Posts

We are all to varying degrees prisoners of our metaphors. This is true in both our heads and when we communicate with others. And it’s definitely true in business. Depending on your industry, any number of metaphors can guide the acquisition of new capabilities. However, there is one global metaphor shift that will help you and your organization succeed in the Network Age, and that is shifting from a mechanical to an organic worldview – from a view of your business as a machine to an actual living ecosystem.

In the last few decades, the predominant metaphor has shifted in nearly every one of the sciences from mechanistic to organic. Some scientists have even posited that the universe itself may essentially be a living, complex organism. So it’s just time business updated its metaphor as well.

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.

Frederick Taylor, the pioneer of industrial age management, used his stopwatch to measure the motions of people as parts in a machine. This was the machinery of the Industrial Age, and make no bones about it, this reductionist approach yielded unprecedented gains in productivity and material wealth.

It seemed rational to treat people like parts in a machine when the way that you unleashed productivity was by putting people into factories. Those factories required huge outlays in capital. It therefore made sense to make decisions around the binding constraint on growth – capital itself. Measures like return on equity and the DuPont formula arose to facilitate rationing of capital and ensure its application to highest return opportunities.

Is ROE Capitalism’s “Runaway”?

The cornerstone of the Industrial Age business measurement system was ROE. Several years ago the Harvard Business Review published an article entitled Runaway Capitalism. A “runaway” in evolutionary terms is when natural selection and sexual selection become decoupled, as in the case of the peacock’s tail. The tail offers such evolutionary disadvantage that peacocks would be extinct were it not for the fact that humans like to collect them. Yet peahens are drawn to the tail, so it continues to be selected for by the species even though the natural environment does not support it. The author’s point was that ROE may be capitalism’s runaway. And the implication is that it may threaten the continued survival of the planet and the species.

The Challenges of a Machine Metaphor

If the above is true, then perhaps the biggest challenge presented by machine management is the belief that everything should myopically serve the machine. And that one number, ROE, represents “the One Ring to rule them all” in the machine metaphor.

Other challenges with the machine metaphor include the fact that it stifles initiative. 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 business that is run solely as a machine is not adaptive. It is too cumbersome and slow and fails to consider externalities. It isn’t designed for emergence. Or as Carolyn Hendrickson, a Ph.D. in organization design quipped, “Why don’t matrix organizations work? Because the mind that designs the matrix is not the mind that inhabits it.”

One of the challenges of a machine metaphor is that we tend to apply it to ourselves when increasingly network scientists are showing us that we are ourselves the products of the networks we engage in.

Despite these problems with machine view, there are new perspectives that can include it in a new larger whole – that of the complex ecosystem.

The Power of a New Metaphor: The Complex Ecosystem
Modern organizations are composed of complex living systems or networks. The metaphor of an ecosystem implies we are part of a community of living organisms, intertwined with nonliving components like technology, all interacting as a system.

This boundless system goes far beyond the physical boundaries of the firm and includes psychosocial characteristics as well as the material elements of our supply chain. It also includes the emergent outcomes of local agents acting upon very simple rules (which is why a rigid three-year plan won’t work). In order to compete and stay relevant, we need to stop managing what we think of as static machines and start nurturing our boundless dynamic ecosystems. Some principles for a networked ecosystem design could include the following:

1. Think like a Gardener: assemble, shape, influence, enable and nurture.

Recognize that some problems like the “diabesity” crisis cannot be directly solved. For certain classes of problems what scientist call “wicked problems” we must focus on building capabilities for solutions rather than solutions themselves.

2. Start with Purpose. Purpose has been proven to be the factor that enables firms to outperform their contemporaries over extended periods of time. Purpose is the first step in our “strength from the inside out” methodology of ecosystem design and orchestration. Purpose gives a network energy, and it is a natural north star. The HP Way offers a classic example of purpose as an ecosystem design principle. It is as much a set of values that provides the basis for how people in the ecosystem will treat each other as it is an explicit statement of what the ecosystem is designed to accomplish. In complex networks, it is simple rules acted on at the local level that create the network experience. In a business ecosystem, these values act as simple rules that guide the behaviors of decision-makers locally and empower change at the network level. Strategy in the network age is about communication flows and the incentives and relationships driving them. In times of change and uncertainty, values and purpose can provide the DNA for a network structure by enabling communication flows and aligning intellectual, human and social capital.

