What every systems thinker should know

Copyright 2017 Graham Berrisford. A chapter in “the book” at https://bit.ly/2yXGImr. Last updated 17/04/2021 22:07


If system architecture frameworks and systems thinking approaches are to advance, separately or together, ambiguities in them must be exposed and resolved. This chapter defines a dozen or so terms used later. And as a step towards resolving further ambiguities, it divides systems thinking into two broad schools, both of which are needed.


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Contrasting views of systems thinking. 1

General principles. 2

Activity system thinking. 9

Social entity thinking. 11

More ideas to be explored. 13

Remarks and relevance to EA.. 16


Contrasting views of systems thinking

In some discussion, the term systems thinking implies little more than a glorification of holistic thinking (or “everything is connected”). Other articles contrast different ways of thinking about systems using the adjectives in this table.


Contrasting terms

Hard system

Soft system







Simple or solvable

Complex or wicked


Some use the adjectives on the left pejoratively, to deprecate a model or systems thinking approach. This misleads people about system sciences, for reasons discussed below.


Hard or soft systems thinking? It is common to divide systems thinking methods into hard and soft. Midgely (2000) presented hard, soft and critical systems thinking approaches as though they were steps in a historical evolution. Yet the hardest of system sciences regards a system as soft in the sense that it is a perspective of an entity or phenomenon, represented in a model made by observers, in the light of some given interests. And even mechanical engineers must:


·           Analyse current situations, understand assumptions

·           Look at the big picture, overarching drivers, goals and principles

·           Identify stakeholders, their concerns and viewpoints

·           Unfold multiple perspectives, promote shared understanding.

·           Monitor and manage changes to requirements, time, cost, resources etc

·           Outline solutions or changes and how to make them


Reductionistic or holistic systems thinking? All system sciences take a holistic view of systems. They are concerned with how a whole system produces effects that its parts cannot on their own. At the same time, all are also reductionistic in that they divide a system into parts, and sometimes divide coarser-grained parts into finer-grained parts.


Mechanical or human systems thinking? Both hard and soft systems methods view a human activity system as a machine in the broadest sense of the term. Using Checkland’s soft systems method, one draws a business activity model to show how regular or repeated activities complete a “transformation” that human actors are employed to make.







Inputs à Activities à Outputs





Similarly, as in Meadows’ primer in systems thinking, every causal loop diagram or systems dynamics model represents a machine, which connects stocks (representing resources, populations or human actors) by flows (representing rule-bound, cause-effect relationships).


Linear or non-linear thinking? Some promote non-linear rather than linear thinking. Yet all system sciences (including system dynamics) regard systems as having linear causality in the sense that effects can be traced to causes; and all (including cybernetics) allow that over time, a system may behave in complex, non-linear or chaotic ways.


Simple/solvable or complex/wicked? All system sciences presume reality is infinitely complex. All we can know of it is what we can understand and test of descriptions we draw from reality. And most of the ten points that define wicked problems apply to most human activity problems. Be the problem large or small, there is rarely a perfect answer; rather, there are trade-offs to be made between competing goals, and balances to be drawn between different design options. The options can’t be neatly divided into good or bad, only placed on a scale between those extremes.


Activity systems thinking or social entity thinking? This book starts from the position that a system thinking approach (hard or soft) should help us describe a human activity system (be it simple or complex, linear or non-linear) in a holistic way. The primary distinction we need to bear in mind is the one drawn in this chapter, between human activity systems and the social entities that realize them.

General principles

The systems of interest are describable in terms of actors, activities and the effects or results they produce. An actor is a physical entity (animal, mechanical or social) that can perform one or more activities. An activity is an action, in which one or more actors participate. In thinking about these systems, it helps to recognize and acknowledge some general ideas up front.

Systems involve interaction

"Rather than reducing an entity to the properties of its parts or elements, systems theory focuses on the arrangement of and relations between the parts which connect them into a whole.” Principia Cybernetica Web


The systems of interest are not merely passive structures that organize or connect things - be it

·       a classification hierarchy like the Dewey Decimal System,

·       a matrix like the chemist’s periodic table,

·       a network structure,

·       a timetable for work to be done, or even 

·       a management structure (as in an organization chart).


