Disambiguations
Copyright 2020 Graham Berrisford. A chapter in the book at https://bit.ly/2yXGImr Last updated 31/05/2021 11:43
If system architecture frameworks and systems thinking approaches are to advance, separately or together, the many ambiguities in them must be exposed and resolved.
This chapter resolves
about 20 ambiguities recognized in previous chapters.
Contents
Two kinds
of holistic thinking (analytical and synthetic)
Two
kinds of organization (structural and behavioral)
Two
kinds of part (passive and active structure)
Three
kinds of emergence (evolution, hierarchy and interaction)
Two
kinds of actor (automaton and agent)
Two
kinds of system (abstract and physical)
Two
kinds of state (physical and conceptual)
Two
kinds of behavior (process and state change)
Two kinds
of state change (homeostatic and progressive)
Two kinds
of social system (actor centric and activity centric)
Two
kinds of freedom (to select and to invent)
Two
kinds of social entity (purposive and purposeful)
Two
kinds of purpose (intent and outcome)
Two
kinds of organization (order and social structure)
Three
kinds of self-organization
Wholeism (considering every conceivable aspect or element of a thing or situation) is impossible. We can never consider every element or interaction that could be identified in a given system or situation. For example, any holistic model we make of a biological ecology excludes almost everything knowable about the physical reality it models.
Holism: thinking either (synthetically) of the effect produced when given things interact, or (analytically) of the things and interactions needed to produce a given effect, and zooming out or in do that.
Holism in system analysis: Holistic thinking can mean
looking for causes and rescoping systems. It can mean looking outside the
boundary of a given system for the cause of an effect. And then rescoping the
system of interest.
Holism in system synthesis: System
design is holistic by definition. The requirement is to produce emergent
properties or effects. Designers must both a) divide the to-be system into parts and b) design how those
parts interact to produce the required emergent properties.
Part: a structure
inside a whole, be it an active structure (subsystem or actor) or a passive
structure (material or data). To organize some parts into a whole can
refer to a structure in which parts are connected, or refer to a process
those parts play roles in.
How actors connect in a structure is one thing. How actors interact is another. Systems thinking is more about the latter than the former.
Structures can be classified in many ways; we must at least distinguish active structures from passive ones.
Passive structure: a
static that structures organize or connect things. For example, a
classification hierarchy like the Dewey Decimal
System, a matrix like the chemist’s periodic table, or a network
structure, or an organization chart, or a timetable for work to be done.
These patterns are interesting, and some may call them systems, but they are
not systems of interest in the sense here.
Active structure: an actor who can not only connect in some way, but also interact to advance the state of a system. The systems of interest both include active structures, and may be seen as active structures. Think of:
·
a solar system in which orbiting
planets interact by gravity with a star and each other
·
a windmill in which parts rotate and
interact to grind corn and make flour.
·
a tree in which parts interact to
consume sunlight and carbon dioxide, and produce a tree trunk and oxygen.
·
a boxing match in which boxers and a
referee interact to advance the state of the match, as recorded on the judges'
scorecards.
Bear in mind the passive/active distinction is a matter of perspective. A sandstone pebble is an entity composed of sand or quartz grains connected by cement. It is a discrete entity - created, changed and destroyed by events. We normally see it as a passive structure.- as acted on rather than an actor. However, it can be recast as an active structure; it can be presented as dynamic system that absorbs and excretes a small amount of water.
Note also that some descriptions can be animated. Narrative and mathematical procedures are passive structures that may be executed. Human and computer actors not only read descriptions of activities to be performed, but also perform those activities.
Emergence: the appearance of properties in a higher or wider thing that emerge from coupling lower or smaller things.
Some systems thinkers see this as the
concept most definitive of systems thinking. This table (bottom to top) presents a history of the universe from
the big bang to human civilisation.
|
THINKING LEVEL |
Elements
or actors |
|
Civilisation
|
Human
organizations |
|
Human
sociology |
Humans
in groups |
|
Social
psychology |
Animals
in groups |
|
Psychology |
Animals
with memories |
|
Biology |
Living
organisms |
|
Organic
chemistry |
Carbon-based
molecules |
|
Inorganic
chemistry |
Molecules |
|
Physics |
Matter
and energy |
In relation to this table, we can identify three kinds of emergence:
· The emergence by evolution of higher levels over time
· The everyday emergence of higher-level phenomena from lower-level phenomena.