3. Design for the Whole. Tim Cook tells the story of coming to Apple for the opportunity to work at the seams between hardware, software and communications because in his words “that’s where the magic lies”. Leaders who are network designers and ecosystem enablers know that the value you capture cannot exceed the value your ecosystem creates. In this sense, Michael Porter was wrong: There aren’t five forces, only one. In nature, waste equals food. Likewise in business, in the strongest ecosystems, the parts feed each other. Disney’s ecosystem is a great example of a synergistic or integrated business ecosystem in which one innovation feeds the others. When a new Pirates of the Caribbean comes out, for example, the studios make money, the theme park gets a new ride, the franchises sell toys, the brand sells licensing agreements, and each of these entities benefits because the network is designed so that novelty within one part brings activity to the others. In working with clients across industries, we have found there is opportunity to have outsized share of voice and corresponding share gains by helping to solving larger pressing ecosystem problems even if you are not an ecosystem creator like Apple or Disney.  On a more modest scale, taking the bigger view allows one to mobilize resources from complementary providers and other stakeholders.

4. Consider the Parts when Designing the Whole. It is widely known that businesses increasingly win on experience. Increasingly, companies compete by creating platforms upon which customers co-create and it is therefore nearly impossible to separate the network experience from the user experience. It is also the case that we are increasingly a cyborg network now. The best chess teams in the world are currently centaurs—half man, half machine. How are you using to technology to augment your team’s abilities? And do you understand all the facets of your network experience?

5. Make your People Network Designers. Empower everyone at your company to understand and shape network behaviors in short, to design for emergence, because as Churchill said, “We shape our buildings initially. Thereafter, they shape us.” The same is true of our social structures.

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, the world’s first network design firm. 

The holidays are here so it’s time for us to jump on the bandwagon with a friendly PSA: If you go and buy that giant inflatable snowman to keep up with the Joneses, you’re playing into positive feedback loops, and before you do that you should know that nature abhors stagnation. I don’t mean to be a shit, but positive feedback loops are destructive like sneaky, malicious hurricanes and that’s bad news for you unless you’ve got something you want destroyed.

main-street

Now, there’s a time and place for creative destruction and weirdly enough network science can help you find it. Hang on a minute and I’ll illustrate.

When I was fifteen my English lit teacher gave me this really old copy of Main Street. It wasn’t the best book I ever read but it really got down on the physiognomy of boredom. Through his tale of Carol Milford, a cosmopolitan young woman who moves to a small town with her new husband, Sinclair Lewis meticulously unpacked the pathology of sameness as a slow, painful killer of culture and community. The story went boldly into the beige, laying bare the domestic misery that it seemed to my teenage self everyone was ignoring. I was repulsed and impressed. I remember climbing onto the roof of my suburban house gasping for fresh air, wondering, “How small can people really make their worlds?”

Pretty small, it turns out.

Fourteen years later (a Saturn cycle! the astrologer says, as if everyone should purse their lips and nod their heads), and I’m sitting in couple’s therapy with my (now ex) husband. What’s that hanging over the counselor’s desk? Why, a familiar illustration from a limited edition first printing of Main Street.

So now I’m paying attention.

On the other side of divorce I became fascinated by the psychology of boredom. Whose fault is the unhappiness that results? Should you blame circumstances or yourself? The small town, or your own resistance to conformity (a cultural positive feedback loop)? Is there a link between boredom and intelligence?  On the other side of the same coin is curiosity, which does have a link to intelligence. Studies show that a tendency to report frequent feelings of boredom, a trait scarily prevalent among people with narcissistic personality disorder, may be a function of the quality of one’s self-awareness. Boredom tendencies run higher in individuals with lower absorption (a measure of attention span–no surprise there) and in individuals with negative self-awareness tending toward evaluation and judgment. No wonder narcissists, who constantly seek external means of self-validation, are notoriously whiny about their listlessness. Boredom is something we all experience at one time or another, and it may have an important evolutionary function: inciting experiences of pattern interruption. But before it does that it can make you stupid and dull. Temporarily.

