System coupling patterns (archetypes)

Copyright 2019 Graham Berrisford. One of about 300 papers at Last updated 31/05/2019 18:30


The term “complex system” has been used widely and with various meanings discussed in Complexity explained and related papers.

One kind of complexity can be found in the way systems or subsystems are coupled to each other.

Coupling is the relating of systems, as subsystems, in a wider system.

This paper discusses several ways that systems may be coupled, classified as in the structure below.


Design patterns (archetypes) 1

Structural patterns. 2

Anarchy: Network structure. 2

Hierarchy: Tree structure. 3

Abstracting dynamic systems from passive structures. 4

Communication path patterns. 4

Anarchy: Point to point 4

Hierarchy: Hub and spoke. 5

Delegation patterns. 5

Anarchy: Peer-to-peer 5

Hierarchy: Client-server layering. 6

Process control patterns. 7

Anarchy: Chain/Choreography. 7

Hierarchy: Fork/Orchestration. 7

Concluding remarks. 7


Design patterns (archetypes)

A design pattern is a shape or structure of elements that commonly appears in system design.

It is a tried and tested way to design a system to address particular problems or meet particular requirements.

The patterns in this paper are relevant to both formal software systems and fuzzy social systems


“If you think you have a pattern, you must be able to draw a diagram of it.

This is a crude, but vital rule. If you can't draw it, it isn't a pattern.”

“A pattern defines a field of spatial relations, and it must always be possible to draw a diagram for every pattern.”

Christopher Alexander (1979) in The Timeless Way of Building


OK, but his paper describes patterns in text; leaving the diagrams to companion slide shows.


Design pattern pairs

A design pattern pair is a pair of contrasting patterns that suit different situations.

Architects choose between alternative patterns by trading off their pros and cons.

This paper discusses several ways that subsystems may be coupled, loosely classified and paired as shown in this table.








Point-to-Point or Mesh

Hub and Spoke



Chain or Choreography

Fork or Orchestration


Every structure or system designer has to strike a good-for-the-context balance between hierarchical and anarchical patterns.

Different patterns may be mixed in one design, and used at different levels of system design.

Structural patterns

A system may interact with one, a few or many other systems.

Wherever two systems or subsystems interact, a structural relationship may be drawn between them.

Connecting all the subsystems by these relationships reveals a communication structure, a network or a hiearchy.

This first pair of patterns is highly generic.

It reappears in different guises in the other patterns discussed below.

Anarchy: Network structure

This is a design pattern in which a node can be connected to any other node.

It is anarchic in the purest sense of the term, meaning there is no hierarchy.


Real world networks have been studied extensively.

E.g. the world-wide-web, the internet, energy landscapes, biological (cell-to-cell and protein-to-protein) networks and social networks.

Some have proposed such diverse networks can “self-organize” themselves.


Small world network

One idea is that as a network grows, it tends to minimises the number of steps from one node to another.

Mathematically speaking, in such a “small-world'' network, the number of steps grows in proportion to the logarithm of the number of nodes.


Scale-free network

Another idea is that as a network grows, the number of major “hubs” with many connections increases, along with minor hubs between the major hubs.

The complexity of a scale-free network lies in the orderly patterns it imposes on the chaos of a random network with the same number of nodes.


These ideas may be useful to designers of large systems in which actors or subsystems must communicate.

However, it turns out that scale-free networks are rare in nature and society.

“The universality of scale-free networks remains controversial.

Across [nearly 1,000] networks, we find robust evidence that strongly scale-free structure is empirically rare.

Furthermore, social networks are at best weakly scale free, while a handful of technological and biological networks appear strongly scale free.” Scale-free networks are rare Nature, 2019

Hierarchy: Tree structure

This is a design pattern that divides a node into subordinate nodes.

The nodes or subsystems are related as “higher” to “lower” in what people often call a tree structure.


A rule of thumb for division: divide one into about seven (the Miller number, as many things as people can hold in their heads at once).

A rule of thumb for decomposition: stop at three or four levels (as many bottom level things as a person or team can manage over time).


Federation and subsidiarity (levels of control)

Federation means coupling several distributed subsystems, states or organizations under one central authority.

The central authority is usually seen “higher” than the local, but almost always delegates some authority downwards.


“Subsidiarity” is widely promoted as organizing principle for a federation (like the US or the EU).

The idea is that matters ought to be handled by the lowest level authority with the required competence.

Decisions should be taken at a local level if possible, rather than by a central authority.


In classical cybernetics, this pattern is a technique for organizing a large and/or distributed system.

