Complex adaptive systems?
Copyright 2017 Graham Berrisford. One of about 300 papers at http://avancier.website. Last updated 05/01/2019 22:41
System Theory Tutorial in London Saturday March 2nd 2019
General system theory was supposed to unify the sciences.
Today, much systems thinking discussion is not general – it is unique to human society
And some is more metaphysical than scientific or verifiable.
E.g. MIT offer this definition of “complex adaptive system”.
"Complex Adaptive Systems are dynamic systems able to adapt in and evolve with a changing environment.
It is important to realize that there is no separation between a system and its environment in the idea that a system always adapts to a changing environment.
Rather, the concept to be examined is that of a system closely linked with all other related systems making up an ecosystem.
Within such a context, change needs to be seen in terms of co-evolution with all other related systems, rather than as adaptation to a separate and distinct environment."
This definition doesn’t tell us what a “system” is, or how to distinguish it from any other kind of entity in the universe.
It uses the term “adaptive” in a way that may be interpreted to mean different things.
And it gives us no hint as to why or how any system, adaptive or not, should be considered “complex”.
So, what does it mean to assert that IBM is a complex adaptive system?
This paper proposes four possible answers.
Note: This paper is not about social network analysis (SNA) or the problems that it solves.
Nor is it about agile system development.
A few terms (recap)
The term system is widely used, but loosely, and with a variety of meanings.
At its most vacuous, it means only "a set of things that are related to each other, if only by their relationship to something else.”
The concept was defined more purposefully, in his introduction to cybernetics, by Ross Ashby.
General system theory is mostly about systems that exhibit regular or repeatable behaviors.
A concrete system contains actors and their actions on objects or variables.
An abstract system contains the roles and rules that actors and their activities are supposed to adhere to.
The latter is abstraction from the infinite complexity of any entity that realises it.
E.g. The roles and rules of the stickleback mating ritual are realised by countless pairs of sticklebacks.
In discussion and testing of the mating ritual, no attention is paid to the complexity of a stickleback’s internal biochemistry.
General system theory concepts are ubiquitous in modern systems analysis and design methods.
This table presents them in a way that can be used in discussion of social entities.
Abstract social system
A set of roles and rules (the logic or laws actors follow)
Concrete social system
Actors playing the roles and acting according to the rules
Actors who inter-communicate and act as they choose
By the way, a social cell is a social network in which actors find that realising the roles and rules of a particular social system is so attractive they resist any change to it.
Network or system?
Some contrast human societies with deterministic or mechanical systems.
Mostly, they seem to be contrasting a network of actors (e.g. IBM employees) with the many different systems they may realise.
Of those many systems, some are complex, others simple; some are adaptable, others inflexible
Some are purely social, others socio-technical; some are cooperative, others in conflict.
The term complex widely used, but loosely, and with a variety of meanings.
Complex or complicated?
Some systems thinkers use the terms complex and complicated as though the latter is simpler.
Mostly, it seems, they are really contrasting realities and descriptions.
They are contrasting concrete realities (complex) with abstract descriptions (merely complicated).
Or contrasting a human’s behavior in the world (complex) with their behavior acting in a defined role (merely complicated).
“Complicated” abstract system
Exchanging Birthday cards
Order > Invoice > Payment
“Complex” concrete system
A network of friends
IBM finance department
How measure complexity (or complication)?
There seems no agreed way.
One thing is certain, to measure the complexity of a thing, you need a description of its parts.
And to compare the complexity of two things, those parts must be described comparably, at the same level of abstraction.
Which is more complex, IBM or a hen’s egg?
At the topmost level of description, the organisation structure of IBM is simple.
By contrast, a description of IBM that included every human actor in it would be exceedingly complex.
But then, a description of a hen’s egg that included every atomic particle would be even more complex.
Often, an entity is called a “complex adaptive system” where one or more of the following are true.
· No measure of complexity has been agreed
· No level of abstraction has been agreed
· No quantifiable properties are described, which makes any measure of complexity impossible.
· No description of the entity as a system has been agreed, or even made.
· No description is possible, because the entity changes continually, rather than generationally.
Some naively presume that the complexity of an organisation lies in its management structure.
