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.)
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