“If your objective was to invent a microwave oven, you would not be working on radars.”

These days, amidst a great collective effort to reverse engineer innovation, everybody’s looking to model the success stories. Tales of disruption pepper our social media feeds, and we want the magic formula—the algorithm—for innovation.

While magic is tricky, success is even more deceptive. That’s because our measure of success, the objective, is “blind to the true stepping stones that must be crossed.” These are the words of Joel Lehman and Kenneth Stanley, the inventors of a breakthrough evolutionary algorithm for robotic neural nets, called novelty search.

What do robot brains and algorithms have to do with our current paradigm of innovation?

At the Evolutionary Complexity Research Group (EPlex) at the University of Central Florida, Lehman and Stanley programmed their AI to abandon their objectives and search for novelty, much like nature’s evolutionary “algorithm.” “Do something you’ve never done before,” they told the robots. They put them in a maze. Guess what? The robots with the novelty search algorithm got out of the maze faster than the ones armed with a plan and a list of best practices. In other words, objectives actually hindered their search. Freed from them, they stopped banging into walls and learned to walk. Are we so different?

Disruption and adaptation ensure the survival of a species, a business, or any agent in a complex system. A network takes in diversity and puts out emergence (the real hero of anyone’s innovation story).

Case in point: two artificial intelligence researchers who use evolution to program artificial neural networks that “learn,” and end up writing a book about Why Greatness Cannot Be Planned. Are we approaching innovation all wrong by holding it against too rigid standards?

So if you want to design for emergence, the scientists in our interview say, the name of the game is to be a treasure hunter. The path isn’t always clear until it’s behind you. Go where curiosity leads you in search of novelty, whatever seems interesting, and you’ll begin to collect the right “stepping stones” for that next big thing…

d4e: Ken Stanley and Joel Lehman, two AI scientists, you wrote a book about Why Greatness Cannot Be Planned. How did that happen? (I’m guessing that wasn’t the plan.)

Ken: There are a ton of self-help books about how to pursue greatness and achieve your potential. A lot of it is speculative and philosophical. What’s unique about our perspective is that we’re offering hardcore scientific empirical research and experimentation that supports the approach that we’re advancing in the book. So people reading this book looking at these ideas can feel a certain level of confidence that they don’t normally feel about where these ideas come from: We weren’t trying to become self-help gurus; we were doing experiments in artificial intelligence. We unexpectedly stumbled on the principles we describe in this book about why greatness cannot be planned.

d4e: The Chinese finger trap is a metaphor for innovation. Why?

Joel: In the Chinese finger trap, the steps that you need to take to solve the problem are exactly the ones you wouldn’t expect would lead to the solution. It’s a model of deception in innovation, in that making a breakthrough discovery often involves taking steps that are seemingly unrelated to the objective.

Ken: It’s the simplest example of this type of innovation process which we’re claiming is very common, where what you need to do looks like it’s exactly the opposite of what you want. It turns out you need to do exactly the opposite of what you think you should. The Chinese finger trap is designed to be deceptive in that way.

You have to push yourself more into the trap to get out of it. The problems of life are far more complex than that, though, so they’re going to be even worse than a Chinese finger trap in terms of being deceptive. If they weren’t, we would just solve all of them. In order to escape the Chinese finger traps of the world, we have to sometimes be willing to step into the unknown rather than go in the direction that’s obvious or “correct.”

d4e: Great invention is defined by the realization that its prerequisites are in place. Apple spends much less than its competitors on R&D. Do you think that those two ideas are related?

We could speculate that people put a lot of effort into pursuing an objective, and that can be very expensive, because maybe the right stepping stones just haven’t been laid. So you’re going to be grinding for a long time to create all the prerequisites you need to get this thing to work. Whereas if you take an unusual approach (and I would be willing to bet that Steve Jobs wasn’t very objective-driven) where you don’t follow an objective path, you can sometimes arrive somewhere interesting and valuable with a lot less effort than someone who is following an objective. People like Steve Jobs seem to have a knack for following those types of trails and taking the kinds of risks that are necessary, and saying, “Let’s just see where this leads.”

d4e: How did an algorithm change your life? Was it a eureka moment, or a slower evolution?

Ken: This question gets to the origins of the idea behind novelty search. There was actually a particular eureka moment before this algorithm that led to the novelty search algorithm, but also later there was the gradual dawning for both Joel and I, that the algorithm is really a way of thinking about life.

Before novelty search, there was an algorithm called Picbreeder, which is a website that we put up in our research group for people to come from the internet to breed pictures, and then publish them on the site. That sounds a little strange, but basically it means that you could come in and pick your favorite picture from a set, and it would have offspring. And the picture’s “children” would be slightly different from their parents — just like if you had children, they wouldn’t be exactly the same as you, but not completely different either.

These experiments exposed a flaw in the paradigm of “innovation through continual improvement.”

I had an experience playing with Picbreeder, where I started with an image that looked like an alien face. I was playing with the image, and it eventually bred into a car. This moment when the alien face turned into a car was the epiphany moment when I was struck with the realization that I had achieved something interesting without trying to achieve it. While it may sound trivial — after all, Picbreeder is just a toy — everything I’ve been taught for years in computer science said that the way you make computers do things — in fact the way we as humans generally do things — is to set your goals and somehow help the algorithm push the computer into the direction of achieving that goal. But this experience was so different than that.

I was breeding these pictures myself, but we have evolutionary algorithms that breed automatically as well, without human assistance. So I realized that this experience of achieving something without trying to achieve it probably has implications far beyond a picture breeding service. This led to the proposition that there could be an algorithm that doesn’t have a clear objective.