Or (after Ashby and Beer) a system’s complexity is defined by the number of variables it maintains.
Our paper on complexity measures points out that one ought to measure the complexity of behaviors as well as structures.
Does complex mean unpredictable or chaotic behavior?
The Plexus Institute glossary at https://plexusinstitute.org/ says:
"complexity is found in systems when there are unpredictable interactions of multiple participants and components across many levels of the system."
Yet chaos theory taught us even the simplest of systems can be unpredictable.
And unfortunately, the glossary contains no definition of "system", let alone what the "levels" of a system are.
By complex, systems thinkers often mean the system’s behavior changes the state of world in a non-linear or chaotic fashion.
But complexity and chaotic are different qualities.
A complex system can behave in a linear or predictable way over time.
A simple system can behave in a non-linear of chaotic way over time.
And one system’s behavior may switch from linear to non-linear change, and back again.
(See the papers on System Dynamics.)
How measure adaptiveness? There seems no agreed way.
And even before that, the term adapt is used with two different meanings.
Remember that Ashby insisted we should on no account confuse these two kinds of change.
System state change
the value of at least one state variable
homeostatic regulation of values to stay within a desired range
the type of at least one variable or behavior.
re-organization changing the variables or the rules that update their values.
Ashby used the term adaptation in the context of homeostatic state change.
Classical cybernetics is about systems that maintain the values of their state variables.
By contrast, systems thinkers often use the term adaptive in the second – mutation – sense
And moreover, they mean the system is self-organising.
Second-order cybernetics was developed around 1970 by Margaret Mead, Heinz von Foerster and others.
It is about self-organising systems; it is the recursive application of cybernetics to itself.
It allows systems actors to be system thinkers, who re-organise themselves.
It allows actors in a system to study the system and change it.
Actors not only play roles in a system, but also observe and change the roles, rules and state variables of that system.
You can’t play a game of tennis unless all players agree the rules, at least for the duration of each rally/point.
Provided actors change a system incrementally, the classical concept of a system is upheld.
But if actors change a system continually, this undermines the very concept of a system.
Systems thinkers often speak of a social entity or organisation as a complex adaptive system.
What does the term mean? Is it merely a pseudo-scientific label for a human society?
Or does it have a more general and scientific meaning?
What is true of a complex adaptive system that is not true of a simple activity system, or true in classical cybernetics?
Let us analyse the MIT definition we started with.
"Complex Adaptive Systems [CAS] are dynamic systems”
The term dynamic may be read as changing or changeable.
In both CAS and classical cybernetics, systems are dynamic in the sense they change state in response to external events.
Also, they are dynamic in the very different sense that they can change via generational system mutation.
“able to adapt in and evolve with a changing environment.”
The terms adapt and evolve refer to changes inside the system.
In both CAS and classical cybernetics, a system is connected to its environment by an information feedback loop.
That loop enables the system and its environment (or external actors in it) to change state in step with each other.
Also, in a second and very different sense of change, a system may change via generational system mutation.
System mutation may happen in these three ways.
1. By self-replication with changes - as a biological system evolves with each new generation.
2. By redesign by actors outside the system - as a machine or software system is changed in each new version.
3. By redesign by actors who play roles inside the system - as a human social system may evolve in each new version.
Probably, the MIT definition is referring to the third kind of mutation.
However, human social systems might instead evolve in the first two ways above – are those to be ignored?
Moreover, it is unclear whether MIT mean generational change or continuous change, which is very different.
Because if a social group changes continually, stuff happens, but it can never be described and tested as a system.
So to call it a system is an empty assertion.
“there is no separation between a system and its environment”
The term separation might be misleading.
In both CAS and classical cybernetics, a system is connected to its environment.
They are connected logically in description, and physically in reality, by an information feedback loop.
At the same time, you must be able (at least logically) to separate what is inside the system from what is outside.
Else the system has no boundary, and you cannot distinguish it from its environment.
“a system always adapts to a changing environment.”
In both CAS and classical cybernetics, a system adapts by changing state in response to events it detects in its environment.
And, in a very different sense of adaptation, a system may evolve via generational system mutation.
Surely, what MIT mean to imply is that the system adapts, rather than the system is adapted?