Rather, a system is found in the interactions between things, in how actors interact in the performance of activities. The interactions between systems and parts within systems are central to system dynamics in general and to the success of business operations in particular.


Core concepts:

Interaction: an activity involving two or more actors or subsystems. In a physical system, the interaction may be by force, matter or energy. In a social and business systems, interactions involve the communication of information in data flows or messages and via shared data stores or memories.

Systems are scoped by observers

When observers describe an entity, they bound it as they choose, and describe the parts and features of interest to them. They determine the granularity of the whole and of the parts they mention. They may speak of some parts, and ignore others. They may speak of parts as though they are atomic, taking for granted what they contain. Two observers, looking at the same entity, may describe it in terms of different parts and at different levels of granularity.


This is not to say that all views or models of the same entity are equally correct or valuable. The truth or usefulness of a model should be testable and verifiable as corresponding to the reality. If a model cannot be falsified, then its value as a model or theory of reality is debatable, to say the least. Still, two different models (e.g. of light as waves or particles) may both be verifiable and useful in different contexts.


When discussing or defining a system that operates in the real word we exclude almost everything that is potentially knowable about that reality, Every system we describe is “soft” in the sense that it is a view, perspective or model of things relevant to our interests.


“Our first impulse is to point at [some physical entity] and say "the system is that thing there". [However] every material object contains an infinity of variables and possible systems. Any suggestion we study all the facts is unrealistic [in practice we] pick out and study the facts relevant to some main interest already given.... there can be no such thing as the unique behaviour of a very large system... for there can legitimately be as many [systems] as observers" Ashby 1956.


In EA? No human “organization” (no orchestra, card school or business) is completely knowable. We model aspects relevant to some given interest. Above the level of indivisible units (people and technologies) that a business employs, the scope and contents of any wider activity system are determined by observers in the light of goals or interests they bring to it.


Moreover, a business is a mess (as Ackoff put it). Different observers may identify many different activity systems in the same business, and some of them may be in conflict.

Holism is not wholeism

"Were the engineer to treat bridge building by a consideration of every atom he would find the task impossible by its very size [therefore] studying very large systems by studying only carefully selected aspects of them is simply what is always done” Ashby 1956.


Wholeism (considering every conceivable aspect or element of an entity) is impossible. We cannot fully understand any real-world thing or situation. Even a grain of sand is beyond our full comprehension. All we can understand are models (sensations, descriptions) we form of a real-world entity.


To think holistically is to think about how effects are caused by interactions between things. We can think holistically only about some particular things we are interested in. For example, any holistic model we make of a biological ecology excludes almost everything knowable about the physical reality it models.


Holistic thinking can involve zooming in or out.


Zooming out. We may look outside the boundary of a given whole for the cause of an effect, and then widen the scope the whole to include that cause. Consider the dramatic flexing of the Tacoma Narrows bridge. At first, we might describe the flexing as an emergent property of the bridge. Later, we realize it is an emergent property of a wider whole in which some parts of the bridge interact with a part of its environment - the wind.


Zooming in. Designers look first at a required system from the outside. Taking a "black box" view, they identify the system’s inputs, outputs, and effects or results of value to external actors. Then, they look inside the system and divide it into two or more parts (subsystems or actors) and design how those parts interact – holistically - to produce the required state changes or outputs. If any part of the system could produce these “emergent properties” on its own, the rest of the design would be redundant.


Core concepts:

Holism: thinking either synthetically (zooming out) of the effect produced when given things interact, or analytically (zooming in) of the things and interactions needed to produce a given effect.

Part: an element inside a system, be it an active structure (subsystem or actor) or a passive structure (material or information).


