One could argue that using “eschew” in the title of a post is missing the point of “simple” entirely. While you could make that argument, it’s a word that suits my purposes admirably. In recent years, it has even somewhat come back into fashion, although according to Oxford it’s real heyday was back somewhere in the 1840s, after which people seemed to eschew using “eschew.” Which is sad, because it’s a lovely word, and rolls off the tongue in a delightful fashion. And as my father always said, “Never use a big word when a diminutive one will do.”
Pleasure in saying the word notwithstanding, what “eschew” means is not about discouraging or limiting use of something. It’s stronger than that. The word derives from the French word “eschiver,” and its origins are ultimately Germanic; “scheuen” means shun. Eschew, then, is about avoiding completely; about banning or shunning. You eschew something that you find morally wrong.
This brings us back to “simple.” Because I’d like us to be eschewing simple as a general principle of being, at least in the vast majority of instances.
I wrote last week about embracing complexity. It was prompted based upon the very real recognition that many of the problems that we are dealing with today—organizationally, in society and personally—are difficult, awkward and messy. They require complex thinking, drawing on multiple sources, and bringing together ideas from multiple fields and disciplines, in order to sort out. There are no easy paths through, and there are no cause-and-effect levers that by pulling on them will make the problems go away.
Despite the truth of that, we want simple. We like simple. We crave simple. It is, arguably, one of the reasons that we wind up with the politicians that we do; especially now. Confident—if misplaced and misleading—assertions that they have all the answers and that they alone can fix our ills is appealing. Surprisingly, for a theoretically literate and intelligent society, the idea of “trust me” has a dangerously draw.
Part of the appeal of simple, I suspect, is that we know the situation is difficult. If someone has figured out a shortcut, we’re all for it. Build a better mousetrap, and the world will beat a path to your door. It’s also true that technological advances have solved previous challenges. We put man on the moon (before we lost interest in the moon as a destination). We can fly to the other side of the world in less than a day (before that got awkward). Vaccines eliminated horrific diseases (before that got weird). There is absolute hope that technology is going to give us a deus ex machina out for many of our challenges, and that we won’t have to make the hard moves that solving them really requires.
Another appeal to simple comes from how we are wired. I’ve acknowledged before that we are cognitively lazy. This is an adaptive evolutionary principle to stop our brains melting down by trying to hyper-analyze every situation to find the perfect rational solution. The work of Daniel Kahneman and Amos Tversky gave us System 1 and System 2. System 1 likes easy answers, and spends much of the day making gut, intuitive, rapid decisions so we don’t have to think more deeply. System 2, by contrast, is our rational decision maker, saved for special occasions when thinking really matters.
Even when we think we are using System 2, however, System 1 is bouncing on its toes at the edge of our thought processes, shouting “Pick me! Pick me!” One of the principles that Kahneman and Tversky highlighted was that of substitution. When we don’t readily find an answer to a complex situation, our minds actually substitute the hard question with an easier one. Rather than doing the work, we take the easy, plausible answer and accept that is true. “How are you feeling these days?” is a question that requires thought and nuance; far easier to answer with how you are feeling right now.
While substitution may help us to avoid awkward conversations that we don’t want to have about our current mental state and the experiences of our inner worlds, that doesn’t help us when it comes to real problems of life. We make token efforts, feel virtuous for having done so, and carry on with our normal behaviours and actions, confident that we are making a difference. In the meantime, challenges remain to confront us at a future day.
One of the substitution that is happy to help us along with this is conflating the ideas of “simple” and “simplicity.” These are not the same thing, although they share the same word root. Simple means what it says on the tin: easy, plain, basic, uncomplicated, presenting no difficulty. Simple problems exist, unquestionably. By their nature, they are easily resolved. The domain of simple problems is also the domain of “best practices.” The laws of cause and effect are present, observable, and offer ready solutions. It’s easy to like simple problems, because they give us ready wins. Fill the car with gas when it runs out, eat when you are hungry, use a three-act structure to tell a story.
“Simplicity” is a quality with a great deal more going on. On the face of it, the ideas of simplicity are similar to those of simple. But the two essential interpretations (at least for our purposes here) are: “the quality of being easy to understand” and “the quality of being uncomplicated in form or design.”
Let’s start with the idea of being easy to understand. As we’ve acknowledged, there are a lot of difficult and complex goings on in the world. The law of thermodynamics is hard. Macroeconomic theory is tricky. For that matter, microeconomic theory is no cakewalk. Nuclear fission is a messy process, and cleaning up depleted uranium is messier still. Rocket science is… well, rocket science. Although arguably rocket science is simply the applied principle of rubber bands, combined with a sufficient mass of high explosives.