This is what I began to speak to Joel about before the novelty search algorithm was created.

d4e: So the idea of discovery without objectives led you and Joel to create the novelty search algorithm. You say that novelty search is paradoxical. How so?

Ken: The novelty search algorithm reflects the philosophy that sometimes you can discover things if you’re not looking for them. It gives the computer the ability to have serendipitous discovery but not necessarily be pigeonholed in the direction of trying to search for one thing and one thing only, or create one type of solution to a problem. Instead of a robot that has one type of walking gait, for example, maybe you have many.

We were playing with this for years, and it would constantly surprise us by doing things that people wouldn’t expect. You don’t tell the computer what to do, but it ends up solving your problem better than if you did. We saw this paradox over and over again. After a few years we realized that we were seeing was about more than a computer search algorithm.

The more I spoke about the algorithm at computer conferences, the more people would ask about things unrelated to computers, such as: What does it mean for my life if sometimes the best way to find something is to be not looking for it? Does this have any broader implications for how we run innovative cultural institutions? Or how we run science?

Or how about the way we support innovation in society?

It became apparent then that it is extremely important that we have this discussion as a society. If objectives are not always the way to guide innovation and scientific progress, then why is it that almost everything we do is objective-driven? That’s when we decided to write a book, because this kind of message is hard to get out in a computer science journal article aimed only at artificial intelligence. This is a much broader issue, in terms of how we foster innovation and treat objectives in our culture.

d4e: In your book, you ask us to imagine a cavernous warehouse of all possible discoveries. You say that “the structure of the search space is just plain weird.” Can you tell us what you mean by that?

Joel: The structure of the innovation space is weird in that it’s hard to predict where certain things will be. The linkages between different kinds of innovations are surprising. That relates to the broader area of serendipity in science or artistic realms, where you might inadvertently create the next big thing. A typical example is the vacuum tube, which was created as part of fundamental research into electricity. The person who was exploring that didn’t have the idea of a computer in mind. It just turned out that from this one point in space, from discovering a vacuum tube, you actually could reach computation.

Ken: Vacuum tubes facilitate computers, and that’s a connection that exists in this big “room” of possible things. But who would ever know that? Somebody later picked up on it and said, “Now that this exists, now we can create this other thing.” There’s a lot of opportunity there for serendipity, in the sense that you wouldn’t even be working on vacuum tubes if your main interest was computation. Vacuum tubes don’t look like they have anything to do with computation. So in some way, to get all this stuff to exist, requires that people sometimes are not working intentionally on the ultimate achievement that stems from the effort that was put into this chain of events.

d4e: Order is important in search. How so?

Ken: When you first hear about novelty search, that we should search for things that are recognized for their novelty and ignore everything else, our intuition might say, “This is just random. How can that kind of search be beneficial?” I think people assume there’s some kind of coherent order that search induces. In other words, we assume that things get better as you continue to improve. That’s an order that we’ve come to expect from an objective — like if you’re trying to get better at school, your test scores will go up. We expect to start out low and get higher, and that’s the kind of order we’re comfortable with.

Whereas with novelty, it’s harder for us to think about what the order of occurrence is going to be, because we’re no longer talking about an objective metric. What we try to argue is that there is an order that’s inherent in a search for novelty — it’s just a different kind of order, one of increasing complexity.

Instead of increasing quality along some objective metric, novelty search basically creates a situation where if you continually try to do something new, you will quickly exhaust all the simple things there are to do. There are only so many simple ways to do things. By necessity, if you succeed in continually seeking novelty, things will have to become more complex over time.

When it comes to innovation, maybe we should loosen the reins just a bit and integrate some of the knowledge that we’re gaining in our scientific understanding of natural evolution.

At some point, somebody invented a wheel. Thousands of years later, someone was on the moon. Things don’t go in the other order. You don’t figure out how to go to the moon and then later come up with the wheel. So there is an order in innovative processes that are driven by invention rather than by trying to achieve a specific objective metric. And that order tends to be the increasing complexity. The reason I bring this up is that there’s good reason here to be confident that the search for novelty does have some kind of coherent principle, and it is anything but random. It’s just that it’s not following the order that we’re used to (of “worse to better”).

We wanted to suggest to our readers that going worse to better is actually not that principled, even if it makes you feel comfortable, because of the fact that it’s a mystery how to do it. We don’t necessarily know what the stepping stones are. So it’s really just a security blanket to say, “I’m going to keep on improving” if you don’t necessarily know how that’s going to happen.

d4e: The age of best practices is over. Would you agree with that?

Ken: There is room, despite everything we’ve said, for trying to improve. But we have to be clear about where that process is appropriate. If your aims are relatively modest, it can be entirely appropriate to just try to improve. If you just want to try to improve your lap time, that’s reasonable. But when it comes to fostering innovation on a larger scale, I’d be ok with endorsing the idea that the age is over, because we should have a revelation that simply trying to continually improve in an objective sense just doesn’t work.

There’s a great opportunity for a paradigm shift here. The amount of information we have now from artificial intelligence is starting to expose problems with the traditional view of achievement and innovation. Our book exists because we had the ability to do experiments that would have been impossible in the past. These experiments exposed a flaw in the paradigm of “innovation through continual improvement.”

