Complex systems

A system is ‘an interconnected set of elements that is coherently organised in a way that achieves something (a function or purpose)’ (Meadows)

‘Complex’ is one of a family of words to describe the relationships between the inputs and outputs of a system or process. The following summary is adapted from the Cynefin Framework (pronounced ku-nev-in).

  • Simple — known knowns. Simple cause and effect relationships. Games analogy: noughts and crosses. Simple rules with predictable outcomes.
  • Complicated — known unknowns. A clear relationship between cause and effect, but it requires more analysis or expertise. Games analogy: chess. The rules are known, the best next move can be calculated but it takes time.
  • Complex — unknown unknowns. The relationship between cause and effect can’t be predicted but deduced in retrospect. Patterns emerge over time. Games analogy: poker. While there are rules, what happens depends on the interaction between the players, and patterns of play emerge over time.
  • Chaotic — the system is turbulent. No clear cause and effect. Games analogy: A child’s game where the rules are being made up as you go.

Role in the Pattern Book

Many organisations are configured to work with complicated problems, prioritising analysis and expertise. But many real-world problems, from supply chain analysis to organisational culture are complex. These require processes based on cycles of:  

  • Data gathering — in particular looking for long-term patterns.
  • Hypothesis testing — using an experimental approach to test different strategies.
  • Learning — building knowledge from experience rather than from assumptions.

Regenerative design is concerned with creating thriving human and living systems, which are inherently complex. This is why complexity is one of the foundational threads that link projects today to how we think about creating thriving in the future.  

This cyclical approach to working with complexity underpins two key motifs in the Pattern Book:

  • Continuous Place-Based Design — taking a long-term approach to designing with emerging properties of a place and its ecosystem.
  • Action Learning — placing action and reflection at the centre of a process of learning about complex systems.

User guide

Meet the systems family

Use this exercise to familiarise participants with the characteristics of different types of system and how to work with them.

  1. Choose a project, strategy or policy you are working on.
  2. Identify which aspects are simple, complicated, complex or chaotic.
  3. Ask where you have been treating situations as complicated when they may actually be complex. 
  4. Consider how you could take a more iterative, long-term and responsive approach.

Conduct small experiments

The best way to learn about complexity is to work iteratively with it — a technique used throughout the Regenerative Design Lab. 

  1. Choose a real system that you are engaged with and identify an aspect that you could experiment with changing. 
  2. What changes could you make? It doesn’t matter how small — the aim is to develop the reflex of testing the system. 
  3. What evidence could you gather about how the system responds to your changes?
  4. Do the experiment. Actually change something.
  5. How did the system respond?

→ see Run Experiments.

Conclusions

Regenerative design requires us to work with complex human and living systems. In turn this requires us to expect uncertainty, to take an iterative, long-term approach to projects and to look for the emergent behaviour of the system — rather than asserting pre-cooked solutions.

Related motifs

Action Learning, Feedback Loops, Heisenberg Uncertainty Principle, Run Experiments

References

Meadows, D.H., 2008. Thinking in Systems: A Primer. Chelsea Green Publishing.

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