In his article “The Surprising Power of an Uncomfortable Brain,” Garth Sundem, author of Beyond IQ and Your Daily Brain, illustrates with friendly snark that “a brain shocked from its easy complacency functions better than a brain kicking along on autopilot,” whereas the repetition of familiar situations can lull your brain “zombie-like into the halls of mindless consumption.” In his article, Sundem sources several cognitive research experiments showing that people whose brains encountered situations where expectations and reality were mismatched performed better on cognitive tests because their brains switched from associative to rule-based systematic processing. The English: encountering the unexpected wakes up your brain. Anything that enforces “cultural dysfluency” should do the trick. Including culture shock. So while the culture shock of moving to a new town may be temporarily invigorating, the newness eventually wears off and the sameness can be stifling, prompting a person to seek new forms of pattern interruption.

So let’s assume you’re Carol Milford of Main Street and you want to look at boredom as a function of your network:

Depending on your own biases you may think people are chaotic or predictable, but they’re not either of these things all the time. What they are is complex, meaning they’re affected by all the feedback loops that run between them and their environment. Boredom is the product of a feedback loop between your brain, your environment, and your perceptual narrative.

You should know that complex networks (personalities, relationships, markets, and even Main Street) are characterized by feedback and have three tell-tale behaviors. If Carol Milford had understood network behavior, she might’ve take more responsibility for her own happiness from the get-go, moved somewhere more interesting and saved herself the effort of trying to transform the culture of the town. If you know these tendencies you can save yourself a lot of trouble, and if you bear with me I’ll tell you how.

Attractors – these are places where the network is moving toward some kind of equilibrium. The beginnings of order. (In our Main Street scenario, something happens and people are drawn to a certain type of behavior).

Self-reinforcement – where order begets more order. If the nodes in a network are the interconnected lives of Main Street, this is where they all keep doing the same thing because “that’s the way it’s done” and there must be some reward for doing things that way. The positive feedback loop continually validates & perpetuates itself in ways that are pretty much invisible unless you’re on the outside looking for them.

Cascades – these are shifts in direction caused by an outside intervention or an internal breakdown, as when a positive feedback loop has become so homogeneous as to be unsustainable and fragile to outside disruption. A cascade rips through it once and the network is never the same again.

Good? Bad? Neither inherently, because we aren’t talking about an abstraction–we’re talking about the fundamental structure and behavior of complex systems, and positive feedback loops always undo themselves. They either accept diversity and pivot toward greater resilience or they cascade and become something else.

Take heart though. Boredom and disruption go hand in hand, like everything else with its opposite (ever had a week of artistic frustration only to have a colossal breakthrough on the other side?) Periods of boredom and listlessness in human beings often spur discovery. In business, innovation clusters explode when a company breaks the lack of competition (a positive feedback loop) by doing something different that the network was ready for: disruption. Eventually you have the Big Idea, or someone else has it for you, because The Next Big Idea is always riding the cresting wave of the network.

The way the world works is fundamentally about linkages. Taiji master Ben Lo said that whenever you embody yin you also embody yang. A system always embodies the whole circle, and here is where the power is, because it allows for movement into one state to create disequilibrium, which incites a system to move and change in order to regain equilibrium. Nothing can be yin without yang. So if you think about it in terms of network dynamics, boredom is your signal to seek a new stimulus (internal or external) or it will seek you. One way or another, everything in a complex system shifts.

Again: A local network either invites diversity and changes, or it stays the same so long that it becomes fragile, unprepared to adapt to perturbations from its external environment. Then a germ comes along from across the pond and destroys an indigenous population, or incumbent tech company doesn’t see the little guy rising up in time… or a marriage runs into trouble and doesn’t make it. Either way change comes and you get to choose a new direction.

If you aren’t designing for emergence you might get comfortable and mistake positive feedback loops for equilibrium–when what they really are is pent-up order. Emergence will happen anyway. Novelty always prevails over habit, else networks crumble and end up on the forest floor, where as cultural detritus they give new life to emergent forms. This is the way of life.

