My family and I recently went on vacation to a big city. On our second day there, we took one of those hop-on-off double-decker buses that show you the main sights of the city. These mass-market tours are useful for getting a sense of the overall shape of the place. At least its highlights — you get a sense for what the main areas are, where they sit relative to each other, distances between things, etc. What they’re not good at is giving you an understanding of the city: what makes it special, its history, why things are the way they are.

When you’re in one of these tours, everything about the city gets collapsed into talking points that can fit into the cadence allowed by traffic. You whizz past neighborhoods and landmarks old and modern. Dates and people blur; context collapses. There’s no sense for cause-and-effect, only facts. “This is the statue of x. It was completed in y date to commemorate the battle of z.” That’s about it — no information about why the battle was fought or why it matters to the overall history of the place. Off to the next landmark.

“What” is easy to talk about; “why,” less so. Yet why is the more important of the two — especially if your aim is to change things. What is effect; why is cause. Designers ought to give precedence to why, but we’re drawn to what. This is because we can point to what. It’s the stuff we include in our portfolios; the stuff other designers fawn over.

A couple of days ago I saw a post on social media that epitomizes this problem. The post had two images: one of a regular airline boarding pass and another of a “redesigned” boarding pass. The redesign was all surface: typographic and layout changes with no signs of understanding of the reasons why the elements in airline boarding passes are laid out the way they are.

There are reasons why boarding passes are the way they are — warts and all. For example, humans aren’t the only audience for boarding passes; they must also be legible to various machines. There are constraints around the systems that generate boarding passes and the machines that print them. None of this was acknowledged in the “new and improved” version.

Redesigning a boarding pass isn’t a simple matter of changing the layout of elements in an Adobe Illustrator artboard. The current boarding pass is a manifestation of particular contextual conditions that have informed its form. You can take a stab at the form without understanding these conditions, but the intervention won’t go beyond an exercise in aesthetics​.

That’s not to say the current state can’t be improved; in most cases, it can. The whys that led to the current what may have changed. New technologies supersede older ones, rendering them obsolete. Legal requirements change. Systems change. Improving things calls for understanding the reasons why things are the way they are. It calls for seeing beneath the surface. Alas, social media doesn’t lend itself to deeper probing. The boarding pass example isn’t unique; hang around designer circles in Medium and you’ll quickly run across unsolicited redesign “case studies.” Most are superficial and naïve.

As a medium, the tour bus establishes the pacing and structure that leads to a superficial overview of the city. Social media’s bite-sized, attention-driven structure also influences the presentation of design decisions. Unlike city tours, I don’t see much value in these drive-by redesigns. They manifest (and reinforce) a common misunderstanding of design as noun, one that ignores the process and complexity that goes into evolving form-context fit.

(Bonus points: replace “design” with “politics” in this post. The structural lack of nuance and substance in social media is a big part of why civic discourse has become so polarized.)

I once worked with a stakeholder who was into numbers. We were redesigning the company’s primary navigation system. We’d planned some quantitative tests on the new structures, but they weren’t enough. Our stakeholder wanted decisions based on hard data. I sensed that this was a call for certainty; a way to dispel criticism of the work, to provide cover in a tough political environment.

Certainty is a tricky aspiration. Design is, by definition, uncertain: You’re trying to give tangible form to a possible future so that you can test it. You go into this knowing that early iterations will be wrong. (Hopefully, they’ll be usefully wrong.) The whole point is to start a feedback loop that leads to something good.

How do you know it’s good? Because it’s evolved through interactions with the real world. You’ve put it (or something that looks and feels and works like it) in front of real people, you’ve seen them use it, you’ve changed it based on their reactions. At some point in the process, you start to develop confidence in the direction. (Only confidence; never certainty.) The early stages are fuzzy. Yes, data can help — mostly, to give you a read on the current context. But data can’t dictate design directions. That requires design intelligence, experience, and craft. (Perhaps the day will come when algorithms can do part of this work, but they’re not here today.) This is what designers call abductive reasoning.

In his book Designerly Ways of Knowing, Nigel Cross introduces the concept by contrasting it with inductive and deductive reasoning:

[Philosopher Charles Sanders Peirce] suggested that ‘Deduction proves that something must be; induction shows that something actually is operative; abduction merely suggests that something may be.’ It is therefore the logic of conjecture…

Design ability is therefore founded on the resolution of ill-defined problems by adopting a solution-focussing strategy and productive or appositional styles of thinking.

I love this image of design as a solution-focussing strategy. It suggests that while the goal is clarity, getting there requires dealing with fuzziness. Ideally, the process moves from an ambiguous state to one that is sharp and detailed. In the course of the project, you get a better sense of what the solution is.

Ann Pendleton-Jullian and John Seely Brown’s Design Unbound describes abductive reasoning thus:

[it] is often understood as a “best guess” hypothetical reasoning — a form of logical inference in which an observation leads to a hypothesis which might explain the observation. The hypothesis can then be tested. In abduction, one is seeking the simplest and most likely explanation, without enough facts for a foothold on certainty.

Designers need to acknowledge that working like this can be scary, especially in projects undertaken under challenging conditions and/or where there’s a lot at stake. It’s scary because there’s a lot of uncertainty in the process, especially early on, and people whose jobs are on the line will want to reduce uncertainty as much as possible. It’s also scary because this requires trusting the people shepherding the process; there’s no cover in numbers. The upside: abductive reasoning can help teams deal skillfully with complex, ill-defined problems. It can lead to a good solution faster than any other approach I know. (Note I didn’t say it’ll lead to the perfect solution — for complex problems, there’s no such thing.)