Complexity Basics — Part II: Types of Systems
Table of Contents
1. How to conceptualize the world?
2. Simple, complicated, complex, chaotic and completely random systems
↳ 2.a. How to conceptualize a system?
↳ 2.b. Which system references can be distinguished?
↳ 2.c. Simple systems
↳ 2.d. Complicated systems
↳ 2.e. Complex systems
↳ 2.f. Chaotic systems
↳ 2.g. Quantum mechanical randomness
3. Closing remarks
Two crucial questions have so far not been asked in the context of the Cynefin framework (see part I of this series):
- What is the system reference?
- How is a specific system observed using which distinctions?
However, it is worth specifying the concept of world in terms of complexity first before addressing these two questions because it is often said, especially from a common sense point of view, that the world has this or that property:
- complex
- chaotic
- nonlinear
- uncertain
- volatile
etc. (see, for instance, James Cascio’s BANI-post Facing the Edge of Chaos from April 2020, which in this respect is representative of many other time-diagnostic texts).
1. How to conceptualize the world?
There were and still are many different world terms depending on context / discipline, often referring to some totality.
In the systemic context, this totality view can be radicalized by considering the world as an irreducible ultimate horizon (a horizon of all horizons in the sense of phenomenology). Then one can refer to observers who operate / observe using, e.g., the leading distinction system/environment with an implicitly always running along, but inaccessible world as an ultimate horizon.
Or to put it differently, the observers have no access to the world as such, but always only observe their environments or other observers / non-observing systems in these environments. Consequently, there is not a single environment whose observation is somehow really objective and, thus, valid for all observers.
Since there are many observers, there are also many observed environments that are tolerated by the world. If they‘re not tolerated, one can expect serious consequences (including deaths)!
And only these observed environments or systems / observers in these environments can be meaningfully interpreted as chaotic, complex, etc., because the world itself as the ultimate horizon remains inaccessible!
2. Simple, complicated, complex, chaotic and completely random systems
2.a. How to conceptualize a system?
It follows from the preceding that a system is an observation. In other words, it‘s a construction of observers who resort to specific distinctions for this purpose.
In our context, these are primarily the following guiding distinctions:
- Whole/Part (Note: This is a guiding distinction that has been prominent since Greek antiquity, but no longer makes sense in the case of social systems such as organizations, networks, or swarms, see the Medium post (Social) Complexity Basics: Containers All the Way Down)
- Element/Relation
and
- System/Environment
2.b. Which system references can be distinguished?
Depending on the context, discipline, and theory, sociological, mathematical, biological, etc. observers may distinguish various types of system references with specific key characteristics.
However, my heuristic suggestion for an overview of different system references is the following:
2.c. Simple systems
For example, a pendulum, where an analytical and linear way of thinking is appropriate because the cause-effect relationships are obvious (see also the first context in the Cynefin framework).
2.d. Complicated systems
For instance, Charles Babbage’s Difference and Analytical engines, where deterministic causal relationships are harder to grasp, but analytical and linear ways of thinking allow decompositions into simple parts / systems.
In short, a reductionist way of solving problems often works for these kinds of systems (see also the complicated context in the Cynefin framework).
2.e. Complex systems
A key characteristic of such systems is usually that conventional ways of thinking and problem-solving such as
- Causality and determinism
- Analytical reductionism (which attempts to break down complicated systems into simpler components)
- Linearity
- Traditional statistics
fail. In short, complex systems are either severely impacted or, in the worst case, extinguished when interventions are made that rely on recipes for complicated systems (see also part I of this series: When Simplicity Kills).
Apart from that, complex systems are said to have the following (open) list of characteristics:
- Energetic openness, whereby stable patterns emerge, although these systems are often far from energetic equilibrium.
- A multiplicity of interacting components, resulting in emergent behavior of the system that can’t be attributed to its individual components.
- The possibility of critical transitions as abrupt system changes that some observers may interpret as chaotic.
- The possibility of internal differentiations (nesting).
- Unpredictability of the system due to non-linear relationships and negative (dampening) and positive (reinforcing) feedback loops.
In practice, this means that a small perturbation can have a large, proportional or no effect at all in complex systems.
Etc.
In addition, we can distinguish three types of complex systems, depending on whether they have some kind of memory function and / or a learning mechanism to adapt to a changing environment:
- First, complex adaptive systems (CAS) with an explicit or implicit memory function.
- Human organizations (businesses, schools, hospitals, universities, etc.) have written, nowadays often digital records and documents, as well as implicit forms of memory such as organizational cultures (norms, values, etc.) and routines.
These forms of organizational memory can then be used for adaptations to their observed environments.
- The adaptive immune systems in vertebrates with their immunological memories.
- Ant colonies in which pheromone trails in particular are used as a mechanism for behavioral coordination. That is, the strength of the pheromone trails acts as a kind of implicit memory that allows ants to adapt to changes in their environment. - Second, CAS without a memory function. However, direct real-time interactions can act as the equivalent of a memory function. For example:
- Systems with decentralized and self-organizing swarm intelligence, such as flocks of birds or schools of fish, exhibit coordinated behavior and adapt to changes in their environments, e.g., attacks by predators, by following simple rules, such as keeping a certain distance from their neighbors and matching their direction of movement to that of their neighbors.