Joel: And yet it seems that at the same time, the cultural crest is pushing more toward the paradigm of objectives and continual improvement. We have evidence that this isn’t how the world really works, especially in areas of innovation, discovery and creativity. It’s troubling that so many innovation endeavors are still ruled by objective-based approaches. When it comes to innovation, maybe we should loosen the reins just a bit and integrate some of the knowledge that we’re gaining in our scientific understanding of natural evolution, and how creativity works — and some of these insights come from artificial intelligence.

Ken: There should be a paradigm shift, but we wrote the book because there hasn’t been. This is a current argument about how we should approach innovation. When Joel says we run a lot of things in this very objective-driven way, that’s literally true. Look at what we’re doing in schools. The standardized testing craze is all about objective measurement, and it’s used for all kinds of things, not just for students. We basically say the school has to objectively improve on some metric, or the school gets penalized. It’s all based on objectives, and there’s a lot of discussion about whether that’s a good idea or not, but we’re not part of that debate explicitly.

Our work offers a different angle, which says that if you kept demanding higher scores, eventually everyone would get a 100. That looks like a pretty naive approach. There should be room for people to try new things — and that could lead to scores going down from time to time. If you always penalize for scores going down, then none of those things become possible.

In the world of science funding, one of the things you almost have to do to get money for research is to state your objectives. We’re running our entire federally funded scientific enterprise — really, billions of dollars — based almost entirely on objectives. You can hardly get your word in if you don’t state in the beginning what you’re trying to achieve. It’s not common sense; it’s a problem.

d4e: There’s a book called Why A Students Work for C Students. How does that relate to this philosophy?

Ken: I haven’t read that book, and I think it’s obvious that that’s not always the case — there are plenty of A students who are the bosses of C students. But that’s an interesting question. You could imagine there’s a connection there in that somebody might assume that if you get A’s that’s the correct goal for getting to the top of the heap in some organization. In reality, often it’s the case that the route to success is more circuitous. It may be that the C student was more willing to take risks that the A student just didn’t take because the A student was so single-mindedly focused on doing what everyone says you’re supposed to do in order to be successful.

d4e: Objectively speaking, unstructured play can be bad for us as individual adults, but good for us as a society. True or false?

Ken: I would say false, because I think it can be a good thing for individuals and society. Unstructured play can be risky, though. It may lead to no particular advance to the individual; on the other hand, it may lead to something great. You just can’t be sure. You may have a hobby, and pursuing that interest may just be “play” for you, but it could end up being the stepping stone to your next great achievement.

And of course I’m totally in agreement with the idea that it’s also beneficial to society, because we need people to pursue their passions and try the things that other people wouldn’t necessarily try, so that they can build the stepping stones for others to follow.

Everybody can benefit, but we have to just accept that anything unstructured has risk. That’s why we tend to be against this kind of approach to life as a policy matter: we like to control things with standards and objectives and metrics, because we’re afraid of risk, ultimately. At the same time, you have to take risks in order to have great achievements in the end.

d4e: Let’s say I run a venture capitalist firm. How should I go about building a portfolio of startup investments?

Ken: I think venture capitalists actually put the ideas in our book into practice in a better way than a lot of other areas in society because they understand the value of a portfolio: Not all of your bets need to pay off. Just some of them need to pay off. VC’s are willing to go in some very exploratory, risky directions. If you have one big hit, it can make up for all the ones that didn’t pan out. This is, I think, a pretty good lesson for society in general. In a lot of our institutions we guard against failure as if it’s some kind of pathology to make a mistake. Venture capitalists have good instincts and are willing to have failures, and that allows them to search in a less objective way. I think we would find that the most successful venture capitalists are less objective about their portfolios.

d4e: You don’t seem to dwell much on the concept of probability. Don’t you like it?

Ken: The book isn’t really about probability, but I think we would endorse probability as an important concept. We see its importance in our field of machine learning and artificial intelligence. The point that’s being made in the book is largely independent of an in-depth discussion of probability, although it factors in to risk.

Any individual discovery could be regarded as highly improbable. In innovative processes, the likelihood of making a particular discovery is unpredictable. And yet, overall, you can increase your ability to make discoveries and the probability that you’ll make some interesting discovery.

d4e: You say that novelty is information-rich. What did you mean by that?

Joel: One way to look at novelty is that it’s information based on not where you’re trying to go, but where you’ve been in the past. In some sense, it can be seen as more information-rich than taking an objective-driven approach, in that you completely know where you’ve been in the past, and so that’s more certain. When you say “this is novel,” you can have confidence that it actually is new. Whereas if you’re trying to take a step along the way to your potential objective, you have to be willing to be uncertain, because you really don’t know if that’s going to be a stepping stone toward your goal.

More than that, the idea of being genuinely different often requires some sort of conceptual advance. You can imagine, for example, being on a skateboard. Who’s going to be more likely to create a novel skateboard move? Will it be me, who’s likely to fall on my butt, or will it be Tony Hawk, who has all this knowledge and experience to create something genuinely new? There is some ability, knowledge, or talent that’s required to create something that’s genuinely new. In that way it’s also a source of information.

d4e: Is it possible that there’s a historical trend toward us wanting more certainty? And if so, is the value of novelty rising or falling?

Ken: I think that novelty has always been valuable. What’s happening is that because of things like the internet, there’s now a significantly greater potential for the creation and dissemination of novelty. We’re exposed to much more novelty in a short time than we used to be, because the network has created this capacity to expose people to new ideas almost instantaneously and from enormous numbers of different people. That means that it’s going to accelerate the production of novelty, and we’re all going to be exposed to more, and that’s a feedback cycle. Now that there’s more novelty around, there are more stepping stones, and so more people will create novelty.

