Freshly Stolen Material From the Blogosphere:
In 1996 Steve Jobs expressed strong agreement with a quote he ascribed to Pablo Picasso, Good artists copy; great artists steal. This is a meme with a long pedigree, and has also been expressed in very similar terms by other creative giants, from T S Elliot to Stravinsky.
With quotations, there is often debate around who said exactly what and when. However, what I love about them is that, like proverbs, they often very succinctly capture a powerful insight. I believe this is one such insight, and one worth stealing. While the idea itself is hardly new, I believe that looking at it through the lens of Behavioral Science, and hence using analogy, enabled by deep causal understanding, and problem mapping can teach us how to steal more effectively.
Of course, I’m my no means advocating plagiarism, which is anything but innovative. However, I believe there are at least three different forms of what I consider honorable creative theft:
1. Transferred Innovation. The problem has already been solved in another, superficially unconnected area. Reapplying a pre-existing solution, stolen from somewhere else, transforms innovation from an act of creation into an act of search, discovery and transformation.
2. Emergent Innovation. Operating at the interface of established disciplines enables blending or remixing of existing knowledge in new ways, often leading to ideas that are emergent, and exceed the sum of heir parts: 1+1 >2
3. Inevitable Innovation. Sometimes simply having all of the pieces of a puzzle is all we need to complete it. Christened Multiple Discovery by Easton (1), the world of innovation, and even Nobel Prizes f (2), are replete with examples of innovations that occur independently, and at roughly the same time. When all of the parts of the puzzle are available, sometimes the answer almost jumps out at us. This makes Innovation, in part, an exercise in staying current. If there were ever a case for reading websites like Innovation Excellence, this may be it!
Transferred Innovation. This is obviously not a new idea, as smart innovators have been doing it for a very long time. For example, James Dyson realized that the way cyclonic separation removed sawdust from the air in a saw-mill could be reapplied to create the revolutionary, and lucrative new Dyson vacuum. (3). There are also many examples in Bio-Inspired Innovation or Biomimicry. Frank Fish reapplied the unusual structure of whale fins to improve efficiency of windmills (4), while George de Mestrals’ invention of Velcro was famously inspired by the burr (3). We can steal ideas from virtually anywhere. However, domains with high R&D investment, like medicine, the military, or nature are good places to start, simply because so much innovation already exists there. Looking in unusual places can also lead to really big ideas, and sometimes the more surprising the connection, the bigger the innovation. For example, tapestry and computer programming may seem quite far removed, but early computer programming was ‘stolen’ from punch-cards used in Jacquard tapestry looms (5). Many of the innovations described above are more serendipitous than systematic, and favored by an agile and prepared mind that is already searching for the answer to a nagging problem. However, psychology gives us some ways to tackle this more systematically.
Firstly, analogies are key to making these connections (3). Dyson saw the analogy between vacuum cleaners and air filtration in a saw mill, and de Mestral used analogy to bridge between burrs and buttons. Humans naturally make analogies, but we can amplify this tendency by incorporating a high level of causal knowledge into our problem definitions (3), and translating that knowledge into relational maps.
These help us to break through implicit, ‘fish doesn’t see the water’ limitations that govern how we see our world. Instead, by making implicit connections explicit, they reveal problems at a more generic level. This in turn helps us to see similarity in the structure of systems that superficially appear to have no similarity at all. So while the need to reduce information to binary code was common between tapestry looms and early computers, you needed to peel back the onion to see structural and relational similarity between the two systems. Likewise, Dyson had sufficient causal knowledge to see that filtration represented a trade off (efficient filters clogged), but also to reframe the problem at a slightly higher level of abstraction. He saw that the goal wasn’t better filtration, which was what everyone else was working on, but instead, effective separation of dust and air. This allowed him to connect the saw-mill and vacuum cleaner, and ultimately deliver the Dyson Vac.
