Intro to systems thinking for designers

The film Mindwalk and the book The Turning Point by Fritjof Capra switched me onto systems thinking. When I studied it formally I was surprised by the overlaps between the theoretical concepts of systems thinking and the practical methods that I was using in my work as a user experience designer. When a colleague asked me to explain systems thinking I realised that I didn’t have a simple explanation. Here are a few systems thinking concepts (as I understand them) that I find helpful in my design work.

Boundaries

What is the difference between a human rights system and a transportation system? When thinking about a transportation system it is easy to name what is part of the system and what is not. When thinking about a human rights system the boundary of the system is not that clear. But what is similar for both systems is that we make decisions about what to include and what to exclude in our understanding of the system. These are called boundary decisions, it is a key concept in systems thinking.

Systems are models

Systems only come into view when we make boundary decisions (implying personal responsibility), they do not exist ‘readymade’ out there. We make decisions based on a range of emotions, values, and prejudices shaped by our individual backgrounds and contexts, hence no system can be thought of as objective. This explains why there are more than one ‘system’ for understanding and improving climate change and other wicked problems. Systems are models created by people to understand situations better, to either exploit or improve them.

Systems thinking is design

Systems thinking is a method of arranging named phenomena to learn, understand, and plan action to improve something – it’s an opportunity to design better systems (models).

A response to the question: What is Design? could be:

“Oh, so design isn’t about this pixels thing. It’s about systems thinking.” I’m a systems thinker. “Oh, so it isn’t just about the appearance.”

The quote is from an interview with John Maeda on why senior executives must understand design.

Testing systems

There are many schools of thought in systems thinking. Understanding and experimenting with systems thinking is not easy, it’s the reason why it has failed to get traction outside academia. I do think that systems thinking provides useful conceptual frameworks for designers. Maybe systems thinking should take its place in the Design toolbox, alongside design thinking and user centred design – methods that are successful because they make it easy to experiment with, and test your models (systems) in the real world.

For a short introduction to systems thinking read the How-To Guide by Daniel Kim.

Google Ventures Design Sprint

We ran our first Google Ventures design sprint recently. It’s a 5 day sprint where we work together with our clients to shortcut:

the usual endless-debate cycle and compress months of time into a single week. Instead of waiting to launch a minimal product to understand if an idea is any good, teams get great data from a prototype.

Design Sprints are versatile, they can be used to explore almost any idea:

the first version of new mobile apps, to develop new features for existing products with millions of users, to define marketing strategies, to design reports for medical tests, to create the personality of a hotel delivery robot – the list goes on.

The Process

Sketching ideas on Tuesday

Choosing the best ideas and refining them on Wednesday

What we learnt

  • It’s good for us because we get to put our skillset to the test in one week. From creating personas and journey maps to prototyping and usability testing, and we’re doing it with our clients joining in. It clears the cobwebs and makes us think on our feet again.
  • It’s good for clients because they get to test a concept in five days. Investing in a one week sprint is a win-win for clients because bad ideas can be discarded or changed, and good ideas can be validated and iterated faster before writing code.
  • Committing to strict timeboxing improves productivity. By the end of the week we were in agreement that we had achieved much more than we usually do.
  • It’s OK to admit that you don’t know the answer. Provided that you keep exploring ideas that you can test. And being in a group that admits to vulnerability builds trust which is essential for teams that do good work.

Making a prototype on Thursday

User testing on Friday

  • Client participation is key. Although we could have run the sprint without clients, the consequence would have been designs based on our assumptions and incomplete information, which means we lose the ability to move fast in the ideation phase.
  • Don’t try to do everything. Knowing which parts of the idea to test on Friday will help prioritise where to focus the ideation phase. And it protects the group from being overwhelmed by the enormity of the task at hand.
  • Clients have good ideas. And they sketch them out much better than they think.
  • It sets up a project with the right focus. By testing with customers and iterating early on, the principle of continuous improvement is built into the process. The distinction between evidence and assumption is understood by everyone, and the team is less likely to implement designs that have not been tested with customers.
  • No need for lengthy documents. Stakeholders are part of the exploration team and contribute to all design decisions. And by co-creating the prototype and the test plan they know what is going on and why.
  • Don’t shortcut the process. If ideas flow on Day 2 don’t be tempted to jump ahead and start Day 3, allow ideas to incubate overnight and start again the next morning.
  • It builds design stamina. The process is both exhausting and exhilarating, because you have to continually make decisions and justify why you are making them. Developing this skill makes everyone better designers.