There’s a tendency to trap people in the things that they’re comfortable with, and as long as that’s making money, everybody’s happy. But that doesn’t produce the stepping stones we need for innovation.

d4e: What about machine learning and the curation of information? What about phenomena like the popularity of the Kardashians? Aren’t we suppressing novelty?

Ken: Because computers are making decisions for us about what we look at, and those decisions might cause us to not be exposed to interesting things?

d4e: Right, like the rich get richer effect. The more that machines learn our preferences, the more they are fed back to us.

Ken: I think there is that risk. We have to guard against always being given just more of what we want, what we are already comfortable with. I’m pretty optimistic about human nature and its ability to get around the tendency toward convergence. Certainly I think the algorithms will play a role in that too. Algorithms like novelty search can give us a bit of a clue about how to create computer algorithms that are not so convergent that they just always push you in some predetermined direction.

We’re exposed to much more novelty in a short time than we used to be, because the network has created this capacity to expose people to new ideas almost instantaneously and from enormous numbers of different people.

In general, we like to be exposed to stuff that’s unexpected. And we see that there’s been some attempt to do that in services like YouTube, for example. On the homepage they try to expose you to things you weren’t searching for. Of course they may base it on things you’ve searched for in the past, so there’s a bit of a paradox there.

It’s in the interest of anyone running a business to hook people into new things. People are trying to do that, with algorithms, but at the same time, the danger you’re identifying is real, and we should be cautious about it — because there’s a tendency to trap people in the things that they’re comfortable with, and as long as that’s making money, everybody’s happy. But that doesn’t produce the stepping stones we need for innovation.

Joel: One potential danger with some of these algorithms is that they can get very good at providing us with trivial novelties — novelties that are just some modulation of some formula. “The top 10 X, Y or Z.” It fulfills a very basic human desire for novelty, at a very trivial, unfulfilling level. Maybe over time people will become more aware that they’re being exploited by these algorithms. Like Ken, I’m optimistic about humanity’s ability to adapt to technologies. But it is worrisome that this very human desire for novelty can be undermined by clickbait.

d4e: Will there be enough competition in artificial intelligence for robots to evolve, given that some firms may dominate development?

Ken: These kinds of endeavors can become rather objective when a dominant firm has set the standard for success. It does potentially dampen the ability to try new things. Something really novel might not look as good. Someone might say “Our way of doing things is the objectively superior way; these other approaches are inferior objectively, and you shouldn’t invest in those.” I think that’s a problem, and we are suffering from it right now. There is a belief that there’s a canonical approach that works really well, and therefore other things should be relegated to obscurity. To shed some daylight to some of these less conventional approaches would help foster diversification. Of course, the people still need to be experts. We’re not saying that any idea off the street is worth millions of dollars; but if an expert has an unconventional idea that looks interesting, let’s give it a try.

d4e: Making distant associations and unlikely connections within the network is, to me, crucial to innovation. For us, these processes are often subconscious. Will AI have a subconscious?

Ken: I think that’s on the minds of people in the field. Generally, people in machine learning are concerned with what you’re describing as a subconscious process — the ability to make deep, subtle connections. That’s probably a little bit ahead of where the field is at the moment in terms of making those connections through algorithms on computers, although there’s certainly work being done in that direction. Anything that’s interesting about the human intellect is fair game for AI.

To learn how Dialog can help your business, contact us at 512.697.9425 or LetsChat@DialogGroup.com.

This article was originally published in design4emergence, a network science community sponsored and nurtured by Dialog and Panarchy, the world’s first network design firm. 

“For the strength of the Pack is the Wolf, and the strength of the Wolf is the Pack.”

— Rudyard Kipling

We define nodeness as the art and science of living and working as both a whole and a part.

The Greeks gave us a similar term holon, from holos, meaning whole. It’s not an entirely esoteric idea. We are at once individuals and members of families, groups, and communities. Employees work in teams to define organizations, and businesses create value inside ecosystems.

So why the fancy word?

Nodeness isn’t just about how pieces fit together. It’s the idea that there is nothing to the world but nodes and connections. That when we think we see an edge, in reality the full network has simply yet not fully emerged. There is no end. There is always something beyond: another input, another output, another impact. And it’s all connected.

This is the promise of network thinking. We must simply have the patience to sense it, the insight to design for it, and the confidence to act on it.

Nodeness energizes multiscale lenses and levers

Thinking in terms of networks also means thinking about multiscale lenses and levers. The same rules that govern the node rule the network, and vice versa. This means we can find power laws and principles that drive innovation, adaptivity, and resilience in people, teams, companies and ecosystems.

Nodeness also lets our perspective quickly scale without losing focus. Imagine a lens zooming out overhead from a single person, walking down a street, and pulling back into space. As that person moves, simply zooming back in isn’t enough to find them. But nodeness gives us the context we need to zoom in / zoom out with clarity. We understand what is pushing the person to move, where they might end up, and how they’ll travel. Nodeness keeps perspectives centered regardless of scale.

Nodeness helps us transmit purpose in meaningful ways

The search for purpose should be a short one. Ask yourself: What am I optimizing for? That’s your purpose — or it should be. As individuals and teams, we look where we spend our time and energy. For organizations, we can analyze dollar and talent budgets. For ecosystems, we look at how and where we are creating value. If we can transform our individual purpose, we can do the same at any scale.

And purpose is no longer an optional conversation, in an era of unprecedented transparency where all is eventually revealed. You can make endless statements and promises on purposes, but ultimately what we optimize for illuminates our true aim.

