Designing, Fast or Slow?

Abhinav Sharma
5 min readDec 21, 2016

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Designers and their companies struggle to avoid the engagement trap. This isn’t because they’re idiots but because metrics can reflect usage that comes from one part our brains, while the user’s decision of what makes your product worth their time comes from another. Distinguishing these can be useful.

Daniel Kahneman’s Nobel Prize winning work, accessibly described in Thinking, Fast and Slow, is not only a fascinating standalone read, but is also extremely applicable in day to day work in software Product Design. It explains these two parts and how to tell which one is at work. If you haven’t read it, you really should, but let’s face it, you don’t have the time right now, so stash that for later. Look at the image below:

As surely and quickly as you saw that the young woman’s hair is dark, you knew she is angry. You sensed that this woman is about to say some very unkind words, probably in a loud and strident voice. A premonition of what she was going to do next came to mind automatically and effortlessly. [This] was an instance of fast thinking.

Now, look at the following problem:

You knew immediately that this is a multiplication problem, and probably knew that you could solve it, with paper and pencil, if not without. A precise solution did not come to mind, and you felt that you could choose whether or not to engage in the computation. If you have not done so yet, you should attempt the multiplication problem now, completing at least part of it.

You experienced slow thinking as you proceeded through a sequence of steps.

You just saw the distinction between the kinds of thinking these two systems trigger, and as designers when we put something up on a screen, we’re making the active choice of what we trigger in those who give our products their attention. To keep the basics of System 1 and System 2 in the back of your mind as you read this, here’s a handy chart:

Products are designed to bias towards triggering System 1 or System 2, and no, System 1 isn’t always for the worse.

And your choice as a designer is not just in the output of what you show your user, it’s also in your choice of input.

There are countless examples, and they extend beyond images, into video and in particular music — ever notice the catchy jingles that transition you to ads on the radio? Again, System 1 isn’t bad, and we wouldn’t make it through the day alive without engaging it, but it is a more primitive part of our brains. It is subject to many cognitive biases and shortcuts, and vulnerable to spending substantial time using a product before realizing if it’s valuable to them.

A short case study is Facebook. Images are boldly presented. Headline space on articles is limited so publishers write to provoke. Text is truncated. Reactions are highly emotional.

On the other end, sites like Hacker News deliberately limit what media they allow. Forcing the use of text is one of the simpler ways to ensure mindfulness on the product.

But then, there are incredibly powerful hybrids that take advantage of both system 1 and system 2, and they’re not a new artifact, they’ve been around for centuries. For example, Charles Minard’s visualization of Napoleon’s March to Moscow and back presents dense, nuanced information using elements that system 1 can grapple with, like the change of color to denote the direction, the width to intuit the size of the army, etc. And it also invites you to look at the charts, slowly dive deeper into the hard numbers and reference other sources. A modern day example would be some of the data visualization work the New York Times does.

Now, where it gets particularly interesting is when your product is personalized and a carrier for user generated content. For example Facebook, or Twitter. With user generated content, is the choice about which system to engage yours or your users’, or both? Beyond the decision of what artifacts to give real estate to, designing the interfaces to collect and reinforce signals of user behavior is the real heart of it. Facebook, YouTube, or any personalized machine learning based product shows people more of what they react positively to.

If you as a designer really care about the holistic user experience, what you reinforce is your responsibility. Is it good if someone clicks through on an article, or is the time they spent reading it what really matters, or maybe, or is it whether they come to regret the time they spend reading? Is your goal that they built trust in your brand? Or is it another goal, requiring a different balance?

Making sure you’re able to collect the right product signal, indicating the right user experience is half the battle. This gives your ranking systems have a clear objective to maximize. Now that you have the signals, the second half is which signal you as a company choose to reinforce.

Often, the System 1 signal is easier to collect, and it’s easy to get trapped into optimizing what we can easily measure. Only you and your team can say what mix of these systems is optimal for your product, but the lens is helpful for sorting out the purposes and uses of different kinds of features and designs.

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Abhinav Sharma

Product Design at Quora. Previously Search, Machine Learning, Data at Facebook.