The following examples of emergent systems illustrate the kinds of feedback between individual elements of natural systems that give rise to surprising ordered behavior. They also illustrate a clear trade-off between the number of elements involved in the emergent system and the complexity of their individual interactions. The more complex the interactions between elements, the fewer elements are needed for a higher-level phenomenon to emerge. Hurricanes and sand dunes form from vast numbers of very simple elements whereas even small groups of birds can exhibit flocking behavior.
What is the lesson for multicellular computing? It is that networks of computers support many sorts of emergent meta-level behavior because computers interact in far more complex ways than air and water molecules or particles of sand. Some of this emergent behavior in computing contexts is desirable and/or intentional, and some (system bugs, malware such as computer viruses, botnets, digital propaganda, and cyber-warfare) are not.
It behooves us to better understand emergence in complex dynamic systems
Hurricanes emerge from mutual positive feedback between wind, humidity, evaporation of warm surface waters and Coriolis effects.
Two phenomena, linked in a feedback loop, provide the sustained energy that allows wind speeds to grow and the hurricane to organize. One is that the rate of surface evaporation depends upon surface wind speed and water temperature. The faster the wind and the warmer the surface water, the more water evaporates and is captured by the wind. The other phenomena is that as humid air rises it cools, causing water vapor to condense, which releases the heat required to evaporate the water in the first place. This heat warms the surrounding air which causes it to rise faster. This updraft draws more humid air from below that further fuels the positive feedback cycle.
By themselves, the evaporation/condensation processes do not necessarily cause hurricanes. The same processes at smaller scale fuel thunderstorms. To become a hurricane, the wind pattern has to organize into the familiar spiral which requires very large areas of warm water which occur only over oceans. The spiral winds allow the process to concentrate the energy into a central region of maximal updraft where maximal winds are generated. The spiral organization is caused by Coriolis effects which, in the northern hemisphere, cause rising air (gradually) to generate a counterclockwise spiral. The Coriolis effect is very weak. It takes days of favorable conditions and thousands of square miles of ocean for a well organized spiral to emerge from what otherwise would simply be a bunch of tropical thunderstorms.
The lesson is that you can't have a tempest (hurricane) in a teacup. Scale matters!
The Web originated in 1989. For the first few years
people found desirable websites by following links to them from
sites they had already discovered or sites recommended by
friends. The Yahoo search index was invented in 1994 to
make this task a bit easier by hiring human "link librarians" to
curate a topic index of the web . Indices from Excite,
Lycos, AltaVista, AskJeeves, and MSN were done similarly.
Five years later, in 1989, Larry Page and Sergey Brin realized
that the topology of the network of links in the web, in
particular the hubs pointed to by many sites, could be found by
"crawling" or "spidering" the whole web automatically to find
and catalog all the links along with nearby
words and phrases. As the web grew rapidly, the
spidering took more and more compute and Internet bandwidth
resources, but Google received entrepreneurial support to gain
those resources. Now it spiders much of the Web daily and
virtually all of it weekly.
The rest is history. Dozens, perhaps hundreds, of other
sites spider the web for various purposes and a whole industry
of Search Engine Optimizers (SEOs) has emerged to reverse
engineer the algorithms used by Google and its copycats to
determine the ordering of recommended links.
Sand dunes result from feedback between prevailing winds that blow grains of sand along the surface and the effect that a ripple in the sand surface has on the flow of the wind over it. When the sand surface is flat, grains of sand blown by the wind land in no particular pattern. But any obstruction -- a rock, a fence post, even an ant hill -- that disrupts the smooth flow of the wind causes sand to land preferentially in the "wind shadow" behind the obstruction. This new sand adds to the disruption of the wind flow which, in turn, causes even more sand to collect on the downwind side. High winds on a beach create small ripples in sand. Large dunes require miles of sand, e.g., in Death Valley, California the Huacachina dunes on the coast of Peru, or in the "Empty Quarter" in Saudi Arabia.
The shapes of dunes are dictated primarily by prevailing wind direction. When prevailing winds are nearly always from the same direction, dunes tend to form in rows. When the winds are more erratic, you may see much more complexly shaped dunes as in the above photo of what are called "star" dunes.
Sand dunes emerge at a scale intermediate between hurricanes
and flocks of birds. Interactions between grains of sand and
surface winds are much more complex than interactions between
the far more numerous air and water molecules in hurricanes, yet
grains of sand interact far more simply than birds in a flock.