Acrobats know that you have to move constantly to find balance and stillness. Sometimes those movements are imperceptible, but they are what allow you to keep your footing.

No doubt Sinclair Lewis quelled the demons of his own small town boredom by creating a world where he could shine a light on its secret interiors. For Main Street‘s Carol Milford, emergence did not produce a cultural renaissance in Gopher Prairie, as she’d hoped. A lot of people (myself included) got pissed off about that. But she was a network of one, and did not have the agency with which time and entropy eventually overcome all homogenous networks and the small towns that personify them. Instead, emergence produced in the small networked world of her mind a new way of seeing, a new frame of mind–one that told her she’d be ok no matter what happened. This peculiar marriage of aloofness and intent is the sweet spot where a human being can find agency in a network.

Memes matter, but not so much as mutability. Designing for emergence, or as Alfred North Whitehead might have put it, seeking ordered forms of novelty and novel forms of order, produces the lucky buds of change that networks nurture into memes, which, once they spread, flower into disruption. What happened when readers of Main Street integrated what they saw there into their own worlds certainly changed some minds, an emergent process that continues in immeasurable ways to this day. (Otherwise people wouldn’t hang it over their desks as a symbol of personal transformation.)

Main Street isn’t real. It exists in your imagination and you can leave at any time. The self-organizing nature of the universe always pulls novelty from the battle between order and entropy. Boredom leads to discovery. So before you go and copy someone else’s strategy, sit with your boredom for a while and allow the network to enable emergence.

Network dynamics dictate that everything changes, and you get to choose whether to accept that or the inevitable cascade that comes to wash away the sameness. Either way, we promise it won’t be boring.

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.

“Ideally, we want News Feed to show all the posts people want to see in the order they want to read them.” -Facebook

What Are Algorithmic Values?

From our perspective, the purpose of a recommender algorithm is simply to give us the content or products we really want to see.  There is a problem right off the bat. We don’t know what content or products we really want to see. If we did, we wouldn’t need a recommender engine! In steps a type of algorithm called, “collaborative filtering”. If you’ve viewed all the Judd Apatow (Director of Knocked Up) movies, the algorithm could observe that other Apatow fans have also ordered Anchorman. Out pops the recommendation.  What? You’ve already seen Anchorman five times on a separate site, or at a friend’s house? The recommendation is just noise.

Here is the thing, though: it costs the site very little, on the margin, to deliver a recommendation with no value. Any increase in incremental clicks from populating your recommendations is gravy for them. This creates a fundamental imbalance: your time is more valuable to you than it is to the algorithm. Any improvement is clicks or time spent on the site benefits their designer’s bottom line, even if it doesn’t quite benefit you as much. Netflix gingerly steps around this issue: “We are not recommending [a movie] because it suits our business needs, but because it matches the information we have from you.” Hmmm… It might be more accurate to say that using the information they have from you serves their business needs.

What’s In It For Us?

Well, watching the TV news, for instance, or searching through IMBD for a movie, or asking a friend, they also take time. Either way we have to wade through useless information. Perhaps the Facebook News Feed algorithm is a big improvement. Or maybe it isn’t, but we are on Facebook a lot anyway, so why not. We learn to adjust to recommendation noise over time, perhaps mentally filtering out irrelevant stories or obtrusive ads as we, say, read through our Facebook News Feed. This last dynamic is important. “Digital inertia” keeps us walking down the path that the sites we depend on have laid out for us. Once we swipe through online dating profiles at a mind-numbing pace, well, we just get used to it. This is “just the way things are”. In a sense, we are trapped by this new world-view. Whatever values we had before, now we have a set of new ones that benefit the algorithm provider. As our accompanying interview with Spritzr dating app CEO Manshu Argawal describes, this shift in values may not be to our individual, or especially, our collective benefit. After all, when we enter the portal that we expect will connect us to a whole world of possibilities, what we’re really hoping is that it’ll be the scaled up equivalent of taking a walk down a friendly street we’ve never been down.

