Copyright 2020 Graham Berrisford. A chapter in “the book” at https://bit.ly/2yXGImr. Last updated 15/10/2021 19:32
My belief is this: we cannot measure the complexity of a real-world entity (structure or behavior, card school or game of poker) except by reference to a description or model of it (be it the rules of poker or some other description) or to work that it does.
My tentative suggestion is this: To maintain its order, a thermodynamic system consumes energy. Perhaps, we might measure the complexity of a system in terms of the work needed to impose its definitive pattern on its structure and/or follow its definitive rules?
Can you place the following systems in order of complexity: the solar system, a steam engine, a beehive, IBM, and Google's software (reputedly two billion lines of code)? Can you say if any of the systems in the table below (some of which feature uncertainty, non-linearity, adaptivity or self-organization) is a complex system?
The tossing of a coin
Tosser, coin, table
Coin lands heads up or tails up (unpredictably)
A wind crossing a sail
Boat, sail, wind, water
Boat moves forward through the water.
A ridden bicycle
Human, bicycle, road
Rider and bicycle move along the road.
A thermostat-controlled heater
Thermostat, heater, air
Feedback cyclically switches heater on and off
A flock of starlings
The flock wheels in the sky.
Obviously, you cannot answer these questions until it is agreed what “complexity” means. In practice, the term is used so variously and so meaninglessly, it is hard to say anything general about it.
This “map of the complexity sciences” is a brave attempt to generalize by imposing a sequence and coherence on a messy jumble of ideas, but it is questionable. The line from classical cybernetics to second-order cybernetics is especially misleading. Confusions arise from sociologists and management scientists taking words from mathematics and physical sciences and applying them with different meanings to social and business organisations.
It is glib to say the complexity of a system relates to "emergence", “non-linearity”, "adaptivity" or "self-organization". Partly because all those terms are used with two or more meanings - as discussed earlier. And partly because systems that appear simple can have those features. It may be said that the bi-metal strip in a thermostat controls a heating system in an adaptive, non-linear way to produce the emergent property of an air temperature that oscillates around the one desired by the actor who sets the dial on the thermostat. Is that a complex adaptive system? Or is it a simple control device, based on a simple feedback loop?
Complex or complicated?
Some systems thinkers draw a contrast between complexity and complication – but in different ways.
abstract system description
concrete realization of a system
rule-bound activity system
social entity in which actors act creatively
All three contrasts in the table can’t be right. To speak of a social entity as a non-linear system is to imply it is a rule-bound activity system.
Physicists often speak of a physical structure (say, a molecule, or a solar system) as a system. Several have proposed measuring the complexity of a structure in terms of the process needed to build it (or a problem in terms of the process needed to solve it).
Seth Lloyd (in this paper) proposed a bounded entity’s complexity is its thermodynamic depth, the gap between its microstates and macrostate. He defined the complexity of a system in terms of the process that produces the system’s macrostate (emergent properties, such as temperature, pressure, volume and density) from its microstate (a specific configuration of microscopic parts), discarding information along the way.
Philip Anderson proposed a bounded entity’s complexity is a measure of its asymmetry (or perhaps better, its departure from symmetry), or the complexity of the process to build or draw the structure.
Thus, physicists tend to speak of a physically bounded entity as being a system, with a given measure of complexity. When they stop to think about it, they may realize they are really only talking about one perspective an entity, but they usually don’t. Instead, they plough on as though every material entity has one physical state, which they can identify. By contrast, cyberneticians and soft systems thinkers are aware that, by taking different viewpoints of an entity, they can identify different systems with different states and different ways of behaving.
As Ashby indicated, faced with a given material or social entity, there are two kinds of system thinking.
a) A physicist thinks of a system as a whole entity – “the thing as it is in itself” – its essential nature – regardless of any observer.
b) A cybernetician thinks of a system as a set of variables and way of behaving of interest to some observer(s).
So, it seems to me there is a dichotomy in how the term complexity is used.
a) A physicist speaks of the complexity they see as inherent in some material entity.
b) A cybernetician speaks of the complexity of an observer’s subjective perspective or description of a material or social entity.
If an entity can realise different systems, then it can have different complexities.
Remember the laws of thermodynamics? The first law states energy cannot be created or destroyed in an isolated system. The second states the entropy of an isolated system always increases over time. If I understand them correctly, this means that to increase the energy in a system, or maintain its order, you need to connect to it - and supply it with energy or else do some work on it.
