What is a system?

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This page is on the general concept of systems and associated ones as well as the use of systems in the context of Cynefin®.


  • system: any network of interactions that has dynamic coherence, - systems may have fuzzy boundaries and shifting patterns
  • agent: anything that acts within the system - may be a person, a community, a dominant narrative, a process, or a rule

The use of systems in the context of Cynefin®

In the world, we find three fundamental types of systems - ordered, chaotic, and complex. At Cognitive Edge, we apply a constraint-based definition of these systems: 

  • Chaos no effective constraints
  • Complex has enabling constraints - in general is connected
  • Order has Governing constraints - in general is contained

Cynefin® then splits order into complicated and clear but the distinction there is not a phase shift - while it is between the three main systems

  • Confused as a Cynefin® domain is in effect a triple point from which vantage, if you are in the aporetic aspect you can, at very low energy cost, make a move into any of the three main types of system; if just confused, you will slip into the one you are most comfortable with.

Systems are defined by the nature of the relationships between the system and the agents acting within it: 

  • Ordered systems: Here the nature of the system constrains the behaviour of agents to make that behaviour predictable. There are repeating relationships between cause and effect that can be discovered by empirical observation, analysis, and other investigatory techniques. Once those relationships are discovered,  we can use our understanding of them to predict the future behaviour of the system and to manipulate it toward the desired end state.
  • Chaotic systems: These are sometimes called random systems, in which the agents are unconstrained, disconnected and present in large numbers. For this reason, we can gain insight into the operation of such systems by the application of statistic, probability distributions, and the like. The number and the independence of the agents allow large number mathematics to come into play.
  • Complex systems: While these systems are constrained, the constraints are loose, partial, and the nature of the constraints (and thereby the system) is constantly modified by the interaction of the agents with the system and each other; they co-evolve."

Central concepts

Equilibrium: Whether a system is near or far from equilibrium is another way of approaching its complexity. Systems that are far from equilibrium are associated with non-linearity and can lead to multiple possible stable states and lead to the creation of new attractors. In this process, small fluctuations can be generated by the system itself or by the interaction with the world beyond the system, since far from equilibrium systems are rarely closed. These fluctuations can be dampened or amplified, both intentionally and unintentionally. This chain reaction makes change harder to predict and implies that small fluctuations can result in large changes. It is also a driver of self-organisation through the dynamics and interaction of the system itself. Conversely, a near-equilibrium system is characterised by uniform and predictable change – a small disturbance will have little consequences. Being near equilibrium also means that change originating from the system itself (unlike the fluctuations we see in far-from-equilibrium systems) is unlikely. A pot of water at room temperature, for example, is stable.

Phase transition: Movements between systems (or perhaps more accurately the movement of a system from one state to another) are phase transitions. This means that they are not continuous and their characteristics radically change. Changes in conditions and constraints can trigger these phase transitions.

Irreversibility: Complex systems and systems that have undergone a phase shift cannot be rolled back to a previous state - the dynamics that drive and create them cannot be undone.


Link to other articles on this wiki if they are relevant.


Specific articles can be referenced here:

Juarrero, Alicia. 1999. Dynamics in action: intentional behavior as a complex system. Cambridge, Mass: MIT Press.

Prigogine, I. (1986). Science, civilization and democracy. Futures, 18(4), 493–507.

Blog posts

Link with commentary