The idea is to organise the management of an organization as a hierarchy of control-target systems.

When a lower level system detects a state variable has moved out of range (an exception), it passes control to a higher-level control system.

The higher level system returns with a direction to the lower-level system.

See Beer’s ideas for discussion of this idea.


The principle of subsidiarity may help us understand how biological and social entities work best. 

It is about how to regulate behaviour efficiently and effectively, but it is not “self-organising” in the sense below.


Balancing the patterns

In practice, the optimal structure may employ both ideas.

There may be a network structure with each node in a hierarchy.

Abstracting dynamic systems from passive structures

The systems of interest here are not passive structures, they are activity systems.

They contain subsystems that interact with each other and/or with entities in their environment.

In accord with the “primacy of behavior” principle, they are defined by their behaviors or dynamics.

The system is composed of interactions between its constituent subsystems.


Abstracting system interactions from network relationships

Any two subsystems, connected by a relationship in network structure, may interact in several different processes.

So, the complexity of a system in which subsystems interact is a different from the complexity of the network structure that connects them.

A complex network of connections (or acquaintances) may be used for simple interactions.

Conversely, a simple network of connections may be used for complex interactions.


Note, moreover, that actors in one communications network may interact in several different (complementary, cooperating, competing or contrary) systems.

E.g. the people in one city may vote in both local and national government elections - and vote for different parties in each.


Abstracting system interactions from the communication stack

Every communication act between human or computer actors depends on the use of communication stack between them.

When co-located humans converse, they use neurons > vocal chords > sound waves > ear drums > neurons.

When remotely distributed humans communicate via a telecommunications network, the lower levels of the communication stack are hidden from them.

The depth and complexity of the telecommunication stack is irrelevant to most systems of interest that actors cooperate in.

Communication path patterns

Systems (and actors play roles within them) can interact by forces, energy, matter or information.

The last of those, interaction by communication of information, is the main interest in sociology and software sciences.

Should we allow direct communication between any pair of nodes or subsystems, or centralise it in one place or subsystem?

Anarchy: Point to point

This is a design pattern in which subsystems talk to each other directly.

It can be faster and simpler where inter-subsystem communication is 1-to-1 and the endpoints are stable.

But coupling subsystems to each other can hinder change or agility.

Hierarchy: Hub and spoke

This is a design pattern in which subsystems communicate via a mediator.

(In the classification scheme here, the mediator is seen as “higher” than the subsystems.)

This pattern couples subsystems indirectly rather than directly.

It can be good where the endpoints are volatile; but can be more complex and slower.


Consider this example of organizations involved in emergency response.

“The Emergency Response Coordination Centre coordinates the delivery of assistance to disaster stricken countries,

such as relief items, expertise, civil protection teams and specialised equipment.

It acts as a coordination hub between participating states, the affected country, and civil protection and humanitarian experts.”

Each organization has its own command hierarchy but they are called to act in the same situation.

The coordination centre does not command; it doesn’t have authority within any of the organizations.  

It ensures each organization has information about the state of the situation and each other organization.


This analogous to event-driven architecture (EDA) in software design.

By using a central publish-subscribe mechanism, EDA completely decouples the senders and receivers of messages

By contrast, in the human example above, you’d expect the human actors in the distributed subsystems to meet and communicate directly at some point.


The hub centralises communication (rather control) between subsystems.

It can help to coordinate subsystems efficiently and effectively, but is not “self-organising” in the sense below.


Balancing the patterns

In practice, the optimal structure may employ both ideas.

There may be point-to-point communication within subsystems that communicate via a hub.

Delegation patterns

Any system may delegate work to another system, as a gentleman delegates polishing shoes to his butler.

The delegation relationship between two systems may be two-way, or one-way.

Should we treat all subsystems as peers, or arrange them in layers according to the nature of their responsibilities?

Anarchy: Peer-to-peer

This is a design pattern in which any subsystem can request work of any other.

A cyclic dependency between two subsystems is said to create a fragile structure, difficult to understand and maintain.

But some cyclic dependencies are inevitable, especially between the largest or most coarse-grained of subsystems.


Self-regulation (via causal loops that connect subsystems)

“Ashby popularised the usage of the term 'cybernetics' to refer to self-regulating systems” Wikipedia 2017

Here, self-regulation means (as in homeostasis) constraining the range of a variable’s values, or the population of a stock, between upper and lower bounds.


The idea of homeostasis dates back to the 1800s if not before.

It often appears in sociology and social systems thinking discussion.

In truth, many societies do not stay in a stable state, their state and their nature advances continually.