Thermodynamics and information theory are related. Ashby’s cybernetics leans on the concept of information entropy developed by Claude Shannon in the 1940s, which is similar to the concept of thermodynamic entropy established in the 1870s. And yet, in his Introduction to Cybernetics, Ashby wrote:
1/2 “Cybernetics started by being closely associated in many ways with physics, but it depends in no essential way on the laws of physics or on the properties of matter.”
1/5 “In this discussion, questions of energy play almost no part—the energy is simply taken for granted.” "Even whether the system is closed to energy or open is often irrelevant”.
4/15 “cybernetics is not bound to the properties found in terrestrial matter, nor does it draw its laws from them.” “What is important is the extent to which the observed behaviour is regular and reproducible.”
7/24. Decay of variety: “any system, left to itself, runs to some equilibrium”. “Sometimes the second law of thermodynamics is appealed to, but this is often irrelevant to the systems discussed here."
Kolmogorov complexity (aka algorithmic complexity) is the length of a rule stated in the most efficient fashion possible. In algorithmic information theory the Kolmogorov complexity of an object, such as a piece of text, is the length of a shortest computer program (in a predetermined programming language) that produces the object as output.
Although the concept of information is central in much if not most system theory, there is a dichotomy in how the term information is used. In short:
a) A physicist speaks of information as a quasi-thermodynamic property, inherent in some physical entity, regardless of any observer.
b) A sociologist speaks of the information created by an observer to describe some physical entity (or a fantasy), and its purpose or use.
The physicist, Seth Lloyd wrote. “Information is represented physically by the different states of physical system – a frequency of a wave – the level of current in a semiconductor. Almost all physical processes involve the exchange and transformation of information.”
Sociologists and software engineers may say the opposite, that the different states of a physical system can be represented by information. They discuss information that actors register, remember and exchange in subjective views of physical reality. This information processing occurs over and above the thermodynamics of physical entities and processes represented by the information. In so far as this information is useful, it may be called knowledge, of which Maturana noted “Knowledge is a biological phenomenon”.
In discussions of “complexity theory” the term complexity is associated with many and various interesting qualities you might observe or describe, including those discussed above.
· Many define complexity as a feature of self-organizing and/or adaptive systems, disregarding that some such "non-linear" systems are simple in the everyday sense of the term.
· Ross Ashby pointed to the ambiguity of “self-organization" and repudiated the idea that a machine or organism could change its own organization.
· Several have proposed ways to measure the computational complexity of a structure in terms of the process needed to produce it from a given starting point.
· Seth Lloyd defined the complexity of bounded entity in terms of the "thermodynamic depth" of a process that turns the micro-scale properties of its parts into the macro-scale properties of the whole.
· Philip Anderson defined the complexity of a bounded entity in terms of it being more asymmetrical than a symmetrical structure.
· Some now relate complexity to the "edge of chaos", a zone between order and disorder.
In other sources, complexity has been associated with the following specific features of an activity system or social entity.
· Emergent properties. Yet the simplest of systems has emergent properties.
· Emergent systems. Yet a simple system may emerge from evolution, and a continually evolving entity is an ever-unfolding process rather than a modellable system.
· Networks. Yet there are simple networks.
· Open systems. Yet there are simple open systems, and complex closed ones.
· Adaptive or self-organizing behavior emerging from feedback loops in a closed non-linear system. Yet consider the simple feedback loop between a thermostat and a heater.
· Decisions made on a random or statistical basis. Yet consider the randomness in the calls made in a game of poker, or the probability that a customer fails to pay for goods received. Those decisions make the outcomes of a system more unpredictable, but (as in chaos theory) simple systems can be unpredictable.
· Chaotic system dynamics. Yet such a chaotic system it is still an orderly arrangement of stocks and flows, as may be shown in a simple causal loop diagram.
Most features above can be found in simple systems; a few are not relatable to any system that can be modelled. As a result, it is hard to pin down what "complexity science" or "complexity theory" is about. Beyond bundling interesting ideas people want to talk about under the heading of a "science", there seems no overarching coherence to the field.
If complex does mean any of the above (say, unpredictable, or chaotic), then it would be clearer to use the more specific word. Especially since some unpredictable and chaotic systems (say, a double pendulum) are simple in any normal sense of the term.
Surely, there is no way to measure the complexity of a thing per se? We can only measure a thing with regard to particular perspective or description of it, or a particular algorithm? And since a thing can be described in many different ways, and at any level of abstraction you choose, it has many different complexities.
