Tag: complexity

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Lessons learning – field notes from the MPA conference

Today I spent the afternoon with around a hundred members of the Major Projects Association at the MPA’s annual conference, exploring how we create a culture of learning in major projects. 

I was there to represent the Get It Right Initiative as one of their lead trainers, using one of our Lego exercises to show how errors arise, and used this to pivot into a conversation about culture.

Some things I learnt today.

The power of switching from nouns to verbs:

Organogram to organising

Deliverable to delivering

Project to projecting.

It brings projects alive and allows them to be seen as a journey of discovery and encounter. 

In my leadership workshops I often talk about how we treat complex problems as if they are complicated. This verb-based language can perhaps unlock how we make this shift to embracing complexity.

Projects as a confluence of paths.

It is traditional to think of projects as having a start and an end. But if we think about it, it’s hard to pinpoint exactly when a project really started and when it really ends. Instead we can think about it as a convergence of paths: people, individuals, organisations, circumstances converging to make something happen. 

This approach is much more in line with the concept of continuous place-based design which we teach at Constructivist. 

Lessons uploaded

We were shown the tremendous power of AI for learning lessons from project data. If the knowledge is given the right architecture then the depths of projects can be plumbed for unseen lessons.

And there is double looping here. Using AI agents to sift and quality assure the process. 

And yet…

There seems to be an inevitability about complexity. Our projects will inevitably become more complex. So we bring in complex AI models that are hard to understand and make things even more complex.

I can’t help thinking that all of this complexity is a substitute for elegant thinking. Sure: the living world is complex, but it is kept in balance through the tiniest feedback loops running on the minimum of energy. 

Which makes me worry we are heading down the wrong path. Instead, why not:

Simplify first:

Clarify outcomes.

Gain consensus.

Don’t start too soon.

Simplify the supply chain.

Do a small bit and see what happens, rather than create grand edifices the depend on the whole thing being complete to work.

And then keep it lean:

Look at what is needed to keep each part of the process flowing.

Use all our senses to see what’s going on.

Change course based on local feedback.

Get all of that working first.

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On pattern spotting

Pattern is a word I use a lot. Recently, a reader wrote to say how much they appreciated this use of pattern language in my writing. And that made me pause today and think about why patterns matter so much to me. 

A book I regularly return to, usually towards the start of the summer holidays, is How to Read Water, by Tristan Gooley. In this fascinating guide, Gooley shows us how to understand all the complex things that are going on in a body of water by reading the patterns. 

In fluid mechanics, we can study the bulk properties of water flowing down an idealised channel – its velocity, discharge and whether it will be smooth-flowing or turbulent. Equations give us the means to predict overall behaviour.

But stand on a real river bank and we will find it much harder to predict the detail of what is going on. Sure, the big numbers stay the same, but the detail becomes impossible predict – where an eddy might suddenly appear and then dissolve; or where a submerged stone might set up a standing wave. Multiple factors interact to create a system that is too complex to predict. 

When faced with this sort of complexity, we stop seeking to predict the detail and instead learn to read the patterns, and what these can tell us about the underlying system. That’s what Gooley’s book does so well – gives us patterns to look for that help us understand the underlying structure and behaviour of the water we are looking at. 

Patterns show us what the system is trying to do. Its tendencies, what is reinforced and what is absent or removed. They show us the most likely, energy-efficient response to a set of conditions. 

Complexity emerges in systems with lots of connections and lots of interlocking factors. And so, straight away, we tend to see complexity whenever we are working with ecosystems, communities and organisations – in other words, in the work of regenerative design.

Patterns are a key to working with complexity. And pattern spotting is a key skill.

Spotting patterns doesn’t necessarily mean we need to copy them. Rather, patterns are clues to what is going on so that we can choose the best response to this complex system. 

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The wrong (moment to put on your waterproof) trousers

This is a post for the cycling decision-makers among you. It may resonate even if you don’t cycle. Variations on the question of whether, if it starts raining when cycling, it is worth stopping to put on your waterproofs.

How late am I running? Have I got time to stop? How heavy is the rain? Will it carry on? How quickly could my clothes dry? Will I get wetter stopping to put them on?