Nevertheless, homeostasis does have an important place in cybernetics and wider systems thinking.


Weiner wrote about the science of control in the animal and the machine.

Self-regulation can be seen in natural systems – as when predator and prey populations are balanced by interactions between them. 

And seen in designed systems – as when a thermostat controls the behaviour of a heater/cooler machine


It may appear that predators and prey are peer-to-peer subsystems, but thermostat and heater are higher and lower subsystems

However, in both cases, there is wider system composed of two subsystems connected by a causal loop.

The asymmetry in the second example is as much in our mind as in reality.

Does the thermostat control the heater? Or does the heater control the thermostat?

Arguably, the “control” lies more in the use of the thermostat by some higher level entity – which manipulates the thermostat’s setting.


Where one system regulates the state of another, this is not a “self-organising” system in the sense below.

The organization of the controlled system is not changed by the regulator.

Hierarchy: Client-server layering

This is a design pattern in which higher subsystems in one “layer” delegate work to subsystems in lower layer.

Given a system structure with co-dependent subsystems, suppose you restructure it to eliminate all cyclic dependencies.

The result will be a hierarchical client-server layered structure (which often resembles what Barry Boehm called a Mosque Shape).

This pattern is widely used to make the structure of a complicated system understandable and manageable.


Client-server computer architecture

·        Programming language,

·        OS,

·        Device drivers and CPU instruction sets,

·        Logic gates inside chips


Client-server network architecture

·        FTP

·        TCP

·        IP

·        Ethernet

·        Cable


Client-server software architecture

·        UI

·        Logic

·        Database


Client-server enterprise architecture

·        Business

·        Apps and

·        Technology


Balancing the patterns

In practice, the optimal structure may employ both ideas.

There may be peer-to-peer communication within each client-server layer.

Process control patterns

A design decision that may recurs at different levels of design.

Should we distribute process control to many places or subsystems, or centralise it in one place or subsystem?

Anarchy: Chain/Choreography

This is a design pattern in which subsystems interact directly to complete a process.

It distributes intelligence about the overall processes

Each subsystem calls the next subsystem in a peer-to-peer fashion.

Hierarchy: Fork/Orchestration

This is a design pattern in which one subsystem schedules other subsystems.

It centralises intelligence about the overall process or workflow.


Balancing the patterns

In practice, the optimal process control may employ both ideas.

The GRASP pattern can help to find the optimal balance.

Concluding remarks

This paper has distinguished several ways that subsystems may be coupled.

To assist in presentation and explanation the coupling varieties are classified in a table.








Point-to-Point or Mesh

Hub and Spoke



Chain or Choreography

Fork or Orchestration


In human society, one “organization” or “institution” may involve all these kinds of coupling.

The aim here is not to present an exhaustive list of design patterns.

However, one aim is to prepare the ground for discussion of self-organization.


Self-organization (mutation)

System change may be divided into many different types starting with:

·        state change: changing the values of given state variables (typically triggered by inputs)

·        behaviour change: changing the variable types or the rules that update their values.


While watching some actors playing a game of cards, you might observe both kinds of change.

·        state changing actions: when actors play their roles according to the rules, they win “tricks” that advance the state of the game.

·        game changing actions: actors stop the game, agree a change to the rules, then play on; this creative act changes the game itself.


When people speak of “self-organization”, they rarely make clear which kind(s) of change they are thinking of.

Consider a system of predator and prey populations, which grow and shrink as a result of their interactions

That system is better called self-regulating than self-organizing.

Since the latter implies changing the populations or how they interact.

It implies adding or removing a population, or changing the rules by which one population affect another.


Ashby rejected the idea that a system can change itself by self-organization.

“One of Ashby’s goals was to repudiate that interpretation of self-organization, commonly held, that a machine or organism can change its own organization.”

Instead he postulated “A new higher level of the machine was activated to reset the lower level's internal connections or organization.”

(Goldstein’s introduction to Ashby’s 1962 treatise on self-organization.)

Ashby’s higher and lower level machines can be seen as “coupled” in a wider or aggregate machine.

But the aggregate machine is only ever partially self organising, since one part of the aggregate machine always drives the change to the other part.


Read second order cybernetics for a deeper analysis of self-organisation.



John Flach tells me, some of these concepts appear in

Handy, C. (1992). Balancing Corporate Power: A New Federalist Paper. Harvard Business Review. 70(6), November–December.

Sage, A.P., & Cuppan, C.D. (2001). On the systems engineering and management of systems of systems and federations of systems. In Information knowledge systems management, 2 (pp. 245–325). IOS Press.    



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