By the way, the idea of zooming in and out is not specific to systems thinking. Basic thinking tools include a) expanding and/or shrinking the boundary of what is being studied, b) testing a proposition or argument by taking it to an extreme, c) making a case for the opposite of what you believe (cf. Shannon’s Test: invert the logic of a sentence to see if the ‘negative’ has more impact and information value).

Reductionism is the flip side of holism

It is naive to promote holism and deprecate reductionism, since they are two sides of the same coin. Systems can be nested, such that one person’s whole is another’s part. But you can't zoom out forever (else you’ll find the root cause of all is the big bang at the start of the universe). And when you stop zooming out, and identify how two things interact to produce some effect or result, you are thinking in a reductionistic way about a whole you have just drawn a boundary around.


Whether a system designer approaches the design task by top-down system decomposition, or by identifying atomic parts to be assembled (bottom up) into a system, there is always an atomic level of system definition. The atomic parts are readily obtainable, or another designer's problem, or indivisible agents such as human actors.


Reductionism 1: studying one or more parts of a thing on its own.

This is common practice, because one person’s part is another’s whole. A heart surgeon may see the regular beating of the heart as an “emergent property” of particular muscles and valves interacting inside a heart. A general practitioner may see a patient’s heart as an atomic part of a whole body. People zoom in and out; they decompose and compose systems as they see fit. There are times when looking studying a part on its own, and fixing or removing it, is necessary and beneficial. In a social system, you might even have a good reason to study the psychology of an individual human actor!


Reductionism 2: looking for the part responsible for some behavior of a whole.

Consider a word processor; the interface to the whole includes interfaces to its parts, such as a spell checker, which fulfils just one function of the whole. Obviously, that part is not responsible for all the functions of the whole, else the rest of the whole would be redundant. Moreover, it can be removed with little or no impact on the whole. (By way of removable parts, consider also the spleen, gall bladder and appendix of a human.)

There is a hierarchy of scientific domains

“We presently "see" the universe as a tremendous hierarchy, from elementary particles to atomic nuclei… to cells, organisms and beyond to supra-individual organizations… We cannot reduce the biological, behavioral, and social levels to the lowest level, that of the constructs and laws of physics.” Bertalanffy 1968


People construct hierarchies to make sense of the messy network of things that exist in reality. This table (bottom to top) presents a history of the universe, from the big bang to human civilisation, as a hierarchy.



Elements or actors

Interact by

Knowledge acquisition

Human civilisation

Human organizations

Information encoded in writings

Science and enterprise

Human sociology

Humans in groups

Information encoded in speech

Teaching and logic

Social psychology

Animals in groups

Information encoded in signals

Parenting and copying


Animals with memories

Sense, thought, response



Living organisms

Sense, response. Reproduction


Organic chemistry

Carbon-based molecules

Organic reactions


Inorganic chemistry


Inorganic reactions



Matter and energy




People speak of entities at higher levels as being more complex than entities at lower levels. However, “complexity” an is ill-defined idea. Which is more complex: a social activity like a game of poker or a material object like a playing card? When we discuss the complexity of a thing, we usually do so with respect to a description of it at one level of thinking, ignoring the internal complexity of whatever we see (at that level) as atomic parts.


Within each level of thinking, other hierarchies may be defined. In physics, there is a hierarchy that descends from galaxies through solar systems down to atomic particles. In biology, we see a body as decomposable successively into organs, cells, organelles and organic chemicals.


People often model reality by imposing a hierarchical structure over atomic aims, actors or activities. But do those hierarchies exist in nature? Or only in our models of it? And which is the top? Do cells serve the interests of the body? Or does a body serve the interests of its cells? Do genes enable an organism to live? Or does an organism live to reproduce its genes? The answer depends on your perspective.


The first general system theorist, Bertalanffy, was a biologist, who looked for principles and patterns in systems inside and outside biology. Another biologist and systems thinker Maturana, observed that “knowledge is a biological phenomenon”. As the table above implies, before life, there was no knowledge, description or model of reality.

