How Good Ideas are Born, Get Done


Where Good Ideas Come From
We all like to think our ideas are good, and many likely are. When we stay with an idea long enough, over time, we may find a way to get it done. Adoption, or success is the validation that the idea was good.

Steven Johnson writes about big ideas and in Where Good Ideas Come From: The Natural History of Innovation he tells the fascinating stories of great ideas and great thinkers across disciplines. Setting the tone with Darwin's Paradox in the introduction, Johnson traces the origins of ideas along with their development. He says, “to understand where good ideas came from, we have to put them in context.”

Darwin discovered there was something peculiar in the crowded waters of a reef set in a desolate habitat in the Indian Ocean and noted the observation he made about the coral reef, “so many different life forms, occupying such a vast array of ecological niches, inhabiting waters that were otherwise nutrient poor.” That was the hint that would shape his larger theory about the innovative persistence of life.

Innovation thus follows certain patterns in certain conditions. Johnson looked at this question from an environment perspective. His thesis:

In the language of complexity theory, these patterns of innovation and creativity are fractal: they reappear in recognizable form as you zoom in and out, form molecule to neuron to pixel to sidewalk.

Whether you're looking at the original innovation of carbon-based life, or the explosion of new software tools on the Web, the same shapes keep turning up. When life gets creative, it has the tendency to gravitate toward certain recurring patterns, whether those patterns are emergent and self-organizing, or whether they are deliberately crafted by human agents.

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What we lack is a unified theory that describes the common attributes shared by all those innovation systems.

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by approaching the problem in its fractal, cross-disciplinary way, new insights become visible. Watching the ideas spark on these different scales reveals patterns that single-scale observations easily miss or undervalue.

An idea is thus not a single thing, it is instead “a network.” Two insights that flow from this concept is that we can and should build better environments to help nurture good ideas, and that we are better served when we connect our ideas rather than by protecting them. Johnson, like Eno, promotes the value of ecosystems.

There is value in seeking common properties across various forms of innovation and creativity, says Johnson. The seven areas that are the subject of his exploration offer each cross-disciplinary examples and together provide a solid body of knowledge.

Exploring the adjacent possible

Good ideas […] are, inevitably, constrained by the parts and skills that surround them.

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The adjacent possible is a kind of shadow future, hovering at the edges of the present state of things, a map of all the ways in which the present can reinvent itself.

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What the adjacent possible tells us is that at any moment the world is capable of extraordinary change, but only certain changes can happen.

For some of us, this may mean we are ahead of the times, or seem to be coming from the future. Because:

human minds are not bound by the finite laws of molecule formation, and so every now and then an idea does occur to someone that teleports us forward a few rooms, skipping some exploratory steps in the adjacent possible. But those ideas almost always end up being short-term failures, precisely because they have skipped ahead. We have a phrase for those ideas: we call them “ahead of the times.”

The trick then is to learn to explore the edges of what is possible by creating habits in how we seek and store new and old information and work on building the steps that will make the whole vision come to life. This is how we get them done, one step at a time. We explore the adjacent possible in innovative environments, where we are exposed to a variety of stimuli from things and ideas.

Understanding the value of liquid networks

Along with understanding that a good idea is a network, and not a single thing, in this chapter Johnson says there are two preconditions to the neural nature of ideas for them to activate.

First, the sheer size of the network: you can't have an epiphany with only three neurons firing. The network needs to be densely populated.

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The second precondition is that the network be plastic, capable of adopting new configurations. A dense network incapable of forming new patterns is, by definition, incapable of change, incapable of probing the edges of the adjacent possible.

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What matters in your mind is not just the number of neurons, by the myriad connections that have formed between them.

“The creating brain behaves differently from the brain that is performing repetitive tasks,” says Johnson. Each of us is called to answer the question of what percentage of time we should spend on shallow work, with the understanding that the more we try to switch from creative to repetitive task brain, the more energy we expend on the actual switch vs. the deep work.

Developing the slow hunch

Reading and writing are fundamental activities to engage our creativity and curiosity, they are also inseparable activities when we want to make sense of our world. More than a flash of genius, good ideas start with small hunches. The key is staying with them to develop them further.

You need a system for capturing hunches, but not necessarily categorize them, because categories can build barriers between disparate ideas, restrict them to their own conceptual islands.

In this we deviate from natural history. The world wide web and Google News are two developments from slow hunches. While the first one took a few years, the second took only one year.

Finding a place for serendipity

A dream, says Johnson is:

exploring, trying to find new truths by experimenting with novel combinations of neurons.

There is tremendous power in unexpected connections, but to take advantage of it the most, we should be deliberate about the environment in which they happen. Johnson says Bill Gates and his successor Ray Ozzie famously took annual reading vacations.

During the year they deliberately cultivate a stack of reading material — much of it unrelated to the day-to-day focus at Microsoft — and then they take off for a week or two to do a deep dive into the words they've stockpiled.

By compressing their intake into a matter of days, they give new ideas additional opportunities to network among themselves, for the simple reason that it's easier to remember something that you read yesterday than six months ago.

Every year, Bill Gates makes his summer reading list public to encourage reading more for its many benefits and signal what he's learning and thinking about. The benefits from robust combination of subject matters and bodies of knowledge include — learning to ask better questions, expanding our imagination to apply it to problem solving, appreciating empathy to build a shared understanding.

