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. 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, 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, 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

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.

Near a window, steam rises in spirals from a cup of tea. A column of sunlight intervenes, producing emergent ribbons of heat and ephemeral color. It’s an ordinary cup of tea, and steam is an ordinary product of nature’s processes. But to say that there is no art in nature is only a question of agency and scale.

This is a question Anders Hoff might ask. He is a search engine consultant and a mathematician in Oslo. He is also an artist, but a kind of artist that’s new on the scene, one with a different sort of medium–the kind you’ll start to see more of.

Anders is an artist of networks.

When he’s not spending six to nine hours a day solving complicated problems in search, he’s most likely listening to metal and using math to create generative artwork like this:


It takes a few days to produce something like this. He writes a type of mathematical function, a generative algorithm, that defines the behavior of an agent-based data set. An agent is just something that acts on a network of things. When you’re creating an artificial network on a computer, you can define what you want the agents to be and do–to a point.

The agents are linked to other agents, and you can give them simple rules, but–like the simple rules in nature that create incredibly complex patterns–the agents are part of a network, and together they evolve the overall network in ways you can’t anticipate.


In the case of this piece, which Anders describes as something akin to flower petals, or cabbage, or the inside of your intestines, the agents are the vertices of the mesh. You start with a simple triangular mesh and the vertices interact with each other in some way. Every vertex is connected to five or six other vertices. This creates the whole mesh–the network. These vertices act on simple rules that attract them to their neighbors and cause them to avoid the unconnected vertices of their mesh. In the end (if there is an end), it’s the complete network of all the vertices interacting that makes the mesh move or behave and evolve as it does.

He creates with a beginner’s mind.

“I like how you can get interesting structures arising from these simple rules,” Anders says, and it’s that simple curiosity that drives him to create algorithms that evolve into living artifacts that resemble familiar patterns in nature, analogs to biological mechanisms, like this:


He doesn’t have an end result in mind. “I’ve tried to make it as naive as possible and still see if I can get that behavior. Nature doesn’t solve differential equations, but nature does evolve – so I want to make something as naive as possible.”


Sometimes he sets out to make a thing, and he makes exactly that one thing. But most times he starts with nothing other than a vague idea, perhaps an interesting mesh that someone else has made, and plays with it until he’s tired of it. That’s how this all started. When he was 16, Anders stumbled upon a site called Complexification, which was created by Jared Tarbell, one of Etsy’s cofounders. The site featured animation created by agent-based systems. Anders started playing with Tarbell’s algorithms, copying them at first and then making his own original designs.

Years later while studying physics and working on his master’s in numerical mathematics, Anders stumbled upon Complexification when he was supposed to be cramming for his finals, and out of curiosity and a bit of procrastination a whole portfolio of generative art emerged.


Back to our agency question. Where does the work of one network artist end and another’s begin? How does the artist know when the art is complete?

With art that grows, it’s difficult to say. When it feels complete, when it feels original, when it’s time to do something new.

For Anders, it’s a curiosity-driven process, the reward of creating simple behavior and producing something interesting. He puts his algorithms on github for other coders to play with and hopes someone will make something entirely different. Evolution is the propagation of novelty in nature, and even in art—where nature and the subconscious are exposed in complicity.

In an emerging medium like generative algorithmic art, you could think of the network itself as the artist. A network of artificial agents designed by a network of generative artists, evolving through each node, shown to other networks as art.

“The waking have one world in common, whereas each sleeper turns away to a private world of his own.” – Heraclitus

To learn how Dialog can help your business, contact us at 512.697.9425 or

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.

The data suggests that the creativity and problem solving model of education leads to the highest levels of reading, math, and science test scores:

Finnish students spend fewer hours in school than their U.S. counterparts and even hours in the classroom than students in Shanghai and South Korea. This is significant because the PISA study, conducted every three years by the Organization for Economic Development (OECD 2009), consistently ranks Finland, Shanghai, and South Korea as the top countries in reading, math, and science test scores.


When Finland topped the list during the first year of the PISA study, many Finns thought that the results must be a mistake because the Finnish school system was never intended to manufacture academic excellence, but alternatively was born of the lofty goal to give every Finnish child exactly the same opportunity to learn irrespective of family background, income, or geographic location. So how does a country spend less time instructing students and still attain the highest test scores in reading, math, and science without even trying? The explanation for what is now referred to as the “Finnish Miracle” is that numbers do not drive people, but people and relationships drive numbers. More specifically, test scores and accountability do not drive people to excellence, but engagement, curiosity, collaboration, and creativity drive students as well as teachers towards academic excellence.