- Urban crowds and traffic flows, similar to animal swarms, also lack an explicit systemic memory function.
To tame urban traffic, the computer scientist Carlos Gershenson finds that letting transportation systems adapt and self-organize often works better than trying to predict and control them. [in: Quanta Magazine (2020): Complexity Scientist Beats Traffic Jams Through Adaptation].
- Third, complex, but non-adaptive systems: They neither have an explicit memory function nor the ability to adapt to changes in their environment, similar to human / animal CAS.
- A river delta, for instance, is a complex system made up of many interacting components, including water, sediment, and vegetation. However, the river delta doesn’t adapt to changes in its environment like complex adaptive systems do, although it can be influenced by past conditions (e.g., sediment accumulation).
- A snowflake is another complex, non-adaptive system made up of many individual water molecules interacting through physical forces. But while a snowflake can be complex, it‘s also incapable of adapting to changes in its environment like a typical CAS, which relies either on direct real-world interactions or its history (past experiences) using a memory function.
2.f. Chaotic systems
Sometimes complex and chaotic are seen as synonyms, or chaotic systems are considered as a subclass of complex systems in the sense of complex-chaotic systems. However, it’s better to separate both types of systems because:
- Chaotic systems are based on
- Determinism (that is, the futur behavior of such dynamic systems is fully determined by their initial conditions).
- A small number of non-linear interactions.
- A huge sensitivity to initial conditions so that tiny differences, e.g., due to measurement or rounding errors, in the latter can lead to highly diverging outcomes (the so-called butterfly effect).
In brief, chaotic systems are paradoxa because even though they’re relatively simple and deterministic, their long-term behavior is still unpredictable (random). Therefore, they’re also called deterministic-chaotic systems.
To exemplify this, we take a simple pendulum, which is deterministic (see section 2.c. above), and turn it into a double pendulum, which thus has two degrees of freedom.
The latter is then a dynamic system characterized by deterministic chaos, which the following video illustrates nicely:
- In contrast, complex systems are characterized by
- Non-determinism
- Many nonlinear interactions
- Non-sensitivity to initial conditions
In the case of complex-adaptive systems (CAS), there is also the fact that they can orient themselves on their past experiences thanks to a memory function, or at least on direct real-time interactions as an equivalent of such a memory function (see section 2.e. above). This is also not the case with deterministic chaotic systems.
Therefore, deterministic chaotic systems and complex (adaptive) systems should, in my opinion, be considered as two different types of dynamic systems.
Nevertheless, complex (adaptive) systems can often display emergent patterns and structures, which can be interpreted as chaotic (random). But these would be epiphenomena, related to phase transitions, for example, rather than deterministic chaos.
2.g. Quantum mechanical randomness
Finally, it should be pointed out that quantum mechanical processes are truly random according to current knowledge (but see the German theoretical physicist, Sabine Hossenfelder, on superdeterminism), since they‘re described by probability distributions and not by deterministic equations.
In short, the results of measurements on quantum systems are not determined with certainty, but are described by probabilities (-> indeterminism).
3. Closing remarks
From the above we can extract several takeaway messages:
- First, complex is neither identical with chaotic (in the sense of deterministic chaos) nor with complicated, but it generally makes sense to distinguish between different systems or problem domains.
For example as follows:
- Simple
- Complicated
- Complex — non-adaptive
- Complex-adaptive with implicit, explicit or no memory function
- Deterministic-chaotic
- Completely random according to the probability distributions related to quantum mechanics.
One of the main advantages is then that we escape the law of the instrument (vulgo, Maslow’s hammer, which in this context all too often has only a linear, analytical-reductionist and deterministic-causal form) so that we can solve problems, decide and act in a more diverse way (see also part I of this series).
- Second, it ultimately depends on the observers how they observe, i.e., conceptualize/interpret and model, the respective system in a way that is plausible and understandable for others.
This means, for example, that a mass panic can be studied as a complex-adaptive or as a deterministic-chaotic system or both.
Note:
See also the Medium post Part 3 — (Social) Complexity Basics: An Intro to Complex Systems on how differently social systems can be conceptualized.
- Third, the acronym VUCA is misleading, because the comprehensive term is complexity and aspects such as volatility, uncertainty, ambiguity, non-linearity, etc. are only part of its open list of characteristics (see above).
- Fourth, regarding question 2 mentioned in part I of this series whether VUCA is trivial because we have always lived in such environments, I agree — but this refers primarily to ecological contexts!
However, when we turn to social evolution, this assumption of triviality quickly implodes because societal, media and technological complexity has literally exploded in the last 250 years or so (since the beginning of modernity). But this will be the topic of part III of this series. - Fifth, for an application of the findings of complexity research in business contexts, see, for example, Rick Nason (2017), It’s Not Complicated: The Art and Science of Complexity in Business.
Thanks for reading and, hopefully, see you in the next post Complexity Basics — Part III: The Increase in Socioevolutionary Complexity!
Author for WAITS Software und Prozessberatungsgesellschaft mbH, Cologne, Germany: Peter Bormann — September 2023.