For sure, every business of interest to us has many complexities. Many system design problems are messy, confusing and cannot be solved in a way that meets all requirements (whether due to time, cost, or resource constraints, or conflicts between requirements). Many feature most of the ten points that define wicked problems. 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. So, the best solution we can offer is a compromise that trades off between different needs.
Some have related complexity to the "edge of chaos", a zone between order and disorder, another ambiguous concept, since the term chaos is used in two ways:
a) The wide variety of outcomes produced by a system, given tiny variations in its starting conditions (as in chaos theory)
b) Disorder, the absence of a discernible pattern in some entity or phenomenon (as in thermodynamics).
Suppose we interpret chaos as disorder. To impose order on some parts of a whole is to arrange them according to some sequence, pattern or rules. A passive structure can display structural order. At first you may see a list of names (each: forename, middle name, surname) as disorderly; but when I show you it is sorted alphabetically on the middle name, you’ll see the list as ordered. An active structure can exhibit behavioral order, meaning its behavior over time follows given rules - as may be modelled in cybernetics or system dynamics.
Seth Lloyd made the interesting observations that, intuitively:
· Neither wholly ordered structures nor wholly random structures are complex.
· Duplicating a structure does little to increase the complexity of the whole.
Intuitively, the extremes of order and randomness are simpler than the states in between. At one extreme, a perfectly symmetrical triangle or octagon is simpler than an asymmetrical triangle or octagon. At the other extreme, a randomly arranged set of points, with no discernible pattern, is also simple.
Consider a static model of the particles in a gas cloud, which is apparently a random product of Brownian motion. Now suppose we notice that it matches a model of the stars in our galaxy. Surely, we’ll be right to assume somebody has manipulated the model? However, simply replicating a pattern takes much less work than creating one.
To conclude: it is meaningless to speak of a phenomenon in the real world as having only one measure of complexity; we can only measure that amount with respect to a given description, and there are several possible measures.
I don’t have a general definition of complexity. However, where a description is a model of a system's dynamics, and its run-time behavior is orderly in that it follows some rules, I am inclined to see complication as a measure of how much text (in a given language) we need to specify the rules, and to see complexity as a measure of how much work is needed to follow the rules and so maintain the order of the system.
Probably, given a standard language for defining rules (say the SBVR language from the OMG) the more rules you have, the more work you have to put in to apply those rules. But I can’t promise the complication of a design-time model corresponds to the complexity of the run-time behavior.
"Complexity science" stretches from mathematics to sociology. This makes it difficult to define the whole field in a coherent way. This chapter analyses the terms and concepts of complexity science/theory with reference to many sources.
To begin, here are some examples of what different people have called complex systems.
· A school of fish: nine fish swimming together.
· A ridden bicycle: two actors, rider and bicycle, moving down the road.
· A melting ice cube.
· Google’s software: reputedly two billion lines of code.
· The weather
· The biosphere
Questions to ponder. Do fish define or change the rules they follow? Can a rider change the rules that govern their bicycles motion? Is a melting ice cube complex? Is Google’s software complex? If all above are complex, can you give some examples of simple systems?
And before we analyse complexity theory terms and concepts, below are several mind-bending points to bear in mind.
Complex activity systems?
We model a software system by explicitly relating components at multiple levels of abstraction (delegation from clients to servers, composition of larger from smaller, generalization of more universal from more particular). By contrast, we usually model our world using one level in the hierarchy of sciences (sociology, psychology, biology, chemistry, physics) rather than by including several levels in one model.
Does it make sense to speak of systems in higher-level sciences in terms of scaling up and/or increasing complexity? Which is more complex sun (1057 hydrogen atoms) or a virus (millions of atoms). Can a higher-level social system be smaller and simpler than lower-level biological systems it depends on?
Model or reality? In cybernetics and system dynamics, a system is a model - a selection of structural state variables (cf. stocks) and behavioral rules (cf. flows) that change the system state. The models are abstractions, idealizations or simplifications of material realities. Even the most complex model of an economy or a biosphere is still only a set of state variables and mathematical rules. Observers may abstract countless different systems from one material entity. So, the complexity of a "system of interest" is not the complexity of the entity regardless of any observer.
Prediction time? Given a system model, its complexity might plausibly be measured by how long a computer takes to predict the next state of the system. However, that complexity measure may differ for each event type a system responds to.