As we debate the role of technology in our lives, we are also facing an enormous shift in the types of jobs we will have in the future — near and longer term. Hard to know exactly which jobs, aside from those we are creating right now. The idea is to become whole in our thinking, or well rounded, no matter where we work. Technology can help, says Johnson:

The secret to organizational inspiration is to build information networks that allow hunches to persist and disperse and recombine.

Learning from Error

The history of being spectacularly right has a shadow history lurking behind it: a much longer history of being spectacularly wrong, again, and again. And not just wrong, but messy. A shockingly large number of transformative ideas in the annals of science can be attributed to contaminated laboratory environments.

Alexander Fleming famously discovered the medical virtues of penicillin when the mold accidentally infiltrated a culture of Staphylococcus he had left by an open window in his lab.

This is just one among many examples of people getting things egregiously wrong — at first — and then either they or someone else correcting things and getting them done. While there are things we can do to sort luck from skill to avoid mistakes, and making fewer mistakes is desirable, not all errors are of equal nature and magnitude.

It is also interesting to note how many smart people found their way through trying things that did not work. When we explore a new horizon, the errors help us establish some guideposts, and the willingness to try something helps us engage curiosity. Says Johnson:

Error often creates a path that leads you out of your comfortable assumptions.

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Being right keeps you in place. Being wrong forces you to explore.

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The trouble with error is that we have a natural tendency to dismiss it.

Astronomers Arno Penzias and Robert Wilson had dismissed the discovery of background radiation as meaningless static from faulty equipment. It turned out to be lingering reverberation from the Big Bang. “Yet their first reaction is: Our telescope must be broken.”

Social pressure is also part of that. We want to show our best profile, our strongest contributions out of fear of not fitting in. This chapter provide ample evidence that some of the most prominent, important discoveries in a variety of fields, took place after errors.

Doing Exaptation

The term may not be familiar, but anyone who has worked in several industries and roles likely has learned to do it. Borrowing from nature, Johnson adopts the term first proposed by Stephen Jay Gould and Elizabeth Vrba:

An organism develops traits optimized for a specific use, but then the trait gets hijacked for a completely different function. The classic example, featured prominently in Gould and Vrba's essay, is bird feathers, which we believe initially evolved for temperature regulation, helping non flying dinosaurs from the Cretaceous period insulate themselves against cold weather.

But then some of their descendants, including a creature we call Archaeopteryx, began experimenting with flight, feathrs turned out to be useful for controlling the airflow over the sunface of the wing, allowing those first birds to glide.

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A feather adapted for warmth is now exapted for flight.

The initial transformation was almost accidental, but it then becomes useful in other ways. But it evolves into that. Says Johnson:

As the wing evolves, by definition it has to go through a long period where it's utterly useless at flying. (As the saying goes, “what good is 5 percent of a wing?”) Because natural selection doesn't “know” that it's trying to build a wing, it can't push those emerging wings towards the ultimate goal of flying the way a mathematical engineer can continue tinkering with a toy airplane until it successfully takes to the air.

If your aspiring wing doesn't help you to fly, and thus outmaneuver your predators or discover new sources of food, the new mutations that made that appendage slightly more winglike won't be more likely to spread through the population. Natural selection doesn't give good grades for effort.

Looking at the environment as central to the narrative, chance and happy accidents are central to how exaptation works. This to demonstrate that the history of human creativity is rich with borrowing from one discipline or function to do something else.

Defining Platforms

The reef-building coral Darwin discovered in the Indian Ocean was an organism. Scleractinia, or “minute and tender animals,” had built a platform. Says Johnson:

Platform building is, by definition, a kind of exercise in emergent behavior. The tiny Scleractinia polyp isn't actively trying to create an underwater Las Vegas, but nonetheless out of its steady labor– imbibing algae and erecting those aragonite skeletons — a higher-level system emerges.

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The platform builders and ecosystem engineers don't just open door to the adjacent possible. They build an entire new floor.

“Culture, too, relies on stacked platforms of information,” says Johnson. The chapter on platforms deserves more space.

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Chance may favor the connected mind, but before we go ahead and celebrate “always on” connection over deep thinking, we should remember that we need to do those ideas and that the most impressive innovations developed in fact over time. Says Johnson:

Darwin is a great example of this. Darwin himself, in his autobiography, tells the story of coming up with the idea for natural selection as a classic "eureka!" moment. He's in his study, it's October of 1838, and he's reading Malthus, actually, on population. And all of a sudden, the basic algorithm of natural selection kind of pops into his head and he says, "Ah, at last, I had a theory with which to work." That's in his autobiography.

About a decade or two ago, a wonderful scholar named Howard Gruber went back and looked at Darwin's notebooks from this period. And Darwin kept these copious notebooks where he wrote down every little idea he had, every little hunch. And what Gruber found was that Darwin had the full theory of natural selection for months and months and months before he had his alleged epiphany, reading Malthus in October of 1838. There are passages where you can read it, and you think you're reading from a Darwin textbook, from the period before he has this epiphany. And so what you realize is that Darwin, in a sense, had the idea, he had the concept, but was unable of fully thinking it yet. And that is actually how great ideas often happen; they fade into view over long periods of time.

The challenge for us is how do you create environments that allow these ideas to have this kind of long half-life? 

Watch the video of Steven Johnson's talk below.

 

Where Good Ideas Come From: The Natural History of Innovation presents novel and compelling cases for thinking differently about ideas and provides pathways for doing more to get them done.

 

[image from short book digest]