Next to sex, feeding ourselves involves one of the more complex, irrational decision-making process we engage in. But those on both sides of the political aisle as well as urban advocacy groups and health and food industries seem to have found themselves arguing over statistics as determinators of the obesity epidemic in urban areas. A massive effort in the last few years, and millions of public dollars have been spent on eliminating food deserts with the hypothesis being that if there were whole, healthy foods available, people would automatically gravitate towards those options instead of quick, fatty fast food. A study published by Health Affairs reported: "The present findings suggest that simply improving a community’s retail food infrastructure may not produce desired changes in food purchasing and consumption patterns."
We made a billion-dollar bet on the wrong interpretation of statistics.
Why do we keep making these mistakes? Why do we spend our creativity and compassion, not to mention our time and money, on the wrong definition of the problem? Here are my three reasons:
1. We want to believe we're dealing with one problem
It's much more mentally appealing to believe that urban obesity can be drastically reduced by simply reducing the number of food deserts. That sounds simple and appealing. This is why the first step of design thinking is to get away from numbers and reports and studies of any kind and search for real humans with crazy, complex stories. When you hear those stories, you can begin to discover the true nature of the problem within a problem.
2. We gravitate towards problems defined in numbers
We stand to be the most creative when we are called on to generate ideas from a unique and fairly precise point. So, if you haven't listened to stories from users−in this case, those in urban areas who are obese−then the only inspiration you'll have for your creativity are statistics. To that end, designers build a Point of View statement based on those human interactions. They aim to meet a unique human need rather than change statistical percentages.
3. We offer pathways to specific outcomes
This seems logical. Why would I start something if I didn't know exactly where I was going? Except that everything we do−federal policy and one community garden on one street in one urban neighborhood−operates in a closed system. There's no way to anticipate all the consequences of an action. This is why prototyping and testing are so fun for designers. Using terms like 'prototype' isn't giving ourselves license to fail, it's directing us to the imperative of constant learning. Instead of promising outcomes, why don't we invite a diverse set of collaborators to ask a more beautiful question?