You might not think that algorithms are all that invasive. After all, the Internet is huge and full of noise (and sometimes, rife with dumb.) It’s a self-organizing map, a web of connections whose pathways are forged by whatever pilgrims made them first. Just like ants find scent trails by detecting the pheromones of the hungry ants who traveled before them, or the way neurons that fire together wire together, we leave a trail when we go from one site to the next, and that trail is recorded by an algorithm that assumes we liked our route. And so it recommends future itineraries based on what we’ve already seen before. That’s great – better than if it hadn’t paid attention at all, right? But what happens when the recommending algorithm knows you too well? Perhaps you roll over in bed one morning and open your news feed and it anticipates your interests so accurately that, to your dismay, the app that once made you laugh into your morning coffee or forget all about your boring train ride no longer has anything interesting to say?

There’s almost nowhere we go that we don’t take our mobile device with us. There are no more closed doors. It’s seen our embarrassing searches and medical questions, it knows all the dumb vines we liked. We can’t go back to first dates and first impressions. It thinks it knows who we are. Will we fall out of love?

Mystery, discovery, surprise. These are on the mind of Jarno Koponen, a network science enthusiast and developer of Random, the App. It’s guys like this you might expect to design something like the frighteningly capable and caring AI companion in the movie Her. Koponen seems to understand that the Internet, as a complex network, is in a sense a wild frontier that fluctuates between signal and noise, order and chaos. Too much chaos and links are weak, and you’re on your own in the search for relevant information. Too much order and you could get stuck on Main Street, your preferences over-defined by algorithms that attempt to guide you by making assumptions about your activity and comparing you to others. Learning algorithms are humanity’s early attempt to curate culture and relevance just like we have done on every other frontier. But these algorithms now need to learn boundaries, need to learn when we need some space to take a walk alone and be surprised.

And so dawns the age of the discovery engine. There are lots of ways to invent one, but no one’s yet done it comprehensively.

Koponen proposes the creation of personal algorithms, an “algorithmic angel” if you will, that would give us better visibility into the kinds of things that affect what information is curated for us. Today that information is mostly kept safe and proprietary by the designers of the interfaces we use. For instance when you like or comment on a post in Facebook, you don’t know exactly how that will affect your feed. These personal algorithmic systems would be ours–an ambient layer on our explorations that would be truly personal, evolving with us as individuals, taking our values into account and adapting to us as well as providing a means for discovery. They would interface with recommending algorithms, keeping them in check and making sure we have priority agency in the content environments we explore.

“For many people personal data is abstract,” Koponen says. “Generally we don’t have a lot of awareness about how our data is being used and how it affects what we see. How could this data be powering experiences that are more in tune with who we are as individuals?”

An Experiment in Discovery

Kopenen’s app, Random, aims to make your subjective reality a starting point when recommending new topics and content. The New York Times described it as minimalist and almost toylike, probably because it’s simple and yet it inspires curiosity.

algoportrait1

Random presents you with a screen tiled full of topics and when you click on one, it gives you a bunch of articles related to that topic to choose from. That helps the algorithm learn quickly, and each time you open it, your spread of topics is a little different. There are familiar subjects and some that make you go “hmm…”

“It doesn’t have a lot of utility yet,” Koponen says modestly, “but as a paradigm it could be made more comprehensive and approachable, to evolve into the kind of experience that gives you even more agency.” Like the algorithmic angel that hasn’t quite been invented yet. As the AI researchers in our podcast feature pointed out, discovery is an important part of the human experience, and so it should be an important part of what our technology enables. Currently, Random learns and adapts to your preferences but also uses the network map of this data to enable surprise and discovery–to create a balance between relevance and serendipity.

Let’s say you’re into design, sushi, Apple, and travel. In Random, these are not categories per se, but points in a huge network graph that create your personal profile in the universe of the app. Nothing is truly random, of course. Surprise comes from:

1. Your personal choices

2. Expected correlations with other similar people

3. Trending topics

Where trends are concerned even though that particular connection may not be found in your profile, these topics are so popular at a given time that it’s likely that you’ll be interested in them. There was a bombing in Paris. Paris is something you’ve shown a lot of interest in, but you don’t always want to hear about bombings. Random takes that longer arc into account.