In contrast to the Singapore and South Korean school systems, Finnish schools spend very little time testing students. In fact, Finland uses no standardized testing to measure students’ progress from year to year. The only standardized test that Finnish students take is called the National Matriculation Exam (Partanen, What Americans Keep Ignoring about Finland’s School Success 2011), which every student takes at the end of his/her high-school education. In place of standardized testing, teachers are encouraged to develop their own curriculums and are taught how to develop their own evaluation and assessment techniques.  Without the need to teach-to-the-test, Finnish teachers have more time to teach students using the best-known education techniques. Instead of lecturing, Finnish teachers spend the vast majority of school time directing hands-on, project-based learning (e.g., exploring in nature, cooking in the kitchen). This approach to education engages children and inspires them to think creatively, to collaborate, and to become problem solvers.


Technology companies have the opportunity to launch themselves into the forefront of thought leadership in the K-12 education space by involving itself in solving a global and magnificent problem that is aligned with the philosophy of education, creativity, and problem solving:


Example Problem: Over the next decade, there is a projected global shortage of 18 million teachers—2.3 million of those in the United States. How will we avert the “Darfur of children’s futures in terms of literacy”? (Kotler and Diamandis 2012)

In 1999, the Indian physicist Sugata Mitra discovered that engaging children to learn, be creative, and become problem solvers could be accomplished without any teachers at all! “Mitra designed a simple experiment. He cut a hole in the wall and installed a computer and a track pad, with the screen and the pad facing into the slum. He did it in such a way that theft was not a problem, then connected the computer to the Internet, added a web browser, and walked away. The kids who lived in the slums could not speak English, did not know how to use a computer, and had no knowledge of the Internet, but they were curious. Within minutes, they’d figured out how to point and click. By the end of the first day, they were surfing the web and— even more importantly— teaching one another how to surf the web. These results raised more questions than they answered.” (Kotler and Diamandis 2012) Mitra continued his experiments by making small adjustments to the learning environment. For example, Mitra recruited grandmothers from across the United Kingdom to donate one hour of their week providing encouragement to these Indian children via Skype. On average the “granny cloud”, as Mitra called it, was found to increase test scores by 25 percent. In Mitra’s next experiment he accomplished the unthinkable: after four months of self-organized learning, Mitras students (only 12 years of age) were able to achieve test scores in the subject of biotechnology equal to the average scores of high-school students studying biotech at the best schools in New Delhi. This and other milestones led Sugata Mitra to formalize a learning method called “self-organized learning environments” (SOLES).

The Sugata Mitra example was made popular only recently by the book Abundance upon its publication in February of 2012. The author states most inspiringly, “If what’s really needed are students with no special training, grandmothers with no special training, and a computer with an Internet connection for every fourth student, then the Darfur of literacy need not be feared. Clearly, both kids and grandmothers are plentiful. Wireless connectivity already exists for over 50 percent of the world and is rapidly extending to the rest. And affordable computers? Well, that’s exactly where the work of Nicholas Negroponte comes in.” (Kotler and Diamandis 2012)

Education Technology companies have a window of opportunity to galvanize the support of Education Thought Leaders who are now reading this book and wondering how they can make a difference. Rallying support and involvement around a huge education issue of this sort is an opportunity for Adobe to become a thought leader in the K-12 education space.


Kotler, Steven, and Peter H. Diamandis. Abundance. Simon & Schuster, Inc., 2012.
OECD. PISA 2009 Results. 2009.,3746,en_32252351_32235731_46567613_1_1_1_1,00.html (accessed May 22, 2011).
Partanen, Anu. What Americans Keep Ignoring about Finland’s School Success. December 29, 2011. (accessed May 22, 2012).
—. “What Americans Keep Ignoring about Finland’s School Success.” December 29, 2011.

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)
[4] West, Geoffrey (2011-07-XX)
[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:
[9] Megacity growth = the growth in population of the ten largest US cities
[10] Megacity populations:
[11] US Population:
[12] Patents:
[13] Patents:
[16]  times more innovative
[17]  times more innovative

The surface of American society is, if I may use the expression, covered with a layer of democracy, from beneath which the old aristocratic colors sometimes peep.

This is a description of America democracy, by French aristocrat, Alexis de Toqueville, in Democracy in America I (1835), only 48 years after the founding of the United States. He explains that the vestiges of aristocracy still exist because the American majority is not familiar with civil law and does not question it:

Civil laws are only familiarly known to legal men, whose direct interest it is to maintain them as they are, whether good or bad, simply because they themselves are conversant with them … The body of the country is scarcely acquainted with them … and obeys them without premeditation.

