semiotics, philosophy and mathematics.

What does this book do?

What’s new?

The structure of the book

Further reading?