After the sprint: analysing the test results before planning what to do next

There is nothing complicated about running a design sprint. So why aren’t we doing it more often? Maybe because doing the simple things are the hardest things to do. Try it out, chances are you may not want to go back to the old way of working. We don’t.

Icons from the Noun Project: Márcio Duarte, Konrad Michalik, Takao Umehara, Ainsley Wagoner, DTE MEDIA.

Perspectives on Innovation

What exactly is innovation? I often hear clients say that an approach is not innovative enough, or that their business needs to be more innovative. What I’ve come to realise is that innovation means different things to different people.

This started me on a course to find language and frameworks to make sense of innovation. The Second Machine Age: Work, progress, and prosperity in a time of brilliant technologies describe two schools of thought on innovation that I find useful:

Innovation-as-fruit

An invention like the steam engine or computer comes along and we reap economic benefits from it. Those benefits start small while the technology is immature and not widely used, grow to be quite big as the GPT (general purpose technologies) improves and propagates, then taper off as the improvement – especially propagation – die down.

Innovation-as-building-block

Another school of thought, though, holds that the true work of innovation is not coming up with something big and new, but instead recombining things that already exist. [emphasis mine]

Digital innovation falls in the innovation-as-building-block category:

Waze is a recombination of a location sensor, data transmission device (that is, a phone), GPS system, and social network. The team at Waze invented none of these technologies; they just put them together in a new way.

The book explains that we are currently experiencing an explosion of building blocks and as a result possible combinations and re-combinations. Due to Moore’s Law the building blocks (data, sensors, and software) are getting cheaper and abundant, but most importantly, digital building blocks cannot be used up. This creates an interesting conundrum:

… the only thing holding us back is our ability to work through all the combinations to find the valuable ones.

How do we find valuable combinations? An approach could be to design and run ‘low fidelity’ experiments to learn which ones work and can be implemented. This could be a first step for organisations that want to be more innovative. There is a method behind this called the Principle of Optionality. I’ve written about it before in The Planning Fallacy.

Paradoxes of the second machine age

Among the many interesting concepts in The Second Machine Age: Work, progress, and prosperity in a time of brilliant technologies, three stand out for me: intangible assets, productivity and employment, bounty and spread. A big driver in all of them is digital technology.

The rise of intangible assets

Production in the second machine age depends less on physical equipment and structures and more on the four categories of intangible assets: intellectual property, organisational capital, user-generated content, and human capital.

The decoupling of productivity and employment

Productivity continued its upward path as employment sagged. Today the employment-to-population ratio is lower than any time in the last 20 years, and the real income of the median worker is lower today than in the 1990s. Meanwhile, like productivity, GDP, corporate investment, and after-tax profits are also at record highs.

The bounty and the spread

Thanks to technology, we are creating a more abundant world – one where we get more and more output from fewer inputs like raw materials, capital, and labor.

But not everyone is a winner, it seems that ultimately there may be more losers in the future if disruptions continue along current trajectories:

It is also an engine driving spread, creating larger and larger differences over time in areas that we care about – wealth, income, standards of living, and opportunities for advancement.

What does it mean?

Should we be concerned that increased productivity does not lead to more jobs, and that higher profits are shared by fewer people? Or are these first-machine-age concerns? I suspect that we need new ways of thinking about second machine age challenges. On the flip side, an abundance of good things mean that we have more opportunities to improve ourselves than ever before.

But ultimately, are we ready for what’s to come? We are on the verge of even greater disruptions because eventually Moore’s Law will outrun many of our notions of what’s possible, and what is not. Maybe we should resist the temptation to predict the future and focus instead on using the new abundance to tackle wicked problems.