In other words, there are civil laws (laws regulating private relations) in society that still reek of the English aristocracy because the majority of citizens never question them. So, what are these aristocratic civil laws of American society? According to Alexis de Tocqueville in Democracy in America II (1840) the most aristocratic, civil structure of American society is the top-down business structure:

Manufacturers may possibly in their turn bring men back to aristocracy … When a workman is unceasingly and exclusively engaged in the fabrication of one thing, he ultimately does his work with singular dexterity; but at the same time he loses the general faculty of applying his mind to the direction of the work … as the workman improves the man is degraded … On the other hand, more considerable, wealthy and educated men come forward to embark in manufactures … The magnitude of the efforts required, and the importance of the results to be obtained, attract him. Thus at the very time at which the science of manufactures lowers the class of workmen, it raises the class of masters.

Whereas the workman concentrates his faculties more and more upon the study of a single detail, the master surveys a more extensive whole, and the mind of the latter is enlarged in proportion as that of the former is narrowed. In a short time the one will require nothing but physical strength without intelligence; the other stands in need of science, and almost of genius, to insure success. This man resembles more and more the administrator of a vast empire–that man, a brute. The master and the workman have then here no similarity, and their differences increase every day…the one is continually, closely, and necessarily dependent upon the other, and seems as much born to obey as that other is to command. What is this but aristocracy?

When workers focus only on the tasks at hand and do not apply their minds to the direction of the work they become narrow-minded and dependent. On the other hand, managers must see the big picture, be leaders, and envision the future. Over time, the divide becomes so large between managers and workers that managers despise workers’ lack of perspective and workers despise managers’ lack of empathy. This is aristocracy, and when the majority of citizens are in this environment, as is the case today, managers and executives are treated as kings and geniuses and paid hundreds of times more than workers. In the political arena, these same workers/citizens treat their political managers as kings and geniuses. Politicians become saviors and heroes that will rescue us and fix the problems of the people. These unfair expectations of our leaders cause deficient governance and nurture the seeds of aristocracy and the dependency of men. If left unchecked, America may fully become an aristocracy–or more appropriately a plutocracy (i.e., rule by the wealthy).

On a brighter note, de Tocqueville offered consolation. He saw that some Americans had “commercial and manufacturing companies, in which all take part.” At the beginning of our democratic experiment, many Americans could see as de Tocqueville did that entrepreneurially-minded, informed citizens that open their minds to opportunities and willingly take risks are necessary to maintain our democracy. Democracy cannot exist among human machines that have no vision of the future.

The top-down business structure is a trace of English aristocratic control that grooms the majority to be dependent upon business and political managers. Moreover, the top-down business structure (as defined by civil law) is one of the most difficult aspect of our society to change. If we are to change it and if we are to improve our democracy, we must gain vision and perspective. We must acquaint ourselves with civil law and ask, “Why?” We must expand our minds and believe as Thomas Jefferson did that “men can be trusted to govern themselves without a master.”

The Beehive: A symbol of productivity, tireless work, discipline, frugality, order, harmony, industry, and the sweet results of turmoil. To some though, the beehive is also an example of why hierarchy is the most productive system of industry. It serves as an example of how mindless beings that submit themselves to order will thrive. In real life, however, bees are anything but mindless drones working under a dictatorial queen bee. The title of “queen” is often confused with what the queen bee actually does. In no way does the queen bee make leadership choices. Instead, she is treated with special care by the other bees so that she might do her job of laying over half-a-million eggs per year.The reality is that bees are an amazing example, in nature, of how consensus, union and intelligent cooperation lead to better decision making.

One of the most important group decisions made by a bee colony is where to locate the nest. This particular type of decision making in bees is well studied. The colony sends out a small number of scouts to survey the environment for good nest locations; typically, scouts comprise about 5 percent of the total group. When the scouts return to the colony with information, those who found a more promising site signal their finding by dancing at a higher intensity and for a longer period of time.As a result of this social signaling, more scouts are recruited to the better sites. After additional scouts explore the better sites and return to signal their findings, the dancing of the scouts skews further in favor of the better sites. Eventually so many scouts are signaling in favor of the best site that a tipping point is reached, and the entire colony picks up and moves. Social signaling, communicated by higher activity, causes the information from individual scouts to be communicated, weighted, and pooled, iteratively recruiting a larger and larger fraction of the colony, until a group consensus is reached.

Alex Pentland. Honest Signals: How They Shape Our World (Kindle Locations 695-702). Kindle Edition.

Studies of bee colonies show that over 99 percent of the time scout bees, through consensus, choose the highest-quality nesting site available.

Similar social signaling is used by worker bees, who make up about 85 percent of the colony, to find the best nectar locations (click here to watch video). The direction of their dance indicates the direction of the nectar source, and the intensity and length of dance persuade other worker bees of the best nectar locations.

Consensus, democracy, division of labor and division of management are the governing philosophies of a bee colony. Furthermore, by dividing decision making and decentralizing it to those groups that are best qualified to make particular decisions, bees are able to fully utilize their collective intelligence.

Further learning: