Kurt Lewin’s quote that “nothing is more practical than a good theory” has been repeated so often that it has become trite. However, few appreciate the complementary implication of this truism. That is, that “the strongest test of a theory is design.” In other words, the ultimate test of a theory is whether it can be put to practical use. In fact, Pragmatists such as William James, C.S. Pierce, and John Dewey might have argued that ‘practice’ is the ultimate test of ‘truth.’
William James was always skeptical about what he called “brass instrument” psychology (ala Wundt and others). In experimental science, the experiment is often ‘biased’ by the same assumptions that motivated the theory being tested. The result is that most experiments turn out to be demonstrations of the plausibility of a theory, NOT tests of the theory. That is, in deciding what variables to control, what variables to vary, and what variables to measure the scientist has played a significant role in shaping the ultimate results. For example, in testing the hypotheses that humans are information processors – the experiments often put people into situations (choice reaction time) where successfully doing the task, requires that the human behaves like an information processing system. Thus, in experiments, hypotheses are tested against the reality as imagined by the scientist. The experiment rarely tests the limits of that imagination – because the scientist creates the experiment.
However, in design the hypothesis runs up against a reality that is beyond the imagination of the designer. A design works well, or it doesn’t. It changes things in a positive way or it doesn’t. When a design is implemented in practice, the designer is often ‘surprised’ to discover that in framing her hypothesis she didn’t consider an important dimension of the problem. Sometimes these surprises result in failures (i.e., products that do not meet the functional goals of the designers). But sometimes these surprises result in innovations (i.e., products that turn out to be useful in ways that the designer hadn’t anticipated). Texting on smart phones is a classical example. Who would have imagined before the smart phone that people would prefer texting to speaking over a phone?
Experiments are typically designed to minimize the possibilities for surprise. Design tends to do the opposite. Design challenges tend to generate surprises. In fact, I would define ‘design innovation’ as simply a pleasant surprise!
So, I suggest a variation on Yogi Berra’s quote “If you don’t know where you’re going, you might not get there.”
If you don’t know where you’re going you might be headed for a pleasant surprise (design innovation).
And if you don’t reach a pleasant surprise on this iteration, simply keep going (iterating) until you do!