At ISTE this past June, I picked up Steven Johnson’s Where Good Ideas Come From (reading notes here) and, after his fantastic keynote address, looked forward to reading it. After just finishing it, the book didn’t disappoint, leaving me with quite a bit to think about on the verge of beginning a new academic year.
Using parallels from evolutionary biology, Johnson discusses how innovation happens and great ideas are formed, dispelling the myth of the “eureka moment” in the formation of ideas. He outlines 7 patterns that they typically fall into, and I’ll outline a few of the patterns that I intend to focus on in the coming academic year. Johnson’s TED talk on the topic gives a great introduction to his ideas, if unable to read the book.
Stuart Kauffman’s concept of the adjacent possible, which he used to describe all of the combinatory possibilities of the molecular soup of early Earth, plays a prominent role in Johnson’s arguments. The adjacent possible presents us with a “map of all the ways in which the present can reinvent itself” (31), and new combinations serve to expand the adjacent possible. Using this idea, Johnson argues that “[g]ood ideas are not conjured out of thin air; they are built out of a collection of existing parts, the composition of which expands (and, occasionally, contracts) over time” (35).
Johnson stresses that “to make your mind more innovative, you have to place it inside environments that share the same network signature: networks of ideas or people that mimic the neural networks of a mind exploring the boundaries of the adjacent possible” (47). He calls this a liquid network, where ideas can freely flow from one node to another to build upon each other, and it’s precisely this kind of environment that allows us to explore the adjacent possible. Importantly, “the individuals get smarter because they’re connected to the network” (58) and the diversity that it brings to our thinking.
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The “long zoom” that allows to identify patterns within ideas (20) |
Good ideas don’t simply happen; rather, they must be cultivated over time, which Johnson names the slow hunch (81). As was the case with some of the most revolutionary ideas in our intellectual history (e.g. Darwin’s theory of evolution), it’s most often the case that a good idea comes into form as little more than a hunch. These hunches, in order to grow into something more, must interact with other hunches through the liquid network to open up the adjacent possible.
Finally, Johnson shows that error is an inherent part of innovation. According to the British economist William Stanley Jevons, “[i]n all probability the errors of the great mind exceed in number those of the less vigorous one” (137). That is to say, most brilliant ideas are founded upon failed efforts. Fear of failure is so pervasive in the edusphere today, though, that it’s become a serious hindrance to creativity and the construction of liquid networks. Contrary to what many of us in academia were trained to believe, it’s ok to fail, when working on a new idea.
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“Chance favors the connected mind.” (174)
The experiences I had at InstCon, ISTE, and GTACHI were so profound that I’m still having difficulty putting them into material form. The networks that have grown on account of these PD opportunities have already paid dividends for my own thinking about how I want to change my teaching and how I want to approach PD in the future. So after a productive summer of professional development, it’s now more clear to me than ever that we need to emphasize the construction of environments that facilitate good ideas by building more and larger “liquid networks”.