Emergent properties can evolve or be designed

“that a whole machine should be built of parts of given behavior is not sufficient to determine its behavior as a whole: only when the details of coupling are added does the whole's behavior become determinate.” Ashby 1956


With reference to the levels in the table above, we can identify three kinds of emergence. The last is the primary meaning in systems thinking discussion


The emergence by evolution of higher levels over time. This idea is widespread outside of systems thinking.

"Today, it’s a real intellectual deprivation to be blind to the marvellous vision offered by Darwinism and by modern cosmology – the chain of emergent complexity leading from a ‘big bang’ to stars, planets, biospheres, and human brains able to ponder the wonder and the mystery of it all." Quoted from this essay on science.


The everyday emergence of higher-level phenomena from lower-level phenomena. Somehow, conscious thought emerges from electrical activity in the brain. However, we don’t attempt to explain thought in terms of electrons, or explain a baseball game in terms of molecules, or explain Facebook in terms of the hardware components and radio waves it depends on. We usually model a system at one level of thinking, or adjacent levels.


The everyday emergence of effects from interactions between things at one level.

Consider the effects or results that emerge from interacting things in the examples below. In each case, interactions produce an outcome the things cannot produce on their own.


·        The force produced by a wind passing over a sail.

·        The progress of a rider on a bicycle.

·        The V shape of three geese in flight.

·        The shimmering of a school of fish.

·        The price of fish that emerges when customers and suppliers strike a deal.


Not only is emergence is used with various meanings above, it is also wrongly presumed to imply a multitude of actors or agents, or unpredictable or complex behavior.


Emergence does not require a system to have many actors. Two actors are sufficient to produce emergent properties, as in the progress of a rider on a bicycle.


Emergence does not mean a system behaves in a surprising or unpredictable way. Natural systems often produce results or effects that appear surprising or mysterious. At least, they appear so, until you know how they work. By contrast, designed systems are intentionally designed to produce specified results or effects. And when a designed system produces unexpected effects, we call them "unintended consequences".


Emergence does not mean a system is complex in any normal sense of the term. Because in systems thinking, we necessarily ignore the internal complexity of the “atomic parts”. In studying a rider riding a bicycle, we ignore the complexity in the biology of the rider, and the motions of the ball bearings in the wheel hubs. In the discussing the orbits of planets in the solar system, we ignore the composition and atmosphere of each planet.


Core concept:

Emergence: the appearance of properties in a higher or wider thing that emerge from coupling lower or smaller things.

Open systems are encapsulated

“Every living organism is essentially an open system.  It maintains itself in a continuous inflow and outflow…” Bertalanffy 1968


The boundary of a system may be physical or logical. Physical boundaries are more obvious. Bertalanffy saw the skin of a biological organism as its boundary. Business architects may draw a boundary around an entity located in space, such as a farm, factory or shipyard. More often, business architects draw a logical or legal boundary around actors distributed in space and connected by information flows.


System boundaries can be nested. A boiler transforming water into steam may been as a system, or as a subsystem of a steam engine. The human heart can be seen as a system, or as a subsystem of the body. Boundaries draw can also overlap.


“The distinction between an external (black-box, abstracting from the contents of the box) and internal (white-box) view is common in systems design. The external view depicts what the system has to do for its environment, while the internal view depicts how it does this.” ArchiMate 3.1.


Core concepts:

System environment: the world outside the system of interest.

System boundary: a line separating a system from its environment.

Closed system: a system not connected to anything outside its boundary.

Open system: a system connected to its wider environment by inputs and outputs. An open activity system is characterized by how it responds to inputs (aka events or perturbations).

System interface: a description of inputs and outputs that cross the system boundary.

Process: a sequence of activities that changes a system’s state and/or transforms inputs into outputs.


Some say there are no closed systems in the real world; however, people do model closed systems, for example in causal loop diagrams. Such a closed activity system may be characterized by its line of behavior (a concept defined later).


In EA? Whether, the boundary of a business is physical or logical, wide or narrow, the business may be connected to its suppliers and customers by inputs and outputs. The classic representation of such an open system is a SIPOC diagram.