To take you beyond your current personal interests into new territory that won’t feel obtrusive, Random does an interesting pirouette, leapfrogging behind the scenes using subtle links within the content you consume. It looks for stepping stones. You might ask why you suddenly see an article on algae.

“Because of the interface and its underlying dynamics, it’s possible every now and then to bring in a wild card,” Koponen says.  How is that different than anything Facebook or Twitter or Pinterest does? Because it’s just one of many choices that are presented to you, not an ad you have to look at.

You might like design, so somewhere back in the articles you read or someone like you read there was a design article that was related to bioengineering and had to do with algae, and it somehow involved the design process. So now there’s just one suggestion for algae, and you don’t have to click on it unless you’re curious. There are many other choices.  (Personally, I’d be curious enough just to know what the connection was).

What Does the Future Look Like?

Koponen is a humanist, so he’s always asking technology how it’s taking our personal values into account when it uses our personal data, because what we consume feeds what we create, and all of this adaptive content universe will affect how human culture is curated–in other words, what our future looks like.

We want what we want, and even that’s hard enough to figure out, much less explain to a computer algorithm. That’s because most of those preferences bubble up from the subconscious, a far more complex network than anything we’ve ever built. We don’t want to look at the same things we’ve always seen before, but we don’t want to be insulted by stuff that’s too far out–  jarring experiences that break our technology’s rapport with us.

We are creating our world even as we experience it through our unique perceptual filters. It shouldn’t come as a surprise. Machine learning–recommendation, discovery–is only reflecting that process and making it more obvious.

We created different media to ensure that we have access to the information that we consider valuable, meaningful. Something worth keeping. The key here, Koponen says, is that there will be technology creating information for us, that can be used as a mediator to curate things for us. Culture is a repository of our connections, and it connects us to one another. But it also thrives on diversity. When machines are curating culture, we want them to understand that reality is subjective, but when it becomes too subjective it isolates us.

Personally, I want to understand how my culture, my network, is evolving–especially when machines are creating and making choices about the world that I see. Send me an angel already.

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.

“Mama always told me not to look into the eyes of the sun, but Mama that’s where the fun is.”

This line, from Bruce Springsteen – perhaps made famous by Manfred Mann – could well have been said by Toby Shannan, Shopify’s SVP of Support Operations.

You see, Shopify made its way from startup to a $5.2B valuation in 9 years by solving problems in the interstitial spaces between small business and e-commerce solutions, and between companies that don’t solve customer problems in “a hands on” way and those that do. The market has rewarded Shopify with hyper-growth for solving small business e-commerce headaches. That is the joy of integration – at least for Shopify.

The sorrow? All of Toby’s tech support issues are at the interface between different vendor solutions.  This is the burden of integration.

At Dialog’s recent network design symposium with Santa Fe Institute, Toby opened his seminar by quoting SFI’s Will Tracy as saying, “The edges are where the action is in a network.” By ‘edges’ he meant the links of a network. (That is one reason why networks are best thought of in terms of “flow” or the connections between. As It turns out that in all fields, connectivity is the main source of innovation. According to the recombinant DNA theory of innovation, the only way you can create something new is to bring together two previously uncombined elements.

In an open plug-and-play ecosystem, you can create virtually anything when compared to a closed ecosystem. However, this places a greater need on the role of integrator, whether that is the end user or a professional intermediary. In a closed ecosystem, the ecosystem sponsor takes on more of the task and decisions of integration. (Think Apple vs. Microsoft or Android).

All ecosystems, especially open ecosystems, require Integrators. Bridge builders. Translators. Renaissance men and women. That’s what we need more of as we race forward, ever-faster, pulled by our technology and self-reinforcing momentum, into deeper and more sprawling amounts of knowledge.

Specialization and exchange has created our world, but it will take renaissance men and women to keep it whole.

We need a unified worldview, right now. We can no longer afford brokenness. We can no longer afford to look at or manage problems in silos.

All silos are constructs. Organizational insiders can always tell you the informal network by which work really gets done. What is really there is a network, a series of nested ecosystems both formal and informal.