Nodeness shows us where the future of business is headed

At every scale, evolution rules. Humans have moved from questions of scarcity (food/water/shelter) to the challenges of abundance (equity/empathy/elevation).


Components of the S&P 500 Market Value


Businesses and brands, having made the most of the commodity economy, are seeking to elevate the value they bring to customers, as demonstrated by the rise of the intangible economy¹.

Are you ready to maximize your nodeness?  Dialog can help.




¹Libert, Barry, et al. The Network Imperative How to Survive and Grow in the Age of Digital Business Models. Harvard Business Review Press, 2016

Last week was big, and we believe it’s just the start of something even bigger.

Dialog, in collaboration with the Santa Fe Institute (SFI), hosted an all-day network design symposium titled “Influence and Complexity: New Views for Business, Politics, Innovation, and Growth,” at the Long Center for the Performing Arts in Austin, Texas. SFI is the first and premier complex systems institute that includes five Nobel Laureates, and Rolling Stone has called them “a sort of Justice League of renegade geeks, where teams of scientists from disparate fields study the Big Questions.” The symposium married SFI’s scientific research of how complex systems work with Dialog’s approach and application to solving real-world, complex system business problems.

Speakers and attendees included world-renowned scientists, senior executives from companies, such as Boeing, VMware and Under Armour, as well as leaders from organizations such as Savory Global, the U.S. War College and the New York Stock Exchange.

From first session through closing happy hour, it was an insightful day of conversation and exploration that we will be exploring in greater detail in the future. For now, we want to send heartfelt gratitude to program participants and attendees.

We have received many requests for takeaways from the event. There were many and we will be sharing them over the coming weeks. To start, here are just a few of our favorite highlights from the panel discussion:

The panel on innovation and networks included Ross Buhrdorf (SFI, former CTO of HomeAway), Bryon Jacob (CTO of data.world), William Klehm (CEO of Fallbrook Technologies), Jeff DeCoux (CEO of Hangar Technology), and Josh Baer (CEO of Capital Factory). As these successful entrepreneurs chatted, representing emerging industries spanning drones and next gen NuVinci Sphere-based CVP transmissions, to big data and the semantic web, it was striking the alignment they had on the importance of networks to them and their business.

The conversation quickly centered not on technology but rather the people in their networks – internal and external.

  • It is so easy to forget in our age of technology and constant change that human emotions don’t change, neither does the desire for human connection, nor the desire to be part of something greater than ourselves. It’s in our DNA.
  • So Connect! “As a species our greatest adaptation is the ability of humans to work together. We built HomeAway with a weekly “kitchen table” meeting that persisted as we scaled from startup to global leader”, as Bryon and Ross recounted.
  • It’s almost trite, but entreprenuers have to be conscious of their network and put effort into building it.
  • What does change, says Josh Baer, is the scalability of it. Today’s tools let us be massive network builders on a scale previously only available to big organizations. He perpetually pays it forward thanks to a DIY app that lets him match needs, talents, and interest as he orchestrates the Austin Startup network.
  • Another common thread was how much diversity really matters, especially women in leadership and technology roles. Not just to perception, as Buhrdorf noted, but the real deal bottom line – studies prove 30% female leadership nets 6% profit improvement on average.

Luckily, a diverse audience brought much needed perspective to the discussion. NYSE Public Board Member and author of Women Make Great Leaders, Jill Griffin offered advice for women looking for opportunities to maximize their chances of success. Her insights included: 1) look for diversity at the top, 2) insist on objective measurement, and 3) find male champions.

A special thanks to Casey Cox and Will Tracy from the Santa Fe Institute for making this event possible. The event demonstrated the power of a network in action and we look forward to sharing more insights over the coming weeks about using network design to solve problems and unlock opportunity.

Stay tuned for more insights and also join us in conversation online using the hashtag #NetworksInAction


I am unreasonably saddened by David Bowie’s passing. To understand why, it is helpful to know a little bit of network theory and understand the implications of neuroplasticity.

On the network theory front, the “Rule of 150” states people can easily keep track of about 150 people in their lives. This is by some reckoning the size of traditional hunter-gatherer bands. In our modern lives, celebrities fill in some of the 150 for many people. David Bowie was one of my 150. He was the Kevin Bacon of my musical universe. One degree to Brian Eno, Talking Heads, Arcade Fire, Mick Jagger, John Lennon, Bing Crosby, Annie Lennox, Luther Van Dross, Pat Metheny, Secret Machines, LCD Sound System, TV on the Radio… And two degrees to anybody you choose.* He was, as Sylvester Stallone said of Rocky Balboa at this year’s Golden Globes, “The best imaginary friend I ever had.”

In one concert I saw, Bowie described himself as having been in his “Nietzsche phase” when he wrote a particular song. He said, “You remember your Nietzsche phase, when you carried your pocket Nietzsche in your trench coat?” Was he talking to me? Yes, I had a pocket Nietzsche! I seriously doubt there was anyone else in the audience that night who had a Nietzsche phase.

I only saw him live three times over the years: on the Serious Moonlight Tour in ’83, on the Glass Spider Tour in the late 80s, and for 2004’s Reality — which came on the heels of Heathen (one of Bowie’s most listenable records – start to finish). The last tour showcased a man who had found his groove. He laughed. He was comfortable. And he was entertaining. He was at the height of his success as a person. He was a happy father and spouse. But throughout his career, I felt his evolutions and realized deep truths about what creates happiness and about ongoing innovation.