Open system



Inputs à Processes à Outputs




Regulators need feedback

A special kind of interaction is one in which the output of a flow leads to the input of a flow. Feedback is important in cybernetics, in the control of one system by another. A business is coupled to its environment by producing outputs that affect external entities, and receiving inputs, some of which are responses to previous outputs.



Feedback loop


 ßstate information  




The Conant-Ashby theorem, or “good regulator” theorem, was conceived by Roger C. Conant and W. Ross Ashby and is central to cybernetics. In short, it states that every good regulator of a system must have a model of that system.


“Abstract "The design of a complex regulator often includes the making of a model of the system to be regulated. The making of such a model has hitherto been regarded as optional, as merely one of many possible ways. In this paper a theorem is presented which shows, under very broad conditions, that any regulator that is maximally both successful and simple must be isomorphic with the system being regulated. (The exact assumptions are given.) Making a model is thus necessary.



Here, a regulator can be any animal, machine or business that has a model, or has access to a model, of what it needs to monitor and control. So, read this triangle from left to right: regulators <have and use> models, which <represent> targets.


The good regulator


<have and use>           <represent>

Regulators    <monitor and regulate >   Targets


Ashby wrote of the model being isomorphic with system that is regulated, meaning, the elements and relationships in the model must be correlatable with elements and relationships in the reality.


The theorem has the interesting corollary that the living brain, so far as it is to be successful and efficient as a regulator for survival, must proceed, in learning, by the formation of a model (or models) of its environment."



Evidently, to function and respond to changes, an animal must “know” what it going on in its world. It needs a model of entities and events its environment if it is to find food and mates, and avoid enemies. The richer the model, the more adaptive the animal can be to changes in it environment. Similarly, a business needs to know the state of things it seeks to monitor or direct.


The question is not whether an animal or a business has a model; it is how complete and accurate is the model? To which the answers might be both “very incomplete and somewhat inaccurate” and “remarkably, complete and accurate enough”. Thinking about these answers leads inexorably to the view of description and reality that is outlined in the second half of this book.


Core concept:

Feedback loop: the circular fashion in which output flows influence future input flows.



Activity system thinking

This table outlines three schools of activity systems thinking which have a great deal in common.


Soft (business) systems

Ackoff and Checkland


Weiner and Ashby

System dynamics

Forrester and Meadows

Regular activities transform inputs into outputs wanted by customers

Regular activities maintain variables that describe the state of actors (organisms, machines, societies)

Regular flows increase/decrease variable stocks that represent the state of resources or populations of any kind

Feedback loops connect a business to its environment thus:

a) it detects changes in the state of its environment

b) it determines responses

c) it directs entities to perform activities.

Feedback loops connect control systems to target systems or entities

a) a receptor senses changes in the state of the target

b) a control center directs responses, and

c) an effector changes the state of the target.

Feedback loops connect stocks that respond to changes in each other.

The whole model represents a closed system or ecosystem.

Observers may draw a business activity model.

Observers may observe the current state of a system, and draw a graph to show how the system's state changes over time.

Observers may draw a diagram of flows between stocks, and draw a graph of stock level changes over time.


The distinction between hard and soft systems is a distraction. All three schools are about systems characterized by regular activities (physical, organic, social, economic or ecological). All presume several activity systems may be abstracted from one social or business entity. All allow that systems may display complex, non-linear, self-organizing or chaotic behavior.




Activity systems thinking is applied every day, all over the world, to physical, organic, social, economic and ecological systems. It is useful whenever we seek to:


·       understand how some outcome arises from some regular behavior.

·       predict how some outcome will arise from some regular behavior.

·       design a system to behave in a regular way that produces a desired outcome.

·       intervene in a situation to change some regular pattern of behavior.


Generally speaking, an activity system is a regular or repeatable pattern of behavior, such as the motion of a rider on bicycle, a game of poker, or a billing and payment system. This table contains more examples of systems in which actors interact in regular activities to advance the state of the system.