· The everyday emergence of effects from interactions between things at one level
The first idea is widespread outside of systems thinking. In “On the ontology of space-time”, Gustavo Romero wrote that reality seems to be organized into.at least 6 levels. Each step up to a higher level adds some emergent properties and laws - adds complexity you may say.
|
Romero’s level |
Elements
and rules |
|
1 ontological substratum |
basic
events, which precede the emergence of physical things at the levels below |
|
2 physical |
matter
and energy, which obey the laws of physics. |
|
3 chemical |
molecules,
which interact according to the laws of chemistry. |
|
4 biological |
animals,
which embody biological processes. |
|
5 social |
behavior,
which requires interactions between people |
|
6 technical |
see note below |
Level 1 "is formed by basic events and precedes the emergence of physical things at the physical level." This reflects the presumption that events come before entities. Every entity in the universe is created, changed and destroyed by events. What we perceive as persistent structures are the transient side effects of behaviors. An entity can be modified by events in ways that change its state (e.g. cold to hot), or change its type or the activities it can perform (e.g. caterpillar to butterfly).
Level 6 is oddly placed in terms of complexity. Consider a pendulum, a very simple technology, a physical system that operates at level 1. That it was designed by a person does not make it more complex than a person or a society.
It seems reasonable to say that complexity (in the orderly rather than messy sense) increases or emerges when moving from a lower level of thinking to a higher level. Chemical evolution after the big bang complexified the periodic table and the range of possible chemical reactions. Biological evolution tends to complexify the organisms of a species, and widen the range of an animal's possible behaviors. Animal behavior evolution has led to complex social interactions which depend on perceptions and memories at the psychological level, synapses at the biological level, chemicals at the chemical level, and atoms at the physical level. Human evolution has created actors who can envision and make new things (e.g. paintings), new activities (e.g. card games), new tools and software systems.
Misconceptions about emergence include that it requires manyness, surprise, complexity.
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. Designed systems are intentionally designed to produce specified results or effects. 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”.
For more on complexity science, catastrophe and chaos read this chapter.
Automatons are actors that have no choice other than
to follow given rules. If they do select from a fixed range of activities, then
it is by following some deterministic rule. Or else by making a random choice (perhaps
qualified by some degree of probability).
Agents are actors that can choose to exercise some degree of autonomy.
The term agent coves a spectrum of possibilities, since an agent may be free to:
At the extreme, an agent may be entirely self-serving. And act in ways that are regardless of any other actor or other actors' aims.
“different observers of the same phenomena may conceptualize
them into different systems”. Ackoff
"a
system... is independent of the concrete substance of the elements (e.g. particles, cells, transistors, people, etc).” Principia
Cybernetica Web
When people speak of a system, they may speak of an entity (the unknowable whole of a thing or situation, regardless of how observers look at it) or a pattern of activity (a regular or repeatable behavior observed or envisaged in a thing).
In cybernetics, an entity is only a physical system in so far as it realizes an abstract system - a description of regular behavior relevant to “some main interest that is already given” as Ashby put it. This table shows how an abstract system can be realized as a physical system by a physical entity.
|
How
an abstract activity system
is realized |
Game of poker |
|
Abstract system: rules,
rules and variable types |
The rules of the game |
|
Physical system: activities
giving values to variables |
A game of poker |
|
Physical entity:
physical actors able to perform the activities |
A card school |
This book contrasts and relates activity system theory, which is about an abstract system and how it is realized, with social entity thinking, which is about a physical entity, and the activities (or activity systems) it realizes.
The passage of time is revealed by changes. The universe is an ongoing process in which the state of things changes over time.
E.g. Consider a tennis match. At any moment in time, the players, the balls and the tennis court have a current material or physical state, which we don't attempt to measure. At the same time, the tennis match has the conceptual state shown on the score board. In practice, we represent the physical state of a thing as a conceptual state, as a vector containing the values of state variables.
E.g. In physics, the vector of an object might contain its spatial coordinates at a moment in time. And in economics, the vector might contain the current inflation rate and unemployment rate.
In business and software architecture, a behavior is usually a process. Any action, activity, operation or procedure that takes time to perform. Perhaps shown in some kind of flow chart. In system dynamics, a behavior is a state change trajectory, a line of behavior that shows how the value of a state variable changes over time.
Systems thinking is concerned with things whose state vector changes over time. Some things advance from one state to the next. E.g. a moon rocket, or a tennis match. Other things cyclically return to the same point, or stabilize themselves in an equilibrial state. E.g. a solar system, or a thermostat-controlled room temperature.
Ashby discussed how a system may respond to input events or disturbances in two ways – deterministic and stochastic. When observing actors’ behavior, we can classify their responses to stimuli into four kinds.
|
Causality |
In theory, when an event happens, we can predict |
|
Deterministic |
exactly which action an actor will perform in
response. |
|
Probabilistic |
how likely an actor will perform activity type A or
activity type B. |
|
Possibilistic |
the actor will choose from the range of activity
types in our model. |
|
Self-determining |
nothing – because actors can invent activities
outside any model made. |
Classifying causality types this way helps us to characterise what makes social entity thinking different from activity systems thinking.