For those paying attention in the back seats, what I just did is make a relatively complex idea very simple. While we might not know the thermal properties and relative densities of liquid oxygen, we understand what a rubber band is, and how to shoot one. By using an anology, a difficult topic is made understandable. Ignoring the complicating factors of engine and nozzle design, gravitational pull and the challenges of supporting human life in a vacuum, I have used simplicity of explanation to make an inaccessible topic a little more understandable.
One of the concepts behind this is the process of learning developed by the physicist Richard Feynman (and rather unsurprisingly called the Feynman Technique). What Feynman posited was that if you wanted to learn a subject, you should pretend that you are teaching it to a 12-year-old child. Trying to do so will identify gaps in your learning and understanding. Go back to your source materials, research broadly, build your understanding, review what you have and simplify your explanation. It’s a wonderful tool to demonstrate that complexity of understanding is required to support simplicity of explanation.
Simplicity is also what is created when we use models to understand and explain ideas. Whether your model is the Eisenhower matrix of distinguishing urgent or important, the Competing Values Framework of organizational culture as a product of orientation and control, or Lewin’s model of organizational change by unfreezing, change and refreezing, you are applying a useful representation of a complex topic. Behind the boxes and lines of the model, there are a great many moving parts. Understanding those moving parts is the basis of expertise. But being able to access the model provides a first frame of reference to guide understanding and application.
The value of models is largely the same as maps. The map is not the territory; it is a representation of the territory. To be useful, it amplifies some information, and hides others. Road maps highlight navigable routes, but don’t show every building, tree and cowpath (with the possible exception of Michelin guides in France). Soil maps ignore buildings and trees as well, but identify swaths of soil makeup and composition. Topographical maps might ignore roads entirely, but provide a greater nuance of elevation and the change of landscape. In each instance, the mapmaker decides what to omit in order to highlight what they are trying to convey.
Models (and explanations that embrace simplicity) also prioritize some information while ignoring other aspects. The point of a model is to highlight what is most salient and relevant about a topic, and to eliminate everything else. Excess detail becomes distraction and tangential information that might be useful down the road, but is unhelpful in understanding the essential framework. In this way, a good model has untold depths behind it. It is possible to keep peeling back layers of understanding and meaning, while the essential structure of the model holds true.
The other part of simplicity we highlighted was about form and design. We often think of simplicity of design as the absence of information, for example when we consider the use of white space. In fact, we confront simplicity of design—or the lack of it—constantly in our day-to-day interactions. Our cars, our computer applications and even our dishwashers are often needlessly complex. It’s one of the reasons that we delight in products that offer true simplicity, whether that was the first iPhone or the Nest thermostat. Both rethought what had been previously complex products in radically different ways, with a very different user experience.
Designing for simplicity is hard. Again, there is the question of what to include and what to omit. Similar to the Feynman Technique requiring the ability to frame things from the perspective of a child, good design requires appropriate empathy with those who will be called on to utilize the resulting solutions. History is littered with tragedies—from Three Mile Island to the Deepwater Horizon to the Boeing 737 Max crashes—where a fundamental contributing factor was the interface and control surfaces of the operators not providing the right information in the right way.
There are rules to good design. Adhering to those rules enhances simplicity as well as aesthetics (and the two are not unrelated). Those rules encompass everything from the rule of thirds—an idea that every photographer learns as a basic tool of composition and framing—to human factors and user interface design. Achieving simplicity in design requires mastery of the rules, empathy with the person that will ultimate use the design, and a determination of what must be highlighted, what must be available and what can be excluded.
One of the great quotes of Albert Einstein is, “Everything should be as simple as possible, but no simpler.” There is an incredible amount of nuance in that sentence. If you just take the first part, you can reduce it to “everything should be simple.” This is an arguable overstatement. It’s also not what Einstein meant. The inclusion of “as possible” is an important qualifier that recognizes that there are real limits to what can be simplified, and when some level of nuance and complexity needs to be allowed. The final part, though, is the kicker. “And no simpler” is a hard boundary that brooks no violation of its principles.
Combine the ideas of Feynman and Einstein (no mean feat) and you get to a very useful place in terms of embracing simplicity and eschewing the simple. By all means, you should strive for ease of understanding, ease of use and ease of comprehension. You also need to avoid oversimplifying at the expense of nuance, meaning, context and comprehension. When important and essential layers of complexity are being masked or hidden, you have gone too far.