Actors (active structures)

Activities (behaviors)

State variables

A solar system

Star and planets


Planet positions

A windmill

Sails, shafts, cogs, millstones

Rotations that transform wind energy and corn into flour

Wind speed, corn and flour quantities

A digestive system

Teeth, intestines, liver, pancreas etc.

Transformation of food into nutrients and waste

Nutrient and waste quantities

A termite nest


Disperse pheromone. deposit material at pheromone peaks

The structure of the nest

A prey-predator system

Wolves and sheep

Births, deaths and predations

Wolf and sheep populations

A tennis match

Tennis players

Ball and player motions

Game, set and match scores

A church


Roles in the church’s organization and services

Various attributes of roles and services

A billing system

Customer and supplier

Order, invoice, payment

Product, unit price, order amount


Core concepts:


Actor: an active structure of any kind (person, planet, cell, machine etc). It occupies space. It has either evolved, or has been made, bought, hired to perform activities.


Activity: a regular behavior or process, performed by actors over time. The term “regular” implies we are able to describe or model the activity, perhaps in a value stream, flow chart or a causal loop diagram. Activities can create, use and change passive structures (material or information) and active structures (actors). They can advance the internal state of a system, and so produce a “line of behavior” over time, and/or produce outputs, which advance the state of the system’s external environment.


Aim: a motivation, desired outcome or goal which is ascribed to a system by an observer. (Some express aims as actual outcomes, results or effects, as might be shown in a line of behavior. This ambiguity is addressed in chapter 2.)



Social entity thinking

"The first decision is what to treat as the basic elements of the social system. The sociological tradition suggests two alternatives: either persons or actions." David Seidl 2001


Person-centric social entity thinking is one thing; action-centric activity systems thinking is another. When people speak of a system, they may speak of a social entity or a pattern of activity. Both are useful views, but the system in one is not the system in the other. It is somewhat unfortunate that even highly respected systems thinkers have flipped from one to the other, apparently without realizing it. If systems thinking is to advance, consistently and coherently, the two schools must be distinguished.


“More abstract entities are realised by means of more tangible entities.” ArchiMate 3.1


This table shows how abstract activity systems are realized by tangible actors who communicate in a physical social entity.


How an abstract activity system is realized

Orchestral music

Game of poker

Abstract system: a description of roles for actors, rules for processes and variable types

A musical score

The rules of the game

Physical system: a performance of defined activities, which gives values to variables

A performance of the score

A game of poker

Physical entity: one or more physical actors able to perform the activities

An orchestra

A card school


In EA? An enterprise is a business entity of some kind, public or private. It could be an army, coal mine, steel maker, bicycle manufacturer, road haulier, logistics company, bank, insurance company, retailer, hospital, government department, or an internet giant like Amazon.


A business entity is a social entity that employs some human actors to perform some activities to meet some aims. To paraphrase Meadows, in observing a system, the actors are the most concrete and tangible elements, the activities are harder to see, and the aims are even harder to see. Conversely, in designing an activity system, the natural sequence is aims before activities before actors.


In defining aims, the normal practice is to zoom out, encapsulate the system of interest, and consider the effects it should produce. The overarching concern is the desired outcomes of system activity.


In defining the activity system needed to meet aims, the focus is on defining processes that produced desired effects, the rules to be followed and roles actors play in activities.


In defining the social entity in which actors are employed, the focus is on how actors are directed, motivated and organised to perform required activities; including general principles for actors to follow, specific aims for their activities, and the structure under which actors managed.


Whether your focus is on the activity system or the social entity depends on where you are coming from and what you aim to achieve. You may flip from one viewpoint to the other, but they come with different ideas about what it means for a system to be defined, to exist, and to change or evolve.

Causality and choice

Management science addresses human institutions that – in more or less bureaucratic ways - organize most or some of what human actors do. Typically, these social entities employ many discrete activity systems. And when observing actors’ behavior, we can classify their responses to stimuli into four kinds.



In theory, when an event happens, we can predict


exactly which action an actor will perform in response.


how likely an actor will perform activity type A or activity type B.


the actor will choose from the range of activity types in our model.


nothing – because actors can invent activities outside any model made.


In so far as a social entity realizes a known activity system, its actors always act in the first three ways. We may not be able to predict which action they choose to perform, but we can say they will choose one of the actions available to them in the activity system. In so far as actors act in the fourth way, they act outside any activity system we know of. Even if they are in acting in an activity system we don’t know about, we must to treat them as having free will, and the ability to do what they choose.

Systems thinking is not a movement for social change

Karl Marx referred to Darwinian evolution as though it provided a rationale for political revolution. Some now speak of systems thinking as though it is a movement that will solve problems they see in education, government or the biosphere. Some see systems thinking as a kind of philosophy or socio-cultural mission statement of the kind “everything is connected, and we've all got to work together” or “decentralization is preferable to centralization”. This kind of thinker tends to promote the ideas to the left of this table over the ones the right.


Do we value these?

Over these?



Individuals and interactions

Following processes

Network structures


Bottom up

Top down




Client-server relationships

Self-determination of actions

Direction and coordination of action


This book does not favor either side of the table. As Clemson said in his 1984 book on “management cybernetics” a systems thinker looks to find the optimal balance between centralization and decentralization. System design is a process that involves making trade-offs between alternative design patterns. The designer’s role is to understand the trade-offs and fit the pattern to the situation. And to prioritise what is achievable over what some might promote as an ideal.


Our ability to solve the world's problems by promoting "holistic thinking" and "self-organization" is limited. Moreover, to solve some problems (like climate change) we may need central authorities to agree a set of goals, rules and measures for all.



More ideas to be explored


Systems thinkers commonly refer to the adaptation of a system or business. What does adaptation mean? Ashby wrote that the word is commonly used in two senses which refer to different processes. He urged us to distinguish system state change from system mutation.


In fact, system change can be classified in three ways: continuous or discrete, state change or mutation, natural/accidental or designed/planned. Representing change as a three-dimensional phenomenon helps us to think about what it means to model change and design for it.





State change


the growth of a crystal in liquid


asleep to awake, or day to night


analogue light dimmer switch


light on to light off



maturation of child into adult


parent to child




version 1 to version 2


X? Continuous mutation may occur in nature. A designed activity system cannot mutate “continually”, but it can change in discrete steps. Continuous mutation can be simulated by dividing changes into discrete steps frequent and small enough to appear continuous.



Design and description

The terms “description” and design” appear many times in this book. What is the difference? We can observe and describe an existing thing by analysis or reverse-engineering. We can envisage and design a new thing by synthesis or forward engineering. Analytical and synthetic thinking are often contrasted as though people do one or the other. In practice, we interleave the processes, zooming in and out. Design involves making decisions about the thing to be made, and describing it. So, while not every description is a design, every design is a description.


Evolution? When people speak of the design of a natural system, they are referring to a structure and/or behavior that is an outcome of evolution. You might think of evolution as an opportunistic kind of design process, and DNA as a special kind of description.


Architecture? To keep things simple, think of architecture as high-level description or design. We’ll return later to look at what else an “architecture framework” addresses.


Cartographers create maps (descriptions) of territories (described things). The map is not the territory, but the two must be correlated well enough to help map users find things.


This book introduces an "epistemological triangle" that relates descriptions to described things. Read the triangle below from left to right: Mappers <observe and envisage> Territories. Mappers <create and use> Maps. Maps <represent> Territories.




<create and use>          <represent>

Mappers    <observe & envisage>   Territories


Bear in mind that one phenomenon can be described in several ways. Different maps, showing different features, may be drawn of one territory. You may use different maps for country walks, planning journeys, driving, studying geographic features, studying demographic features. And you may need to find or buy a new map now and then.


Bear in mind also that no description is complete. A territory is infinitely complex; a map cannot be a complete or perfect representation of it. A map is an abstraction that shows only enough that we can find or learn what we need to.