In the documentary David Bowie: Five Years in the Making of an Icon, they point out Bowie was uncanny in his selection of collaborators (a few are even listed above). And as Josh Groban tweeted about his death, “He bent genres, genders and our minds.” This is why I was attracted to David Bowie — for his purported ability to bend minds. How did he do it? He was a network designer par excellence. He deliberately designed his network to create novelty.

Identity stood at the heart of Bowie’s career. As The Atlantic said on his passing, if you are going to invent as many characters as David Bowie, you have to give consideration to their death. As they note, identity and dissolution is essential to so many human relationships. And it is this that stands at the heart of the pain I feel. As one critic said on Bowie’s passing, quoting Gorky on Tolstoy, I cannot be ‘an orphan on the earth, so long as this man lives on it.’

To understand the neuroscience of this, consider the following simple hand tapping experiment: Tap a table and tap a subject’s hand under the table simultaneously. After a few minutes, you can smack the table and a galvanic skin response shows the subject responds as if they have been struck. Why? Because, as neuropsychologist Donald Hebb coined, “Neurons that fire together wire together.” Put yourself in the subject’s shoes. You aren’t hurt when the table is struck. Yet you think you are because neurons that fire together wire together. It is this that allows us to play the game of life. But it is also this that causes us suffering. We aren’t actually in the picture. We don’t actually get hurt. Our body doesn’t sustain blows when the table gets smacked. But we react as though we did because we are, in cyborg–like fashion, wired into these stimuli. We have inadvertently begun to identify with the table. And neuroscientists tell us there is an especially acute pain when our mirror neurons activate — when we experience a sense of “I/me/mine.”

We have all sorts of things we are attached to inside, but one of the most basic or largest is our identity. David Bowie became a part of mine — for 38 years. That is longer than many friendships. And I know we shared a Nietzsche phase. That is why I am so sad … because as Bowie sang in “This is Not America” — “a little piece of me, a little piece of you… has died.”

* Yet, strangely in a perfect illustration of being trapped in our past preferences by the internet, the David Bowie Station on Pandora on the afternoon of his death repetitively plays the Kinks (Lola six times), the Beatles, the Stones, Led Zeppelin, and Talking Heads. It is like some kind of transitional 70s music ghetto. I don’t discover anything new.

“All theories of organization and management are based on implicit images or metaphors that persuade us to see, understand, and imagine situations in partial ways. Metaphors create insight. But they also distort. They have strengths. But they also have limitations. In creating ways of seeing, they create ways of not seeing. Hence there can be no single theory or metaphor that gives an all-purpose point of view. There can be no ‘correct theory’ for structuring everything we do.” Gareth Morgan

Metaphor Insight

We develop a formidable understanding of individuals and cultures through the use of metaphors. We’ve learned to maintain stability by not rocking the boat or have used wallflower to describe someone who is quiet or shy. These metaphors communicate values related to security and social acceptance. On the other hand when we use metaphors such as life is a battlefield or refer to someone as top dog wecommunicate values associated with winning, advancing and being the best.

Machine Management

The machine metaphor is ubiquitous. From the dawn of the Industrial Revolution we have used metaphors about machines to communicate about work. When things are running smoothly we say they are “humming along” or “it’s well oiled.”  Likewise if we encounter a problem that needs to be fixed, we simply “re-engineer” the machine. Henry Ford captures the essence of the machine mentality when he asks, “why is that when I inquire for a pair of hands, they come with a brain attached.”

A machine is inanimate, non-relational and made of disparate parts; all separate but each serving the whole. The machine’s purpose is to be as efficient, quick and productive as possible. Machines don’t have other needs besides serving its one particular function.

Fifty years ago the political, social, economic and technological climate fostered an environment that was conducive to thinking we were separate. We are not separate, we are interconnected, and together we serve the whole. John Muir saw this truth reflected in nature, “When one tugs at a single thing in nature, he finds it is attached to the rest of the world.” The metaphors we use to communicate our perceptions of the world profoundly shape our objectives, attitudes, behaviors and values.

The Need For a New Metaphor – Ecosystem Management

Modern organizations are composed of complex systems. In order to compete and stay relevant, we need to stop managing the static machine and start nurturing our dynamic ecosystem. The new recipe for success lies in our ability to keep our ecosystem healthy. As business leaders we need to continually ask ourselves if our actions make life more stressful or are they alleviating stress, simplifying processes and empowering those around us to do the same. The metaphor of an ecosystem implies we are part of a community of living organisms, in conjunction with non-living components (technology), interacting as a system. Future growth and success will depend upon aligning organizational objectives with ecosystem management principles, resulting in unprecedented success. Paragons of ecosystem management embody the following characteristics:

  • Thinks systemically, strategically and contextually
  • Leverages diversity to drive innovation
  • Manages abundance instead of scarcity
  • Acknowledges the ecosystem extends far beyond the physical organization
  • Nurtures interpersonal relationships
  • Designs work to optimize for the employee
  • Engages in lifestyle and experience design
  • Fosters a purpose-centric culture
  • Leverages self-awareness as a source of competitive advantage
  • Promotes a holistic approach to wellness, integrating bio/psycho/social/spiritual components

There is an interconnectedness of all life, from the smallest molecular compound to the largest galaxy, and undoubtedly between every person. Organizations aligned with ecosystem principles will drive innovation through the 21st century and beyond. The choice is yours to make, what metaphor will you choose?