In a poker game, the range of actions is limited to those that characterize the system. The rules do not tell players whether to "call", "raise" or "fold". Players strive to make their choices unpredictable. They also try to detect probabilities in how others choose between the actions allowed in the system.
Where the actors in a system are anthropomorphic rather than computational, we assume they can not only act in the first three ways. but also be innovative, in the fourth way. Outside of their role in playing cards, the same human actors are innovative; they invent new responses to events and conditions.
A social entity is not well called an activity system. A complex adaptive system (CAS) with human actors might be called a complex adaptive social entity (CASE).
Sociology may address all four kinds of causality above. Whether our psychology is deterministic or not at the biological level is irrelevant. At the sociological level, we must treat people of sound mind as having free will.
Two branches of social systems thinking may be distinguished. Activity system theory - about regular activities, performed by actors playing roles (e.g. a poker game). (The actors are changeable, and may act outside the system.) Social entity thinking - about actors, who perform activities (e.g. a card school with a pack of cards). (The activities are changeable.)
Other views are possible (e.g. aim-centric and state variable-centric). But the dichotomy above is the best explanation of why so much systems thinking discussion is confused or confusing.
Why say social entity rather than
social system?
Paradoxically,
some systems thinkers promote anarchical social structures and/or irregular
one-off behaviors. They propose human actors respond to events and conditions
as they choose, learn from experience and respond to novel situations in
innovative ways. Which is fine. The trouble here is, it undermines the general
concept of a "system". If actors continually exercise their freedom
(as autonomous agents) to innovate, then there is no regularity or repetition,
and no recognizable activity system.
Freedom might be defined as the degree to which the actors in a system can make decisions and act as they choose. Freedom to select activities: In an activity system, actors can select from a range of regular activities. The range is limited to what the system allows. Giving actors a wider choice of actions, a higher degree of freedom, increases the system’s complexity. Freedom to invent activities: In a social entity, actors may invent their own activities, even their own aims. They may do this without any overarching change control, and make ad hoc decisions that lead them down novel paths. Ironically, every decision is a constraint in the sense that choosing one path denies another.
The purposes of a purposive social
entity lie in the desire of external actors for that entity to produce
particular state changes in the state of its environment. The internal actors
may be seen as slaves to that end. The purposes of a purposeful social
entity are found in the desire of its internal actors to produce internal
and/or external state changes, as they choose to do.
Purpose as desired outcome: an intention
– an aim we have - a reason to do things. Purpose as actual outcome (POSIWID):
an outcomes of system behavior we observe, or a use we find for things
We can't have it both ways. One of the most powerful ways to influence the behavior of a social entity is through its purpose or motivation. By changing its purpose(s) you can change the activity system(s) that a social entity realizes, and the outcomes it produces.
In social entity thinking, the
term organization is used to mean two different things.
The management structure of a
social entity under which human actors are arranged to perform the required
activities.
A pattern of behavior, a repeated
pattern of inter-actor communication in a group or network of actors from which
properties emerge.
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.
|
|
Continuous |
Discrete |
|
State change |
Natural the
growth of a crystal in liquid |
Natural asleep
to awake, or day to night |
|
Designed analogue
light dimmer switch |
Designed light
on to light off |
|
|
Mutation |
Natural maturation
of child into adult |
Natural parent
to child |
|
Designed X |
Designed version
1 to version 2 |
X? Continuous mutation may occur in nature. In design, it can be simulated by dividing changes into discrete steps frequent and small enough to appear continuous. Moreover, we can design a system that acquires new features or abilities in discrete steps, by introducing a higher entity or meta system of the kind discussed later, in the chapter on system change and evolution.
Agile system mutation: Agile
system development implies a designed system mutates in small ways, and
frequently, from one generation to the next. Agile activity system: An
agile activity system is one that can handle changes in its environment,
without having to mutate when the environment changes.
Discussions of self-organization can refer to at least three kinds of change; the last is the most interesting here. Goal-seeking state change: An entity is drawn to one or more "attractor" states and resists being moved from such a state - as in homeostatic biological and electro-mechanical control systems. Self-assembly or growth: This is another kind of state change. An entity grows incrementally by adding more elements or actors to its body. E.g. the growth of a crystal in a liquid, or a plague of locusts. Self-improvement: This is a kind of state mutation. Self-improvement implies changing from "bad to good" in some way. Ashby's way of thinking about this is discussed in other chapters.