Chaos? Chaos theory tells us there are rule-bound systems whose state at some future date is in practice unpredictable. And yet, we do build complex system dynamics models, and do attempt to predict future realities using those models. That is how climate change scientists and epidemiologists do their job.
Using the term complex to mean unpredictable or chaotic is confusing, since even the simplest of systems (e.g. a double pendulum) can have lines of behavior that are unpredictable or chaotic.
Complex social entities?
When "systems thinkers" speak of complex systems, they are often talking about complex realities (social entities, cities or organizations) that evolve in unpredictable ways, rather than rule-bound systems in the sense of cybernetic or system dynamics.
Some refer to a group of cooperating people as a Complex, Adaptive and System (CAS), yet find it difficult to define what they mean by any of those terms. Each term may each be used for an idea that is interesting and valuable on its own. However, you only have to skim the systems thinking literature to find different interpretations of the terms, both separately and together.
· Complex can refer to disorder or order: the messiness of a disorderly entity in the real world, or the complexity in the orderliness of a system description. It can mean large, that there are many actors or entities in a real-world phenomenon (like viruses and people in an epidemic), even though those actors may interact in simple ways. Or it can mean that, even though a system may be small and simple (like a double pendulum), it produces a complicated line of behavior.
· Adaptive can refer to the homeostatic behavior of an entity (e.g. shivering when it is cold), or to the evolution of an entity from one version or generation to another, or else to the design of a machine to be configurable. Sometimes it is equated to robust or resilient, two more terms used with various meanings.
· The system in question is rarely an abstract system, a model of the kind drawn in cybernetics, system dynamics or soft systems thinking. It can be a social entity, a group of people, for which there is little or no definition of roles or rules. The membership of the social entity may clear, or not. So, a complex adaptive system (CAS) might better be called an evolving social entity (ESE) whose members may realize countless activity systems, and change them now and then.
Let us look at each term in more detail.
A thing can only rightly be called a "system" with respect to a description made by some observer/describer that bounds some aspect or part of the world in which some smaller parts interact. Say, a card school, or a rider on a bicycle.
A card school (social entity)
A ridden bicycle (activity system)
the card players
the rider and the bicycle
each part responds to feedback from what the others do
each part responds to feedback from what the other does
does what one part cannot do alone
does what one part cannot do alone
adapts to changing conditions
adapts to holes and bends in the road
no overarching director or controller
no overarching director or controller
exhibits non-linear behavior
exhibits non-linear behavior
Of course, despite sharing so many characteristics, a card school and a ridden bicycle are very different. The first is social entity (a community of actors), the second is an activity system (a coordinated set of actions).
An adaptation is a change made to a thing in response to a change in its environment. However, Ashby pointed out the word adaptation “is commonly used in two senses which refer to different kinds of processes.” There is the process kind whereby an entity (animal, mechanical or social) responds to external changes so as to maintains its state or integrity. And there is the process kind called re-configuration or mutation that produces a new version or generation of an entity.
In short, an entity can adapt or be adapted to events by rule-bound state change (whether to maintain homeostasis or to advance its state progressively) or by rule-changing mutation, from one version or generation to the next.
"Change the rules from those of football to those of basketball, and you’ve got, as they say, a whole new ball game.” Meadows
"Social systems are not just ‘complex adaptive systems’ bound by the fixed rules of interaction of their parts. Rather, they are ‘complex evolving systems’ that can change the rules of their development as they evolve over time." This book Jackson 2003
In discussing social entities, Jackson preferred the term evolving to adaptive. Again, what many call a complex adaptive system (CAS) may better be called an evolving social entity (ESE) whose members may realize countless activity systems, and change them now and then.
A thing can only be called a "whole" or a "part" with respect to a scope defined by some observer/describer. E.g. consider the whole that is a fire, in the firebox, of a steam engine, at the front of train, carrying passengers, in the system defined by a train timetable.)
Then, the whole thing can only be called "simple" or "complex" with respect to the granularity of parts and activities regarded as atomic in the description of the thing.
Does complex mean messy? Given a disorganized and/or ever-changing entity, in which no pattern, regularity or repetition can be observed, there is no system. Surely, to call something a “complex system” is to imply the opposite, that it is organized or orderly in some way whose complexity can be agreed by some kind of objective assessment?
How to measure complexity? Google is two billion lines of code that interact to produce the search results you want. Is it simpler or more complex than a school of fish?
