Swarm Intelligence - Ant Colonies And American Capitalism

Swarm intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence.

SI systems consist typically of a population of simple agents…interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples in natural systems of SI include ant colonies, bird flocking, animal herding, bacterial growth, fish schooling and microbial intelligence (Wikipedia)

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In other words ants build colonies, forage, fight, defend, and survive with no organizational leadership. Each ant has limited intelligence, only enough to perform the tasks that evolution has assigned to it.  Ants are differentiated by task – workers, soldiers, drones, princesses, and queens. The workers carry out the basic tasks of foraging, and colony construction and repair. The soldiers defend the colony and fight invaders. The princesses are ‘virgin queens’ to be inseminated by male drones.  There are no alpha males which fight for dominance and then rule until deposed; and no rule of tooth-and-claw to determine social order.  No one in the colony is in charge.  No one gives orders through pheromone exchange. Ants have evolved to collectively perform complex activities in a coordinated way and have – without instruction, discipline, or commands from above – created efficiently functioning colonies of over half-a-million individuals.


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The ants, however, do not simply perform routine activities.  They do not always forage in the same place, construct identical colonies; but respond to the complex, changing environment around them.

In the Arizona desert where Deborah Gordon studies red harvester ants(Pogonomyrmex barbatus), a colony calculates each morning how many workers to send out foraging for food. The number can change, depending on conditions. Have foragers recently discovered a bonanza of tasty seeds? More ants may be needed to haul the bounty home. Was the nest damaged by a storm last night? Additional maintenance workers may be held back to make repairs. An ant might be a nest worker one day, a trash collector the next (Peter Miller, National Geographic)

How is this sophisticated job allocation accomplished without the oversight of a general manager?

Ants communicate by touch and smell. When one ant bumps into another, it sniffs with its antennae to find out if the other belongs to the same nest and where it has been working. (Ants that work outside the nest smell different from those that stay inside.) Before they leave the nest each day, foragers normally wait for early morning patrollers to return. As patrollers enter the nest, they touch antennae briefly with foragers.
"When a forager has contact with a patroller, it's a stimulus for the forager to go out," Gordon says. "But the forager needs several contacts no more than ten seconds apart before it will go out." (Peter Miller)

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Scientists have used swarm theory to improve the operation of complex systems. Southwest Airlines, for example, improved efficiency by simulating ant behavior.

So [Southwest] created a computer model of the airport, giving each aircraft the ability to remember how long it took to get into and away from each gate. Then he set the model in motion to simulate a day's activity.
"The planes are like ants searching for the best gate," Southwest’s Don Lawson says. But rather than leaving virtual pheromones along the way, each aircraft remembers the faster gates and forgets the slower ones. After many simulations, using real data to vary arrival and departure times, each plane learned how to avoid an intolerable wait on the tarmac (Miller)

Crowdsourcing is another application of swarm theory.  In a now famous experiment, random subjects estimating the number of gum balls in a jar were always more accurate than mathematicians who applied sophisticated analytical principles.  Not only were they accurate, but were always one or two gum balls off the actual number. Bettors have always shown to be better odds makers than experts.



One of the most recent applications of swarm theory is robotics.

By demonstrating the power of self-organizing models to mimic swarm behavior, Reynolds was also blazing the trail for robotics engineers. A team of robots that could coordinate its actions like a flock of birds could offer significant advantages over a solitary robot. Spread out over a large area, a group could function as a powerful mobile sensor net, gathering information about what's out there.

If the group encountered something unexpected, it could adjust and respond quickly, even if the robots in the group weren't very sophisticated, just as ants are able to come up with various options by trial and error. If one member of the group were to break down, others could take its place. And, most important, control of the group could be decentralized, not dependent on a leader (Miller)

On a recent walk through my neighborhood I noticed all the construction activity going on. Residents were adding new rooms, building decks, landscaping, and doing extensive replacements and repair.  Each homeowner decided that market value, income levels, and increasing family demands for space all made the decision to expand, remodel, or reconstruct made sense.  The work done on the houses was done by a variety of different contractors – roofers, landscapers, carpenters, electricians, and builders – all of whom worked in small enterprises that had been started by individuals who had worked on their own, organized, financed, and created a legal entity; and expanded within the DC metropolitan area.

Each of the services that enabled these entrepreneurs to get started followed the same trajectory.  The banks who approved the small-business loan, the lawyers who drew up the legal documents, and the accountants who set up the books all started small and grew according to ability and demand.

A few years ago I traveled in what is known in American history as the Old Southwest, a region including Western Florida.  The story of the region’s growth is interesting. Early American investors saw that the burgeoning economic activity north of the Gulf coast offered unlimited potential; and if they could build the infrastructure to expedite the movement of natural resources and finished products to market, they would become wealthy.  The pooled their resources, bought land, and built ports, improved lower waterways, constructed roads, and made a fortune along with the businessmen who supplied the products to be sold in New Orleans, the Caribbean, the North, or even Europe.


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There was no local, state, or local government at the time; no overarching administration to plan and manage these complex operations. Private interests coincided, supply and demand operated, and only after business was well underway did business people see the need for public oversight – a judicial system to adjudicate disputes, to keep law and order, and to maintain public services.

All of America in fact was built this way.  No one told private entrepreneurs what to do or when to do it.  They operated on their own guided only by basic economic principles and an understanding of the nature of supply and demand.  Government did not precede private enterprise. Just the opposite.

In other words, a snapshot of 19th Century America would be little different from that of an ant colony. No one told ranchers, farmers, industrialists, shopkeepers, homemakers, preachers, and laborers what to do or how and when to do it. They were, like ants, guided by evolutionary givens – human nature is self-interested, territorial, expansionist, and self-protective; and performed tasks that best corresponded to given levels of intelligence, strength, savvy, creativity, and ambition.

With or without intellectual endowments, both ants and human beings act individually to produce collective outcomes. No investor in Apalachicola in 1830 envisaged the growth of the city, the region, or the trade. The earliest settlers in New York never imagined what the city would become. Cities grew not because anyone planned them, but because individuals, cooperating in an open environment, added to their incremental growth.


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Forced collectivization – the imposition of a priori communal ideas, programs, and concepts on individual enterprise – has never worked and never will.  As even the most committed ‘progressive’ will eventually realize, individualism is the engine of productive, private cooperation.  There is no need to enforce ideas of collaboration, teamwork, or communalism. Human beings have worked together because of mutual self-interest for millennia and will continue to do so.

Big data, crowdsourcing, and the emerging recognition of the collectivity of random individuals will eventually marginalize outdated interventionism.

The human ant colony works just fine.