If I do decide to carry on, is it wetter to go quicker or slower?

Do I have all the facts? Do I know all the unknowns? Is this a complicated or a complex problem? Am I able to make a good decision? 

Is there an angle I can cycle at in which my rain shadow protects my lower half sufficiently? 

Is how I’m framing the question limiting the result? What opportunities am I not considering? If I stop at a random location to put on my waterproofs, what might I notice that I might never have discovered had I ploughed on?

What happened last time? Was it the right decision? What are other people doing? What would my future self advise?

Am I even in the right frame of mind to make this decision? What could I be thinking about instead?

What happens if I get it wrong? How much does it matter to me if I get it right? Am I deluding myself that I’m in control? 

[This post was originally published on 28th September 2024 on eiffelover.com]

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The past, present and future at the same time

Diagram of the Three Horizons model with three overlapping curves labeled H1 (red), H2 (blue), and H3 (yellow), showing how different patterns rise and fall over time.

In conversations about regenerative design we draw heavily on Bill Sharpe’s Three-Horizons Model because it allows us to make sense of a complex situation. In any group of people collaborating on a project it is possible to find people who are managing the decisions of the past, some who are dreaming about the future and some who are thinking about what we should do next. 

This co-existence of past, present and future so beautifully showed up for me recently as a parent, watching our daughter manage the transitions of the present, dreaming about her grown-up plans for the future, and still wanting the care of a younger self. 

And now I am thinking about it, I recognise these different voices, with needs and hopes, from different times, co-exist in my adult head too.

The power I see in Bill’s teaching is to recognise and welcome all three of these voices at the same time. Last week I wrote about chaos and looking for the signal in the noise. But when we can start to recognise that there are three (or more) things going when we encounter any change, we can start to make more sense of the signals we are working with. 

The future, present and the past are always with us. Recognising them can help us work with them to reach design decisions that are the best next step. 

The Three Horizons model is part of our Tools for Regenerative Design collection.

Explore frameworks like Three Horizons, Systems Bookcase and Living Systems Blueprint — practical ways to bring regenerative thinking into everyday design practice.

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Go (notes on complexity)

My favourite board game is Go. A 19 by 19 board. White stones versus black. You win by surrounding your opponent’s stones before they surround yours. The game has just three rules, but from this simple concept a game of incredible complexity emerges. 

My early years of playing Go were frustrating: it didn’t matter what I did, I couldn’t find a way to win. And now that I am more experienced, I find it hard to teach others. I take solace therefore that while the first computer to beat a reigning chess world champion (Deep Blue versus Gary Kasparov) did it in 1997 it took another 20 years for a computer, Deep Mind to beat reining world Go champion Ke Jie. 

The reason Go is so much harder for a computer to play than Chess is the number of branching possibilities that emerge from each move. It is just not possible to play solely on the basis of the player assessing the opposite player’s best move. And therefore a much more complex dynamic emerges in the game that involves the players ability to spot patterns as much as the patterns themselves. 

I find this fascinating. In this complex situation, the players are part of the solution. Or put it another way, the solution is function of both the physical reality (the stones on the board), the players’ perception of the stones, and the players’ perception of each other’s perception of the stones. In maths terms, the solution y = f(physical world, internal world).

It highlights for me that with complex situations in which engineers (and other humans) are agents, how we show up and how everyone else is showing up has a big impact on the outcome. We are a long way from optimum answers that can be deduced from calculation.

This post originally appeared on eiffelover.com in September 2024

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Design as a learning process

Many projects treat design as a problem with a fixed answer. But what if we treated design as learning journey?

In a complex world, design needs to be a responsive process. This means observing, setting intentions, developing ideas, testing them and seeing what happens. With each iteration of this process we get a better understanding of how the system responds to our thinking — and how our thinking needs to respond to the system.



  • This mindset encourages us to:
  • Respect complexity
  • Work iteratively
  • See value not in a single deliverable, but in insight that accrues over time

It’s hard to argue with that. And yet many design processes are linear and short-term.

But just think about what long-term value could be unlocked by shifting from merely delivering an answer toward building understanding.