In practice, scores of complexity measures have been proposed, and using them is difficult. We do make subjective comparisons. However, to say the complexity of a thing is only in the eye of an observer is unhelpful. We do better to use a measure agreed by observers as useful for their purposes. And to agree how complexity is assessed or measured requires us to answer many questions.
How is complexity measured?
Do we measure reality or description? Cilliers (see appendix 1) didn't quite say so, but he implied systems thinkers tend to say "complex" when thinking of a physical entity (like a card school) and "complicated" when thinking of a description of what the entity is or does (such as the rules of poker).
We cannot measure the complexity of a card school per se, we can only measure it with respect to a description of what it is or does. We can count the roles, rules and variables in the definition of a game of poker. And we can measure the complexity of an actual poker game, in terms of the number of card players, the hands dealt, the money wagered and the decisions made by players.
So, do we count the types or the instances? Do we count the number of entity and event types in the abstract system description? Or the number of entity and event instances that exemplify those types in the operation of a physical activity system?
Do we measure structure or behavior? A simple structure can behave in complex ways. For example, look on the internet for a video of a double pendulum. [Links to videos to be included here.] Conversely, a simple system can produce a complex structure. For example, a Mandelbrot fractal.
(Aside: A fractal is an infinitely detailed two-dimensional image (a set of points). As you zoom in to look more closely at its border areas, you see the same pattern emerges, recursively, at lower levels of decomposition. By contrast, the process to generate or calculate this structure can be coded in less than 20 lines of code (based on a simple formula: z(n+1) = z(n)^2 + C). One systems thinker defines the term fractal very differently. "A fractal system is a complex, non-linear, interactive system which has the ability to adapt to a changing environment." Surely that describes every social entity you know of? No human organization is fractal. An organization's reporting hierarchy is not fractal. Zoom in, you divide one element into different elements. Zoom out, you group different elements into one.)
Which dimensions of system structure or behavior do we measure? There is complexity in memories actors maintain, messages actors exchange, activities actors perform, the resources actors need, the network in which actors connect, and the lines of behavior (trajectories of state variable value changes) over time.
At what level of abstraction do we measure? The atomic level of a description must be agreed. For a software application, do we count the modules, the operations, the lines of code, or the verbs and variables in the lines of code? Do we consider the internal complexity of human actors? The question is discussed in Brooks' no silver bullet article.
Appendix 1 contains a discussion of Cilliers’ ideas about complexity.
This section reviews this short guide to complexity theory: https://lnkd.in/dp7jsU5
In different posts/articles, the Systems Innovation source has defined complexity theory in terms of Emergence, Networks, Phase transitions, Non-linearity, Evolutionary dynamics, and Self-organization. Let us explore what these terms can mean.
Of several kinds of emergence, the kind most commonly discussed is the emergence of effects from the interaction of two or more things. All systems, including simple ones with only two actors, have emergent properties. Consider the holistic effects or results that emerge from interacting things in these examples:
· 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.
Judging by these examples, the emergence of effects or results does , does not require a system to be complex in any normal sense of the term (we always ignore the internal complexity of what we see as atomic parts), and does not imply a system behaves in a surprising or unpredictable way. If it turns out that a designed system produces unexpected effects, we call them "unintended consequences". require a system to have many actors
Networks of actors? activities? physical connections? communication paths? logical communications? Simple systems (say, actors sending and receiving an SOS message) have such networks.
“It is important to be aware that real world complex systems are the product of many overlapping networks interacting dynamically.” Ibid.
What does this mean? What “many networks” are found in a ridden bicycle or a melting ice cube? What way is there to interact other than dynamically? Does “overlapping networks” mean that two networks can share some members? Might that situation be better seen as one entity or network structure in which the members can play roles in two activity systems?
As in physics? Water to steam? Or moving from one attractor state to another? Do all complex systems do that?
“A phase transition may be defined as some smooth, small change in a quantitative input variable that results in an abrupt qualitative change in the system’s overall state.” Ibid.
In related videos, the quoted source seems to merge the ideas of “phase transitions” and “far from equilibrium thermodynamics” with the more debatable idea called “the edge of chaos” - discussed in appendix 2 below.
Nonlinearity (emerging from feedback)?
Meaning what? What is the opposite, a simple, linear system? One with no feedback loops? Or only dampening feedback? Surely non-linear systems (e.g. a double pendulum) can be very simple in the everyday sense?
