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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. 

Science isn’t a club. It’s a cultural activity, and it should be participatory. But if it were a club, these people would have made it a whole lot cooler.

“We are the kids who got in trouble in chemistry lab for setting the things on fire that we were not supposed to set on fire.”

That’s the official description of the people who made Experiment, a crowdfunding platform for scientific research. Crowdfunding and crowdsourcing have been a game-changer for many industries, including finance, humanitarian relief, startups, entertainment, and even the military, and the concept has has come to work some network magic on science. Science funding has traditionally been controlled by a few institutions and focused on their objectives. The network age, though, lends us the opportunity to widen those pathways to a greater number of scientists and a greater diversity of ideas.

On Experiment, you’ll find active research in rare diseases, dinosaur excavation, vitamins and eyesight, zombie ants (our favorite), and the scientific rigor you might expect from a couple of scientists who developed anthrax medicine for the Army.

Science tells us that innovation is path-dependent, as we learned from our interview with AI researchers Kenneth Stanley and Jeff Clune. A network that includes openness and diversity makes discovery and innovation more likely.

In The Competitive Advantage of Nations, Michael Porter introduces the idea of strategic clusters. (You know — Italy makes good shoes, the Valley spawns startups, etc). The gist of it is that innovation doesn’t just come from companies; it comes from ecosystems. The “stepping stones” to the next big thing arise from the surrounding network, often without a direct relationship to an objective. Experiment.com makes it possible for the stepping stones to the next big scientific breakthrough to come from untraditional channels.

design4emergence asked Experiment co-founder Cindy Wu about the path that led her and Denny Luan, undergrads at the time, to launch this curious startup experiment in 2012.

It was an ordinary summer for Cindy and Denny, resequencing proteins to fight anthrax…


d4e: How did you get the idea for Experiment?

Cindy Wu: Denny and I were last-year undergrads, and with a group of other students we had just designed an anthrax therapeutic for the Army. We used a crowdsourced video game where you can put up these proteins online and play this game to alter different parts to see if they come up with a new drug or probiotic. We made 87 different versions of that protein that summer. One was able to decapsulate the protective coat on the outside of anthrax bacteria.

The reason anthrax is so lethal is that when it enters your body, your body misidentifies it as safe, and so it spreads. But if you’re able to take off this protective coating, your body will recognize it as foreign and immune system will fight back.

We presented that research at the largest synthetic biology conference at MIT, and published the research, and the Army’s now doing follow up work on it. What we found is the same drug we created could also be used as an antibiotic for more generic bacterial infections in the hospital.

We needed like $5,000 to get that project started because we had all the techniques, we just needed to buy a few reagents. When I asked my professor where she could get grant money he just said, “Look Cindy. You’re an undergrad. You don’t have a PhD. The system just doesn’t fund people like you.”

So that’s when we decided we were just going to solve our own problem. If the government didn’t want to fund young scientists just because they didn’t have a PhD, then maybe the Internet could. We took a lot of inspiration from Kiva.org, which is a microfinance site. Denny had the idea of building a Kiva for science. We didn’t really know what that looked like, so we decided to just try it. We got nine of our professor and grad student friends to put up projects on the site. We funded six out of those first nine and never looked back.

d4e: What was the response from the scientific community?

CW: The majority of our users are professors and grad students at academic institutions, although we do allow anyone that has a research idea to propose projects on the site.

Over time, academics have become really interested because they think it’s a good way to fund really early stage research.

d4e: What types of experiments have you seen that wouldn’t be likely or possible elsewhere?

CW: There was a project where researchers tried to alter their vision to be able to see infrared. They haven’t published the results yet, but that’s something that probably wouldn’t get funded in the traditional realm.

There’s one experiment that actually uses the crowd to collect the data. He has ordered corn that is GMO and non-GMO. It looks identical. He’s sent it to all his backers, and his backers put it in their yard and see which corn the squirrels or other animals prefer. That part of crowdfunding and crowdsourcing is unique.

d4e: Is it usually research scientists carrying out the actual experiments?

CW: Most of the people proposing research on the platform are the ones actually carrying out the experiments, but we do have projects where people went through the literature and saw something they wanted to test, and then partnered with an institution to do the research.

For example there was a husband and wife team, and the wife found out she had a rare prion disease. Very little is known about prion diseases, but they found a compound that they wanted to test. Once they funded their research on Experiment, they applied to grad school, and now they’re both PhD students at Harvard Medical working on prion research.

One of the projects that’s raised the most on Experiment is run by a dad who found out both of his daughters had Batten disease. He did a literature search and found that there was one doctor in New Zealand who had treated the same type of Batten in sheep, so he’s replicating that study and using the rest of the funding for other types of gene therapy. I think we’re going to see a lot more research in rare diseases. This happened even before crowdfunding, where parents would take research into their own hands, and often they become experts in the field because they’ve read every paper that’s been published and talked to all the scientists.

d4e: What unmet demand is being served by Experiment?

CW: The most important thing that this allows scientists and avenue where they have full control over whether or not their research starts. In the traditional grant system you apply for a grant and maybe wait for a whole year before you figure out whether you get funding. With crowdfunding and Experiment, scientists put the idea up, get the money within 30 days and try it out, and if it works, run another campaign or use the preliminary data to go after a larger government grant. That was never the case in the past. The closest scenario we had before would be a faculty member going to a department head for some startup funds from the discretionary budget for some early stage research, but because research funding is drying up, it seems like those opportunities are dwindling.

d4e: What makes a successful Experiment?

CW: The most important thing is for the project to be well defined and for the researcher or whoever’s running the campaign to be very committed to the campaign, and to engage the community after it has run.

d4e: What’s new?

CW: The Journal of Results. People always wonder, once you fund a project, what do you get? The Journal was the first time we aggregated results from finished projects. That closes the loop on what is the reward on giving to science.

d4e: What are your goals for the network you’ve designed?

CW: We want to create a world where anyone can be a scientist. We want to be the first place people go when they have an idea for a scientific project, where they can share the results with everyone who has access to the internet.

I think the majority of the research will be executed by people in the public. And it should be, but it hasn’t been that way because to get funded by mainstream sources you have to have a PhD. Our network gives more access to more people (everywhere, including underserved communities and countries) who have the ideas that will push science forward.


So far, Experiment.com has rounded up over $5 million in funding for research and 19,000 backers, resulting in 20 published papers funded.

You never know where an idea will come from. No one knows this better than designers and our kindred spirits, scientists and inventors. The greatest leaps forward emerge as much from a network as from the genius of a single mind. Sometimes, the right objective for designing a network is discovery itself.

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.

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