From an early age, the Swiss scientist Max Kleiber had a knack for testing the edges of convention. As an undergraduate in Zurich in the 1910s, he defied the conventions of the day by roaming the streets dressed in sandals and an open collar. After a time in the military, he failed to reappear for duty when he discovered that his superiors had traded information with the Germans, despite the official Swiss position of neutrality in World War I. His actions landed him in jail for several months. When he was released, Kleiber decided that he had had enough of Switzerland. And so he packed his bags and went where sandal-wearing, nonconformist, war protesters go—to California. Kleiber matriculated at the agricultural college in the University of California at Davis. “His research initially focused on cattle, measuring the impact that body size had on their metabolic rates, the speed with which an organism burns through energy. Shortly after his arrival at Davis, Kleiber stumbled across a mysterious pattern in his research, a mathematical oddity that soon brought a much more diverse array of creatures to be measured in his lab: rats, ring doves, pigeons, dogs, even humans. Scientists and animal lovers had long observed that as life gets bigger, it slows down. Flies live for hours or days; elephants live for half-centuries. The hearts of birds and small mammals pump blood much faster than those of giraffes and blue whales. But the relationship between size and speed didn’t seem to be a one to one relationship. A horse might be five hundred times heavier than a rabbit, yet its pulse certainly wasn’t five hundred times slower than the rabbit’s.”[1] After a formidable series of measurements in his Davis lab, Kleiber finally had a working model that could predict the metabolism and heart rate of all animals, based upon one single variable, mass! He found that if you double the size of an animal from 10 lbs. to 20 lbs., 50 lbs. to 100 lbs., it doesn’t matter the size, then you get a 15 percent decrease in metabolism and heart rate.


Amazingly, even though all animals on Earth have evolved in their own unique environments and with diverse evolutionary forces, all animals are constrained to lie on this same line. How can that be? The reason is networks. All animals are made up of cells; all animals are simply networks of cells, and all cellular networks act in the same symbiotic manner, no matter which animal you consider. That is to say, “All of life is controlled by networks — from the intracellular through the multicellular through the ecosystem level.”[4]

Years later, Kleiber’s law spiked the interest of another scientist, Geoffrey West, who was attempting to establish a quantifiable, predictive framework for the growth of cities. He wondered if Kleiber’s law applied not only to networks of cells, but also to networks of people, namely cities. He gathered population data, energy consumption data, infrastructure data, pace-of-life data, etc. on hundreds of cities. When all the numbers were crunched, West found that cities were constrained to the same linear pattern that animals are constrained to. Truly, a network of people benefits from the same economies of scale as a network of cells. This means that a city twice as large as another uses 15 percent less energy and 15 percent less infrastructure per capita. Therefore, if an elephant is just a scaled up mouse, then a city is just a scaled up elephant. Hence, when the mechanic, scientist, entrepreneur, teacher and waiter all specialize and work together, they create a more efficient, symbiotic metropolis. And, the more people that specialize, the more efficient the city.

However, one datapoint of West’s research did not follow this negative linear pattern. West found that innovation (in terms of patents, R&D budgets, “supercreative” professions, and inventors) follows Kleiber’s Law, but in the positive direction. That is to say, if a city is twice as large as another, it is not 15 percent less innovative, but 115% more innovative. This means that a growing network of people within a city will increase the collective capacity of its citizens to innovate.

Innovation through networks

If cities and innovation can be compared, we would assume that West’s Model would allow us to predict, with remarkable accuracy, the future pace of technological change based on a single variable, city growth. To make a prediction, let’s crunch some numbers of our own. According to the United Nations Populations Fund (UNFPA), world, urban population will grow from its current number of 3.3 billion to 7.1 billion by 2060.[5] That is more than a doubling of world, urban population in 48 years. If we plug this information into West’s Model and assume that the average city more than doubles in size, we would expect to see two-and-a-half times more innovation in 2060 than in 2011.[6] More specifically, one year in 2060 would equal two-and-a-half current years of technological change. While this is interesting, it is hardly impressive. If we look at the current innovation trend data for the United States alone, we find that just in the last 18 years the number of patents granted to US citizens per year has doubled![7] See figure 2.


Current innovative trends already surpass anything that West’s Model might predict about cities. The truth of the matter is that West’s Model fails to predict the progress of innovation over time for the same reason that a city of one million in the year 1800 did not have the same level of innovation as a city of one million in 2011. The reason again is networks, but this time the reason is networks that are independent and run parallel to cities’ networks. This understanding should force us out of our myopic focus on cities as the only significant human network driving innovation and force us to contemplate what other networks are driving the current pace of innovation.

If we broaden our consideration of human networks beyond cities, we find that the most significant networking technology has been the television. When city-growth data and patent data are graphed we find that all the way up until 1960, American megacity growth[9] predicts 99% of the variation of patents granted per year. However, after 1960 we find that population data does not accurately predict the pace of innovation. See figure 3.


However, after 1960 we find that population data does not accurately predict the pace of innovation. (see Figure 4)

Innovation through networks

At the same time that population no longer predicts the pace of innovation, we see the emergence of the television as a popular medium. By 1950 only 9 percent of American households had televisions. However, by 1959 that figure had increased to 85.9 percent.[14] As we see in Figure 4, the pace of innovation after 1960 skyrockets. In truth, this should come as no surprise. Even at its birth, people understood the enormous networking possibilities of television. On April 9, 1927 when Bell Telephones conducted the first long distance use of television, Secretary of Commerce Herbert Hoover commented, “Today we have, in a sense, the transmission of sight for the first time in the world’s history. Human genius has now destroyed the impediment of distance in a new respect, and in a manner hitherto unknown.” That day in 1927, Hoover had no way to know the prescience of his statement decades before it would change the course of innovation. From 1960 forward, the television introduced us to diverse ideas and captured the imagination of the world in a way that was more physical and unifying than ever before. For example, on July 20, 1969 the world witnessed the first and only manned, lunar landing. On that day 500 million people, three-quarters of which were not Americans, had one of the most memorable days of their life, simultaneously. They felt small and big all that the same time. They viewed our Earth as a small globe whirling around a far larger speck of light. That day the minds of philosophers, scientists, men of faith, men of power, story tellers, and poets united to contemplate the same questions, ‘What is out there? What is our place amongst these other specks of light that shine in the darkness of the night?’ In a way, all 500 million worldwide viewers became philosophers, if only for a moment. No other technology, before that time, was capable of uniting humanity in the way that television did that day. It was a proud day that unleashed our collective creativity, not just for Houston or for the United States, but for all of humanity.