“Nonlinearity describes how when two things interact the output is more or less than the sum of their parts in isolation. It arises out of the interdependency between elements within a system and interdependence over time through feedback loops.” Ibid.
Which is necessary to the definition here: feedback, emergence or both? System dynamics models feature feedback loops that produce emergent effects, yet are entirely deterministic and mechanistic. See appendix 2 for discussion of what else “non-linear” might mean.
One of System Innovation’s videos speaks of complexity theory as “emerging, post-Newtonian, non-linear, system theory.” Yet almost all science is post-Newton. And given that systems with feedback loops that display non-linear lines of behavior, adapt and self-organize, were addressed by system theories in the 1960s, what new theory is emerging?
Evolutionary dynamics (as in CAS)?
As in homeostatic adaptation? Simple thermostatic devices can do that. As in phase transitions? Is a body of H2O a complex system? Or as in replacement by a different system/generation as in biological or business evolution?
Here, this ambiguous term means coordination by peer-to-peer choreography rather than overarching orchestration. Simple linear systems can work thus. And simple rules can generate both complex structures like fractals and simple structures like the V shape in a flight of geese.
“Complex organizations like schools of fish, ant colonies, or car traffic manage to organize themselves into emergent patterns without any form of global coordination.” Ibid.
The rules for how fish swim together, and ants build nests, emerge from evolution. Is that well-called “self-organizing”? Neither fish nor ants cannot define or change the rules of the activity systems they act in. Car traffic is organized in many ways, including but not only the way that individual car drivers avoid collisions, and look for the fastest route.
This source defines a complex system as being “open, non-linear, chaotic, multi-dimensional and adaptive or self-organizing”. Yet a simple system can be open, and a closed system (e.g. an ecology modeled in causal loop diagram) can be complex.
Moreover, what most people would call a simple system can:
· produce a non-linear line of behavior (e.g. a double pendulum).
· have a non-linear disposition. (e.g. a wine glass is disposed to one or other of two possible effects when struck).
· be chaotic. (e.g. a predator-prey system, given slightly different initial populations, can produce very different outcomes and population crashes).
· adapt (e.g. consider a cooling system adapting to temperature change).
· have the same dimensions as a complex system.
This source says complexity concepts include the following jumble of ideas.
· Tipping points. The sociological term used to describe moments when unique or rare phenomena become more commonplace.
· The wisdom of crowds. The argument that certain types of groups harness information and make decisions in more effective ways than individuals.
· Six degrees of separation. The idea that it takes no more than six steps to find some form of connection between two random individuals.
· Emergence. The idea that new properties, processes, and structures can emerge unexpectedly from systems in operation.
The simplest of system produce emergent properties. And by the way, the wisdom of crowds is undermined by the "risky shift phenomena" identified by James Stoner in 1961. People change their decisions or opinions towards the extreme and risky when acting as part of a group, compared with acting individually. This is one form of “group polarization”.
The current consensus seems to be that CAS does not have a strict definition. You might say CAS is a polythetic type - a collection of attributes, not all of which are necessary to be called a CAS. But then, confusingly, not only are some of the attributes ambiguous, but also many are shared by simple systems.
“Complex: a whole made up of complicated or interrelated parts."
This means the complexity of the whole depends on how far you decompose your description of its parts. Other sources suggest complex systems have many actors or agents. However, this seems to confuse size with complexity. Is a school of 99 fish ten times more complex than a school of 9 fish?
“Adaptive: Marked by "adjustment to environmental conditions: such as
A. adjustment of a sense organ to the intensity or quality of stimulation, and
B. modification of an organism or its parts [to better fit] the conditions of its environment."
Remember, an entity can respond to events by rule-bound state change or rule-changing mutation? The former is A; the latter is B.
It seems some use CAS as pseudo-scientific term for a human social entity. Surely, every human social entity may be called complex and adaptable? Be it a small family business, or an army? And note that continual mutation undermines the concept of system in which there is some pattern, regularity or repetition to be modelled.
System: "a regularly interacting or interdependent group of items forming a unified whole."
In activity system thinking, regular means rule bound interactions. In social entity thinking, might it instead mean merely frequent interactions?
Axelrod and Cohen (http://innovationlabs.com/harnessing_complexity.pdf) defined a CAS thus.
1 A “system” includes one or more populations of agents and all of the strategies that those agents employ.
2 A “complex” system is one in which the actions of agents are tied very closely to the actions of other agents in the system.
