Solving design problems with network thinking

I remember a poster on one of the walls of the agency where I worked. It looked something like this:

My boss explained this to me as looking for simplicity on the other side of complexity. This phrase is based on the aphorism of Oliver Wendell Holmes Jr. “I won’t give a penny for simplicity on this side of complexity, but I will give my life for simplicity on the other side of complexity.”

I understood it like this:

Design problems start at the edge of chaos. If you stop solving the problem too early, the solution will remain chaotic. But if you overcome the complexity of the problem, you can get a beautiful and simple solution.

Over time, I came to the conclusion that there are various ways that you can get on the other side of complexity. Among them are linear and networked thinking.

Linear thinking

The most common way to solve a design problem is traditional linear thinking, in which we rely solely on the use of old, organized knowledge and thinking patterns.

Below is a simple example of linear thinking to illustrate it in practice:

  1. During a series of usability tests, you notice that users put a lot of effort into finding the information they want in your application.
  2. Back in the office, you start sketching ideas and arrive at a commonly used navigation pattern.
  3. As always, Dribbble will not disappoint and provide inspiration
  4. You open Sk̶e̶t̶c̶h Figma to design something.
  5. After a couple of iterations, it goes into a development sprint.
  6. After a while, Google Analytics shows that the new navigation is a huge success.

Alas, simplicity is on the other side of complexity!

An idea based on linear thinking is isolated from other ideas, which makes it, at best, limited decision.

This brings us to the second way of solving problems – networked thinking.

Network thinking

The second way to solve problems is network thinking, best described by Anne-Laure Le Kanff in her article:

[Сетевое мышление] Is an exploratory problem-solving approach that aims to address complex interactions between nodes and links in a given problem space. Rather than looking at a specific problem in isolation to find a solution that already exists, networked thinking encourages second-order non-linear reflection to enable a new idea to emerge.

With this type of thinking, problem solving is very similar to a neural network, where an idea can be represented by a node, and neighboring ideas are linked to it by branches. The result is an endless landscape of interconnected ideas that influence and enrich each other.

Neural network with nodes and branches connecting neighboring nodes CC BY-SA

Like linear thinking, you start with an idea, but instead of trying to solve a problem with an already existing solution, you open your mind to explore unrelated topics, thereby allowing new ideas to arise and increasing your chances of accidental discoveries.

To illustrate network thinking, let’s imagine a scenario in which you are trying to come up with a big data navigation system.

  1. During a series of usability tests, you notice that users have a hard time finding the information they need in your application.
  2. Later that day at home, you watch your toddler about to throw the diaper into the bin, but is then distracted by a bug that only became visible to him when he came around the corner.
  3. This gave you the idea to investigate spatial orientation in terms of the navigation problem you identified earlier.
  4. The next morning, you stumbled upon the navigation techniques of the native Polynesians. You will learn that they use the sun, stars, wind, birds and interference from the islands to navigate the ocean.
  5. You have noticed that one of the authors of this article took part in research on spatial user interfaces.
  6. This discovery prompted your team to build on the findings of this research and become the first to successfully implement a spatial user interface instead of a traditional graphical user interface for navigating large data sets.

You don’t know it yet, but this discovery will be a catalyst for Google to buy your idea, opening up a new way of navigating digital spaces for billions of people.

Both approaches are applicable during the design process. Sometimes the goal is not to discover something fundamentally new, but to simply find a reliable and proven solution. This approach is great.

But sometimes some problems require simplicity, which can only be achieved through random discoveries in networked thinking.

Eric Berlow put it well:

In nature, we notice that simplicity often lies beyond complexity – so the more you can zoom out and accept the complexity of a problem, the more likely you are to zoom in on the simple details that matter most.

Millennia of evolution have taught us that reverting to existing solutions is often the safest and best way to survive.

But now a new generation of designers have learned to let their brains wander and explore the farthest reaches of chaos, combining ideas and turning them into solutions that might otherwise remain dormant and unexplored until the next millennium.

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