Analogies don’t have to be perfect to be useful. Problem mapping can also reveal similarities and differences between domains (analogy and disanalogy), providing insight into how much work will be needed to transform analogy into innovation. Dyson’s insight was intuitively brilliant, but maybe even he could have used a little more structure when reducing his analogy to practice. I passionately believe in prototyping and productive failure, but I would prefer a little less than the now famous 5,127 iterative prototypes it took him to transform analogy into breakthrough innovation!
Innovating at interfaces. We know that the interfaces between disciplines are fertile grounds for innovation. Today, Biotech, Behavioral Economics, 3D printing, and nanotechnology are all examples. Because they are in many ways white space, the low hanging fruit in these emerging fields has often not been fully harvested. However, because they are built upon existing knowledge, the learning curve typical for a new are can sometimes be shortened by re-using old knowledge.
Unlike transferred innovation described above, interfaces have the additional benefit of catalyzing the blending of ideas from different sources. This synergy between domains can create emergent, non-linear, innovation where the whole can exceed the sum of the parts.
For example, Picasso stole from primitive African and Iberian sculpture, blending it with early impressionism to create analytical cubism (6). The Beatles blended standards, rock and roll, skiffle, classical, jazz and others to create their innovative repertoire of music. Job’s iPad blended Mp3 players, phones, computers and books, while today, 3D printing blends ink jet printing with CAD design. I’m by no means suggesting that Picasso, Lennon & McCartney, or even Jobs created detailed relational maps as a part of their innovation process. However, they were all a lot smarter than I am! I believe that stealing analogy, mapping and causal understanding to facilitate conceptual blending (via analogy-disanalogy mentioned earlier), can make it easier to make these breakthrough innovative blends.
Inevitable Innovation. The concept of multiple discovery suggests that discoveries and inventions are often made independently and more or less simultaneously by multiple inventors. Examples abound; Newton and Leibnitz with calculus, Darwin and Wallace with natural selection. However sadly, the story that Nylon derived its’ name from its simultaneous invention in New York and London, while perhaps the most famous example, is pure urban legend. Multiple discovery isn’t exclusive to big, Earth shattering inventions either. From personal experience with filing patents, it was common someone to beat me to the finish line by a couple of weeks, or even or them to miss out, when we’d both been working in the same field for a very long time.
Why is this? Humans love puzzles and completing patterns, and are neurologically rewarded for solving them. If different people are looking to solve a problem, and a key new piece of information becomes available to them at the same time, simultaneous ‘Eureka moments’ are quite likely. Invention is often input information controlled, triggered by that final, but crucial, nugget of data. Now, it still takes exceptionally smart people to make connections that lead to Nobel Prizes, calculus, and the theory of evolution! No matter how many pieces of the puzzle I had, I wouldn’t have recognized calculus if I’d tripped over it on a sidewalk. However, for lesser mortals like myself, deep causal understanding, combined with mapping, and analogy can catalyze more modest innovations. At a team level, mixing domain experts with T-Shaped innovators, skilled at making connections, can also create a potent mix of causal knowledge and analogical bridging.
As a final thought, stealing is hard work. Transformation inevitably requires effort, as Dysons’ 5,127 prototypes indicate. Stealing from nature can be even tougher, as human and natural technologies are fundamentally different (6). Hindsight, post rationalization, and our need to provide concise stories can also hide the persistence (3), determination and sheer hard work needed to make analogies useful. As with a movie, the final product often doesn’t show the outtakes and alternative storylines that get left on the proverbial cutting room floor. Stealing works, but not so much as an easy way to innovate, but more as a way to come up with something radically new.
1. Lamb, David; Easton, S. M. (1984). Multiple Discovery: The Pattern of Scientific Progress. Avebury.
2. Harriet Zuckerman, (1979) Scientific Elite: Nobel Laureates in the United States, Free Press,
3. Art Markman, (2012) Smart Thinking. Perigee
4. Bumpy Whale Fins Outperform Smooth Turbines – Scientific American, July 8th, 2008
5. James Burke, Connections/Connections II BBC TV Series
5. Fauconnier , Turner (2002) The way we Think. Basic Books
6. Steven Vogel. (1998) Cats Paws and Catapaults. Norton