3 When the agents in a system are actively trying to improve themselves (“adapt”), then the system is a Complex Adaptive System.
Points 1 and 3 restrict complex systems to ones in which each agent has a “strategy” to “improve themselves” – which implies a human social entity of some kind. Aside from trying to improve themselves, what else makes people members of the system?
Point 2 (interaction) is the defining feature of all systems, be they simple or complex. The simplest of deterministic systems may have many actors/agents, which interact closely with each other, and produce emergent properties.
Again, a Messy Evolving Social Entity (MESE) may realize one or more activity systems, and revise/adapt those systems when needs arise.
Dave Snowden has distinguished complex adaptive systems from other systems by defining them as dispositional rather than linear casual. He might be using those terms in very particular ways, but his definition entangles ideas that merit attention as distinct concepts.
The Merriam Webster dictionary defines a disposition as:
A. prevailing tendency, mood, or inclination.
B. temperamental make up.
C. the tendency to act in a certain manner under given circumstances.
A and B apply to intelligent animals, which narrows the range of systems of interest. C is the more general meaning of disposition used in philosophical discussion of causality, where things have dispositions to act or change in some way when triggered by a cause, by a describable event or condition. E.g.
· A wine glass is disposed to ring or shatter when struck.
· A cooling system is disposed to start up when the air gets hot.
· A person is disposed to shiver when cold.
· A species is disposed to acquire new characteristic(s) when a child is born.
· A democracy is disposed to replace one government by another.
Consider the disposition of a wine glass to ring or to shatter when struck. The cause-to-effect process is non-linear in the sense there are two possible outcomes. The glass "chooses" which to do depending on the nature of the strike, and its own current state. The outcome may be predictable in theory, but unless the strike is hard, it may be unpredictable in practice.
Of course, human dispositions are malleable, and human social entities display innovative causality, because people invent ad hoc responses to events and conditions. Whether an ever-changing social entity is well called a system is questionable, as discussed earlier.
· Adaptation can mean adaptation by state change or by inter-generational evolution
· Agents can be actors who choose between actions in a role, or invent new actions.
· Complex can mean messy/disorderly, or a measure of orderliness. It can refer to a real-world thing, or a model of it. It can be a measure of structure or of behavior.
· Emergence occurs in the simplest of systems.
· Non-linear is used too variously to be discussed here! (See appendix 2).
· Self-organization is used too variously to be discussed here!
· Structures differ from the systems that create and use them.
It has been said that
“A complex system is one that doesn’t behave in a simple linear way (cause A to effect B).” The trouble is that simple structures can behave in ways describable as non-linear, complex, unpredictable, even chaotic. If simple systems meet the definition of complex systems, then, how to differentiate complexity science from simplicity science?
“An adaptive system is disposed to act or change in response to events or conditions.” The trouble is that every activity system is disposed to act or change in response to events or conditions. If every system does that then what is a non-adaptive system?
“A system is a thing contained or connected in some coherent way.” The trouble is that everything from an atom to a solar system is contained or connected in some sense. If everything you can think of is a system, then, how to differentiate systems thinking from any other kind of thinking?
Some talk of "adaptive" and "self-organizing" systems as in system dynamics, in which every agent strictly follows the rules imposed by "the system". Others mean the opposite, empowering each individual to define their own rules, which ends in chaos rather than complexity.
Some shift confusingly from one idea to another. E.g. from phase transition to far-from-equilibrium thermodynamics to the edge of chaos (a debatable idea) to chaos (a mathematical concept).
You might allow “complexity science” to be just a label - a heading for a jumble of ideas from classical cybernetics and other hard science disciplines. However, many in the humanities uses the terminology with much more specific intent. Their CAS is a human social entity.
Again, what many people call a complex adaptive system (CAS) might better be called a messy evolving social entity (MESE) whose members may realize countless activity systems, and change them now and then.
Cilliers is making a good try here … But because of some key omissions, his argument is confusing if not misleading. In particular, it is prone to be misinterpreted, as it does not deal with precision about the underlying technical material. I would not rely on it to teach students about complex systems and the limitations of modeling them. I would certainly not draw any clear, ontological lines between “complicated” and “complex” systems as Cilliers does not do this himself.” Will Harwood
Cilliers’ hedges against defining his terms
“I will not provide a detailed description of complexity here, but only summarize the general characteristics of complex systems as I see them.
Complex systems consist of a large number of elements that in themselves can be
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.