The more general triangle below relates describers to descriptions and phenomena. Note that a description in the mind is at the top of this triangle, not the left.




<create and use>              <represent>

Describers <observe and envisage> Phenomena


In the first half of this book, don’t worry about the triangles. Treat them as informal graphics that illuminate the text. In the second half, the semantics of the triangle are more important, because it turns out there is an essential difference between our triangle and comparable triangles you can find in semiotics and philosophy.


Most comparable triangles separate descriptions into internal models (descriptions in the mind) and external models (in speech or writing). By contrast, the description corner of our triangle generalizes both internal and external descriptions. It contains all descriptive structures (in the mind, in speech, on the page, wherever) that are created and used by cognitive processes (observing, envisaging, remembering, recalling, writing and reading).



Communication of information

“Another development which is closely connected with system theory is that of… communication. The general notion in communication theory is that of information.” Bertalanffy 1968


Data / information flows are especially important in social and business systems. Communicating actors create and use data structures and exchange messages that represent phenomena of common interest. In a society of communicating actors, information or meaning is found in the sender’s encoding of a data structure, with reference to a language, and a receiver’s decoding of that data structure, with reference to a language.


Note that for successful communication, actors must share the same code or language. Where mistakes cannot be allowed, in science for example, domain specific languages are defined. And in information system design, the meaning of a data structure is defined meta data.


Core concepts:

Communication: the exchange of information between senders and receivers.

Information: a structure or behavior that represents something, phenomenon, decision or direction.

Flow: a conveyance of force, matter, energy or information from one actor or subsystem to another.


The term flow is used in three concepts that overlap and are entangled.

·       Data flow: the conveyance of information in a message from a sender to a receiver.

·       Causal flow: the progress from a cause to an effect, or actor to what is acted on.

·       Denotic flow: the conveyance of goals, duties or obligations.



Remarks and relevance to EA

This chapter has reviewed several general principles:


·       Systems involve interaction

·       Systems are scoped by observers

·       Holism is not wholeism

·       Reductionism is the flip side of holism

·       There is a hierarchy of scientific domains

·       Emergent properties can evolve or be designed

·       Open systems are encapsulated

·       Regulators need feedback


Look at any business and you will see actors, performing activities, to meet agreed aims. To a greater or lesser extent, the actors are organized and the activities are systemized. It has been said that EA regards an enterprise as a "system of systems". More accurately, EA sees a business as a social entity that employs and participates in several activity systems. 


Activity systems thinking is about a network of regular activities performed by actors. Regular or repeated activities advance the state of things of interest (people, processes, materials and machines), which is remembered in the values of state variables and communicated in messages.


There is no presumption in activity system thinking that information is digitized. But since 1960, business activity systems have increasingly depended on IT. Commonly, when IT operations stop core business operations stop, it is impossible to continue core business operations in some non-IT-using way, and without access to digital data, the business is sunk.


Commonly, a business employs a messy patchwork of activity systems supported by IT, which may interact, overlap, and even be in competition. EA strives to extend and improve these systems, and optimize how they are coordinated to the benefit of the whole. And to do that, EA needs architectural descriptions of those systems.


Enterprise architecture

Architectural descriptions

<create and use>                 <represent>

System architects <observe and envisage> Business activities


Social entity thinking is about a network of actors who perform activities. EA requires a measure of social entity thinking. To begin with, logical functions and roles must be mapped to physical organization units and actors. Moreover, a business is more than the sum of the activity systems it employs. It is also a social entity, in which actors have some freedom to act as they see fit. The advice in short is:


Is your interest where actors

Then use

determine their own actions?

Social entity thinking


do both above and below?

Social entity thinking

Activity systems thinking

play roles in regular processes?


Activity systems thinking


This book looks at both social entity thinking and activity systems thinking, and their relevance to EA. It goes on to expose and resolve ambiguities with reference to five new or newly presented ideas.

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

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

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

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

·       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.