Toby manages his support and sales operations as one seamless function. In doing so, he avoids the usual escalated customer service issues that arise in the cracks between sales, customer service and tech support. People usually think of “product integration,” but “service integration” may be the secret of Shopify’s success.

In solving interstitial problems, Shopify’s team has found the same joy that Tim Cook found coming to Apple and working at the interstices of hardware, software and communications. In Tim Cook’s words, that’s where the magic is, at the boundaries.

There’s increasing business opportunity in connecting the network to itself.  With that in mind, the next time you see an integration problem, you just might see it as an opportunity.

And if you are lucky, like Shopify, it could offer you a 10 figure valuation.

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

Different diseases spread differently. A simple observation, but with a level of complexity behind it that is worth understanding. It is not solely the infectiousness of a disease that affects its spread; the type of network the infection is dropped into plays a crucial role.

For many years, epidemiologists assumed a “mass action model” where the rate of infectiousness of a disease was a fixed number (basic reproduction number or R0), representing the expected number of secondary cases produced by a typical infected individual, early in an epidemic. For the flu, for example, it is roughly 2. Meaning one infection leads to two secondary infections, on average. Comparatively, for measles, it is 18.

In practice, the actual rate of spread is significantly impacted (and R0 effectively modified) by the host network: by both the density and the structure of that network.

At our recent network design symposium with the Santa Fe Institute (SFI), Lauren Ancel Meyers (Professor of Integrative Biology- University of Texas; Faculty- SFI) shared three simple network maps to illustrate how network structures influence the spread of epidemics. Although there are sophisticated mathematical descriptors of network structure, some simple explanations can easily demonstrate the range of impacts possible.

At the front of this article is a picture of three networks with the same number of nodes, but interconnected differently. The degree distribution (number of links each node has) varies across them, from only 2 on the left, to a range of 1 to 6 on the right. Two simple questions for you: 1) Which network will be the most susceptible to an epidemic? and 2) Which is likely to sustain the largest epidemic?

Are the answers obvious?

Networks with the greatest “degree distribution” (like the network to the right) are the most vulnerable to an epidemic. Variability implies vulnerability. But when an infection occurs, homogeneous networks (to the left) fare the worst.

Think of it this way: variability (or diversity) provides the greatest openness to the introduction of something new (whether virus or message), while homogeneity presents the greatest risk (or opportunity) for spread once established in the network. Beyond epidemiology, Meyers’ research has huge implications for the spread of ideas and information.

Take a Random Walk to Find “Super-Connectors”

Another network task in epidemiology is a desire to find so-called “super-connectors” because the likelihood of spread of a disease rises exponentially if “hubs” like these are infected in an outbreak (think of the highly connected nodes in the far right diagram above). It turns out super-connectors are not that hard to find. Randomly polling individuals in the network offers broad situational awareness and then asking to speak to one of their colleagues inexorably leads researchers towards super-connectors. Thus a random beginning can quickly lead to highly connected nodes. So, in any new network you enter, simple inquiries can uncover important understanding. And just as all rivers lead to the sea (or as Josh Baer joked, all tech in Austin leads to the Capital Factory), one can inevitably find “super-connectors.”

The infectious nature of an idea: Does it engage head and heart?

If you take Howard Gardner’s perspective, a cognitive leadership model recognizes that effective leaders “speak to narratives already present in their audiences’ minds.” For both organizational leaders and marketers alike, we not only need to understand the infectious nature of an idea and map the network, we also need to map the narratives already in existence in the network. In a future post, we’ll explore more on mapping narratives and the current state of a network’s mindset.

Thus, the capacity for an idea to spread depends on several factors: the content (infectiousness) of the idea itself (R0), the narratives already present in peoples’ minds, and the structure of the network in which the idea is released. This makes mapping networks and narratives key to orchestrating the spread of ideas, and yet, so few firms practice systematic network or narrative mapping. So, whether it’s epidemiology or the spreading of ideas, understanding the relevant network is crucial. Stopping the spread and accelerating the spread are simply two sides to the same network coin.

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

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

SXSW_Obama_DialogBlog

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.