Every day, we interact with hundreds of things designed for a particular purpose – kitchen utensils, tax forms, shoes, ticket machines, school curricula, mobile phones, websites, water taps, houses, hamburgers, washing machines, laws, and APIs. Some are so good they give us little rush of satisfaction; others are so bad they make us clench our jaws in frustration. Doing good design is difficult, sometimes it is even difficult to recognise. Why is that and how can you get better at it?
Design is difficult because our mental models of how the world will react to our creations are usually too weak to account for all the pertinent aspects of the real world. Either our mental models are lacking or the world is too complicated and uncertain to let us compute predictions. To combat this you can do two things:
- Study to improve your mental models.
- Iterate on your design and test how it behaves in the real world.
These tactics are closely related and complement each other.
A mental model is a kind of internal symbol or representation of external reality, hypothesized to play a major role in cognition, reasoning and decision-making.
In other words, a mental model is a pattern of thinking about how something works or how to do something. You use mental models whenever you make predictions – how a ball in the air will travel or what will happen when you click an “Abort” button. When designing, you use them to predict how the world will react to your design hypotheses
There are many ways to come up with hypotheses: by analogy with solutions to similar problems (in the same or other domains), elimination of possibilities, considering special cases, and several others that Pólya discusses in How To Solve It.
. You develop hypotheses until you find one that you predict will fulfil your goals. Good mental models allow for good predictions and thus good design decisions. It’s important to understand how good your mental models are. If you have poor mental models and you know it, you still have a good chance at good decisions because you know to be careful. Conversely, if you think that your mental models are great when they are poor, you are likely to make costly mistakes.
Despite the value of good mental models, many underinvest in developing theirs. If you are not actively pursuing a deeper understanding of your domain through focused study of books, experiments, and questions, you are doing yourself and those you design for a disservice. But even great mental models are not sufficient to predict how our complicated world will react to your designs. You also need iteration.
To iterate on a design is to repeatedly evaluate one or more aspects of it and use the insights gathered to adjust both the design and your mental models. Each iteration is an experiment on the design hypothesis – a test of how it interacts with the world.
There is no shortage of empirical evidence of or endorsement for the value of iteration: evolution, science, the iPhone, the lean startup movement, rewriting
William Zinsser in On Writing Well: “Rewriting is the essence of writing well: it’s where the game is won or lost.”
, and Boyd’s OODA loop to name a few.
A key advantage of iteration is that it enables you to work with systems – a market, a microbiome, or a group of people – whose behaviour is more complicated than your mental models can handle. Even if your mental models have enough precision (and they probably don’t), reliable deduction is often infeasible.
According to chaos theory, it is impossible to make long-term predictions about systems that are sensitive to initial conditions.
Iterations externalise the deductions and inform your mental model so that you can effectively make progress towards a solution.
The downside is that iterations are comparatively expensive in resources and time. It takes time and money to build something close enough to your hypothesis to let you test it and it takes time to see how it interacts with the world. Random evolution works, but its cost in time and energy is significant. The cheaper we can make iteration cycles, the more we can leverage mental models that are great at local predictions, but less good at predicting how the full complex system will behave. It is not uncommon that iteration speed can be increased by several multiples, with corresponding benefits to the overall design process.
Mental models and iterations are fundamental to the design process and they complement each other. The quality of our mental models, the cost of iterations, and the complexity of the domain influence the best mix of the two tactics.
Mental models can actually be replaced in some design problems where computers can run representatative simulations or tests quickly. The class of design problems susceptible to this approach is relatively small, but growing.
Though iterations are commonly and rightly lauded, the value of good mental models is less appreciated, but just as important.
Thanks to Jan Sramek, Emily Rookwood, and Simon Meier for their feedback on drafts.
George Pólya, How to Solve It: A New Aspect of Mathematical Method (Princeton: Princeton University Press, )
Robert Coram, Boyd: The Fighter Pilot Who Changed the Art of War (New York: Back Bay Books, )
William Zinsser, On Writing Well: The Classic Guide to Writing Nonfiction (New York: HarperPerennial, )