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It’s hard to win at poker by playing chess

Complicated systems are like chess: we know the rules, and with some calculation, we can work out the best possible move.

Complex systems are like poker: we know the rules but there is much more to how the game will unfold: the relationships, how people have played, patterns emerging.

Chess and poker require different strategies.

Engineers are often trained to solve complicated problems. We calculate outcomes, we stipulate procedures, we aim to control.

But the systems we are engaging with are often complex. Complexity requires different approaches: cycles of observation, action, reflection and updating plans.

And yet, it often feels like we use complicated strategies for complex systems.

It’s hard to win at poker by playing chess.

This distinction between complicated and complex systems draws on the work of David Snowden and the Cynefin Framework

For more on this topic, see the entry for Complexity in the Pattern Book for Regenerative Design.

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The Seven Levels of a Forest Garden

The following I learnt from Steve Watts, permaculture expert, during a talk he gave about Forest Gardens at the wonderful Coedfest, which he co-leads.


In a forest garden, plants and trees are layered over one another to create a growing system that is far more productive and diverse than farming a single crop on an area of land.

By stacking layers of complementary plants and trees over one another, you increase the amount of carbon that is being locked in, you increase the leaf litter that falls and so you increase the richness of the soil.

As Steve describes we can think of a forest garden has having seven layers:

  • High trees,
  • Lower trees,
  • Tall shrubs,
  • Small shrubs,
  • Ground cover,
  • Plants in which we harvest the roots, and

  • Climbers.

Each of these layers can produce food at different times of the year, and each of these layers provides a complementary role to the others. It is a beautiful model for a stacked system that is creating life. And it is a reminder that when we bring different systems together in complementary ways we can create much greater richness and diversity than we keep things separate.

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Tools for telling the future

What began as a conversation this week on the blog about how designers predict the future has unlocked some deeper reflections on how we approach regenerative design.

Let’s rewind.

As designers, we are always concerned with the future. Our job is to imagine how things could be and shape the conditions to get there. To do this, we rely on two types of indicators:

  • Lag indicators — evidence of what has already happened. The results of past design decisions. 
  • Lead indicators — signals in the present that suggest how the future will unfold.

When conditions are stable, precedent (ie lag indicators) can be a reliable guide to the future. But in changing, complex systems, the past is no-longer such a reliable guide to the future.

In these situations, rather than predict the future directly, we can try to assess the capacity of the system we are working with to successfully respond to change.

Capacity to change — a key regenerative lens

In regenerative design we use the living world as a template for understanding how to create systems that thrive. Thriving ecosystems adapt continuously to shifting conditions. This capacity to change is a key characteristic of living systems — and is a guiding principle for engineers (and other humans) thinking about how to create thriving systems. 

In the Pattern Book for Regenerative Design, we go on to define four factors that indicate a system’s capacity to adapt:

  • Building blocks that can easily be recombined.
  • Coexistence of diverse variations to allow for different responses. 
  • Feedback loops that reinforce adaptations suited to current conditions.
  • Mechanisms for retaining and repeating what works.

From analysis to a design brief

These four factors are both analytical prompts and design levers.

When we encounter a new situation, we try to establish the extent to which each of these is present and use this as a measure of the system’s capacity to survive and potentially thrive through change.

And they can be used as design requirements, giving us factors that we can build into a design brief to create a brief for thriving. 

In a complex situation it is hard to predict the future — instead, regenerative designers seek to make things better by building in the capacity for people and ecosystems to respond together to changing situations in a way that creates thriving for the whole system.

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Seedling analogy – working with what is emergent

Seedling analogy - picture shows a sweet chestnut emerging from a tree tube

The aim of conservation work at Hazel Hill Wood is to help accelerate the diversification of the woodland from commercial forest to a mixed-leaf woodland. Why? Because the more varied the woodland, the greater its likely resilience to a changing climate. Of course, in the normal course of things, the make-up of species in the wood would adapt in response to the changing climate. But these aren’t normal times. Human-induced climate breakdown is causing environmental conditions to change faster than the trees can respond. And so part of our work at the wood is to help accelerate this diversification process. 

Continue reading “Seedling analogy – working with what is emergent”