Certain systems may display some of these characteristics more prominently than others.
These characteristics are not offered as a definition of complexity, but rather as a general, low-level, qualitative description. If we accept this description (which from the literature on complexity theory appears to be reasonable), we can investigate the implications it would have for social or organizational systems.”
The fact is, simple deterministic systems can and do produce emergent properties.
Cilliers’ distinction between complex and complicated
“complex is a term we use for something we cannot yet model. If there is nothing metaphysical about a complex system, and the notion of causality has to be retained,
then perhaps a complex system is ultimately nothing more than extremely complicated.”
Cilliers says a jumbo jet is complicated, a mayonnaise is complex. The issue is not the complex/complicated distinction. It is the entity/system distinction. Is every physical entity also a system?
IBM is a system of one kind to its owners, another kind to an employee, another to the taxman. A jar of mayonnaise is a system of one kind to a physicist, another to chemist, and another to a chef. A jumbo jet is a system of one kind to an engineer, another to a pilot, another to a passenger.
As Ashby said, we can find infinite systems in any physical or social entity. And its complexity can only be assessed wrt a description of it. You can change the level to which you (observer) choose to decompose an entity into "parts" - all the way down to quarks if you want.
So, which is easier to disassemble and reassemble – a jumbo jet or a jar of mayonnaise? It is far easier to join two half jars of mayonnaise into one than it is to join to halves of a jumbo jet into one. Unless, that is, you insist the full jar has all the same molecules in the same positions as it did before you divided it. And then, you turn the jar of mayonnaise from Cillier's complex system into his extremely complicated one.
The point is this: the entity (the jar or the jet) is not a system, it is as many systems as you choose to abstract from it. And its complexity can only be assessed with respect to the model you choose to abstract from it.
Linear means in a straight line. A linear cause is a cause that leads to one effect; it implies a straight line from cause A to effect B. A linear line of behavior is a state change trajectory that is a straight line when drawn on a graph. Some use the term linear thinking as an insult, and without any clear meaning.
Generally, the term non-linear means not in a straight line. So, it could imply a line of behavior that is curved or jagged, perhaps an exponential increase or decrease, or a sine wave. Consider price movements in a stock market, or the population of a virus. However, the term non-linear is used with various other meanings discussed below.
Some systems thinkers call the system described in a casual loop diagram linear if it has no feedback loops; and call it non-linear if it shows at least two stocks connected by a feedback loop. Simple systems with feedback loops that display non-linear lines of behavior, can adapt and self-organize, were addressed by cybernetics 60 years ago. Most business information systems are connected in a feedback loop with entities in their environment. E.g. Outputting an order triggers an invoice to be input, which triggers a payment to be output, which triggers a receipt to be input.
This is a discontinuity in a state change trajectory. It appears as a sudden large jump or fold in a line-of-behavior graph. (If the graph is stretched over a very long time-scale, then it might look more continuous.)
The term is ambiguous. In mathematics, a chaotic system is one whose state and line of behavior is highly sensitive to initial conditions. Given different initial values, the system can produce very different outcomes. Or perhaps a system whose state is stuck in a "strange attractor". The behavior of such a chaotic system is still deterministic at the level of a single event.
Less formally, in activity systems thinking, chaos might mean a system's state change trajectory goes up or down in apparently random or irregular ways. In social entity thinking, chaos might mean actors’ activities are messy, not rule-bound, and no pattern can be detected how actors interact (so there is no activity system).
The edge of chaos
The term is ambiguous. Using this phrase can give the impression of something profound when little has been said. Informally, the edge of chaos is a phase transition in a system from a predictable regime to chaotic (but still deterministic) regime. But the phrase has no precise and general definition across domains where it is used. Some use it to describe a human social entity.
Social entity thinkers sometimes use the term non-linear to describe human behavior Obviously, people can perform both deterministic processes, and innovative (spontaneous) processes. Whether human thinking is deterministic or not at some level of biochemistry, we have no option but to treat people as having free will.
Non-linear systems in mathematics (after Will Harwood)
A function relating x and y is linear if y can be expressed as ax+b.
A function f is said to be linear if f(ax) = af(x) and f(x+y) = f(x) + f(y).
A system is linear if it is described by a deterministic linear differential equation.
A system is non-linear if it is described by a deterministic non-linear differential equation.
And a system is non-linear if it is described by a stochastic differential equation.
For more, go to https://bit.ly/2yXGImr.