In other words, the actors who play roles in the system are the ones who change its roles and rules?
“the concept to be examined is that of a system closely linked with all other related systems making up an ecosystem.”
In both CAS and classical cybernetics, the environment outside any one system of interest may be divided into separately describable systems.
And all those systems may be seen as subsystems of a wider system (an eco system if you like).
Is this not as true of simple systems as complex adaptive systems?
“change needs to be seen in terms of co-evolution with all other related systems, rather than as adaptation to a separate and distinct environment."
In both CAS and classical cybernetics, if one system changes state, then related systems may be triggered to change state.
Or if one system mutates, then related systems may be have to mutate also, if they are to survive.
Is this not as true of simple systems as complex adaptive systems?
Regarding complexity, this definition from another source introduces the concept of non-linear or chaotic behavior.
“A complex adaptive system exhibits both linear and non-linear behavior.
As per chaos theory, incremental changes to one of its state variables can turn linear quantitative change into qualitative change.
There comes a point where the system behaves in a qualitatively different way.
As per catastrophe theory, the topological shape of the system state space may change dramatically.” Source lost
The trouble is, simple systems fit this definition also.
As you turn a simple tap, the stream of water changes from orderly to chaotic.
What differentiates MIT’s complex adaptive system from other systems?
Surely, primarily, that it mutates of its own accord, from within?
And isn’t that true of every group of human actors who communicate with each other?
Every family, social club, business (say IBM) or other social network – whether it is describable and testable as a system or not?
So, what does it mean to assert that IBM is a complex adaptive system?
This paper proposes four possible answers, the last two of which seems the best.
1- IBM as a whole is one very complex system, which is changed frequently under change control.
This is implausible, since it is inherently difficult to design and maintain such a large and complex system.
And experience suggests the change control and testing procedures would prevent frequent change.
2- IBM changes its organisation/management structure frequently.
This is not necessarily to change what IBM does, and so not a good answer here.
3- IBM has an organisation-wide meta system (a strategy and architecture team?) for changing what its actors do.
This meta system is relatively simple compared with the countless distinct systems IBM realises.
4- IBM is a complex adaptive social network rather than a system.
It is a society or organisation, in which the individual human actors are inherently complex and adaptable.
The network of actors employed is large and complex.
They realise many distinct systems, some more flexible than others.
The actors are encouraged to change those systems in response to changes in the environment of – and related systems outside of - IBM.
Read Social networks versus social systems for an answer to that question.
How to extend system theory to embrace “self-organisation”?
Read System stability and change for an answer to that question.
A commonly-referred-to chart of “complexity science” can be found here http://www.art-sciencefactory.com/complexity-map_feb09.html
The chart is misleading chronologically and conceptually.
Chronologically, for a different view, read papers before this one.
1. The science & philosophy of systems
2. Thinkers before systems thinking
3. System thinkers & their ideas.
Conceptually, the chart mixes up science, pseudo science and metaphysics.
It mixes up scientists and people who make assertions and classify things with no empirical validation.
It includes people (e.g. Parsons and Luhmann) whose ideas are metaphysical – cannot be verified or disproved.
And note one misleading implication: the line from first to second order cybernetics suggests a progression where there is rather a schismatic difference.
Many social systems thinkers use words that are drawn from harder science, or sound as though they are.
Some speak of self-organising systems.
If the actors in a social network change the roles or rules of a system they realise, that may well be called self-organization
But if the actors make changes continually (rather than incrementally) then there is never any describable or testable “organization”.
The term complexity theory is odd, given there is no widely agreed measure of complexity.
By complex, people often mean a system has non-linear dynamics, or changes the state of world in a chaotic fashion.
But the equation of complex and chaotic is misleading.
Even a very simple orderly system can produce chaotic results over time - as may be revealed by System Dynamics.
Some systems thinkers’ terms relate to patterns revealed by System Dynamics - like strange attractors and fractal geometry.
Other terms often used more poetically than scientifically include emergent properties and entropy.
And some thinkers, deliberately, radically alter the meanings of terms used in science.
E.g. Luhmann’s metaphysical autopoietic social system radically differs from an autopoietic biological organism.
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