As we begin to understand that human networks span beyond the city, we must consider that from the first constructed roads in 4000 BC in the city of Ur to the popularization of the internet in 1982 (see Figure 2), the human genius has destroyed the impediment of distance. And going forward, we will see the impediment of distance razed to the ground as we continue to build and strengthen worldwide networks. By understanding that all of life and all human networks follow the same networking patterns, we can conclude that if a city is just a scaled up elephant then television and the internet are just scaled up cities, and West’s Model can still help us predict the pace of innovation. Thus, if we compare Facebook with an active user population of 900 million to the population of the most innovative and largest city in the world, Tokyo, with a population of 34.5 million[15] we can infer that Facebook has the potential to be 59 times more innovative than Tokyo.[16] It doesn’t end there. If the internet were to reach every person in the world by 2060 and a worldwide network became possible, this network has the potential to be 1,100 times more innovative than Tokyo.[17] That certainly beats the 2.5 multiple increase over 48 years that West’s Model predicted when limiting human networks to cities.

The internet is truly our greatest tool to build a worldwide network; however, it should not be and is not the summit of our networking potential. The internet currently lacks the ability to fully involve all of our human senses in a worldwide network in the same way that the radio and telephone lacked what television had to offer. Relationships on the internet continue to feel superficial. Moreover, we cannot touch, smell, and taste on the internet. This lack of connection leaves us desiring more. However, that impediment can and will be overcome.  Our global, human network will become more meaningful, and the pace of innovation will exponentially increase. One need only pick up an issue of Popular Science to begin to envision the deep connections that will make up our worldwide network in the future. Computer screens as thin as wallpaper[18] and as cheap as a television will one day cover our walls, allowing us to sit in the same room with people thousands of miles away. Combined with simulated-texture technology, we will not only sit in the room together, but we will be able to reach out and touch that person. While a computer may never be able to fully simulate the feeling of a hug, it may not need to. Instead, technology will in the near future bring the computer screen to you instead of you to the computer screen, thereby enhancing our physical connections through augmented reality. Computer screens that fit on a contact lens will allow you to facially recognize strangers in the street and receive biographic information about them. Augmented reality lenses will enhance, enliven, and deepen our interactions. Imagine playing video games with friends in the backyard and interacting with characters as if they are in the real world. Imagine attending concerts, conventions, and tradeshows within a 3-D environment. Imagine training to be a mechanic and when you look at an engine, 3-D specs are displayed on top of it telling you what to fix and how to fix it. Once your imagination gets going, it’s difficult to imagine experiences in our life that cannot be deepened and broadened with augmented reality. According to Google, even our romantic relationships will be deepened. Google’s new commercial[19] for its soon-to-be-released augmented reality glasses, depicts a man performing a sunset serenade for his girlfriend as she sits at her home computer. Now, what could be deeper than that (tongue in cheek)?

While near-term technological possibilities are still lacking, the fact of the matter is that worldwide networks are deepening and broadening and creating a global consciousness that was not previously there. The more we continue to network, the more symbiotic our actions will be, and the more we will benefit from one another. In sum, these worldwide networks will be the engine that drives us to unthinkable, innovative possibilities—all thanks to West’s Law.

Dialog’s Network


[1] Johnson, Steven (2010-10-05). Where Good Ideas Come From: The Natural History of Innovation (p. 58). Penguin Group.
[2] At a more complex level, if an animal is 1,000 times heavier, then its metabolism, and heart rate are 5.6 times slower ( ).
[3] West, Geoffrey (2011-07-XX) http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html?quote=1010
[4] West, Geoffrey (2011-07-XX) http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html?quote=1010
[5] United Nations Population Fund (UNFPA) (2007-XX-XX) “State of World Population 2007.” (p. 6)
[6]  times more innovation
[7] the number of patents granted to citizens in 2011 in the U.S. (120,690) is two times greater than the number of patents granted in 1993 (60,883).
[8] Patents: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm
[9] Megacity growth = the growth in population of the ten largest US cities
[10] Megacity populations: http://en.wikipedia.org/wiki/Largest_cities_in_the_United_States_by_population_by_decade
[11] US Population: http://en.wikipedia.org/wiki/United_States_Census
[12] Patents: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm
[13] Patents: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/h_counts.htm
[14] http://www.britannica.com/EBchecked/topic/1513870/Television-in-the-United-States/283614/The-year-of-transition-1959
[15] http://www.citypopulation.de/world/Agglomerations.html
[16]  times more innovative
[17]  times more innovative
[18] http://www.youtube.com/watch?v=s4c2KnBKXu4&feature=player_embedded
[19] http://www.youtube.com/watch?v=JSnB06um5r4

The Medici’s brought Renaissance men and women together to share cross-discipline perspectives and develop perspectives and drive tremendous advances in innovation.