In the last few years large clusters of zombie bots have
emerged in the Internet. These zombies have been infected
with malware that puts them under the control of bot "herders"--
hackers or criminals who direct certain activities of the bots
that generate income. Example zombie behavior includes
click-fraud and spam e-mail schemes or Distributed Denial of
Service attacks for ransom. Susceptibility to capture by a
bot herder Bot herders create a core of bots by various
schemes or perhaps simple brute-force attacks that try common
passwords. They then set the bots the task of trying brute
force attacks on a list of possibly vulnerable sites.
e.g., by compromising large numbers of sites and adding their
malware to the sites, and then direct the bots as desired.
One common example is WordPress botnets. The bot herders direct brute force attacks on the login page of sites built using WordPress in hopes that they have been poorly built and or have weak admin passwords. Successfully compromised sites then join in the brute force attacks on other sites to enlarge the net. Targeted sites may receive hundreds of unique login attempts per day, each from a different attacking IP address that has been previously compromised and directed to seek out others.
The rapidly growing Internet of Things (IoT) is supercharging the problem of botnets. An IoT botnet is a collection of compromised IoT devices, such as cameras, routers, DVRs, and other embedded technologies infected with malware. Because these devices are sold with default passwords and the buyers often don't know or care about replacing the defaults with secret and secure passwords, IoT bot herders have an easy time compromising them and adding them to their botnets. Traditional botnets may consist of thousands or tens of thousands of devices, IoT botnets may be comprised of hundreds of thousands of compromised devices.
In a flock of starlings, the behavior of the flock emerges from the desire of the individual birds to avoid collisions while staying close to neighbors. Positive feedback occurs because the behavior of each bird affects its neighbors and vice versa. Craig Reynolds' boids simulations show similar behavior. Two researchers have proposed possible relationships between boid-like swarming behavior and aspects of multicellular organisms. See Boids Model Applied to Cell Segregation and J. Rothermich's masters thesis
Flocks of starlings involve hundreds, perhaps thousands, of
birds. But you can observe clear flocking behavior from a couple
of dozen starlings or four or five pelicans. Even the largest
flocks of starlings -- reportedly 5000 birds or so -- are many
orders of magnitude less numerous than the elements involved in
hurricanes or sand dunes. There are billions of sand grains in
just the top millimeter of a large sand dune and orders of
magnitude more air and water molecules in a hurricane than
grains of sand in the Sahara. Yet the interactions between just
two birds is incomparably more complex than interactions between
two sand grains or air molecules.
There are at least 60
popular social networks. Facebook is the largest
with about 1.6 billion members. WhatsApp is second with
about 1 billion. Twitter has about 320 million, and so
forth. New behavior emerges in most of them at a rate that
makes it difficult to even catalog social network behavior let
alone understand how it evolves or what it implies for society,
In 2015 social media became a cyber warfare medium where it
became the chief medium for propaganda that affected the
American Presidential election. The British firm,
Cambridge Analytica, owned by one of the wealthiest Americans,
directed Russian efforts to elect Donald Trump by injecting
"fake news" into the system, attempting to suppress voting in
Democratic precincts and enhance turnout in Republican
precincts, and control the topics of news networks. This
emergence of social networks as political weapons has yet to be
well understood, let alone countered.
Termite mounds appear to be constructed by "intelligent" cooperation. The sometimes elaborate galleries and chimneys control air flow to manage temperature and humidity inside the nest. But individual termites have no more notion of how to build a nest than a starling does of how to lead a flock. Individual termites cannot even perceive the overall shape of a nest (the workers are blind) let alone direct its "design."
Instead, termites respond to very local chemical cues left behind by other termites and to temperature/humidity and airflow cues that are affected by the shape of the nest, wind currents, the amount of heat generated within the nest and other local phenomena. The termite's behavior affects the shape of the nest and the shape of the nest affects the termite's behavior. In that sense, the nest is a bit like a flock of starlings in very slow motion.
Cities emerge from societies and geography much the way termite
mounds emerge in the context of temperature and humidity.
Very rare exceptions aside, they are not the result of any sort
of design nor do they reflect the knowledge or desire of any
person. Even their location is largely accidental.
Jerusalem (photo left) is one of the oldest cities in the
world. During its long
history, Jerusalem has been attacked 52 times, captured and
recaptured 44 times, besieged 23 times, and destroyed twice.
The oldest part of the city was settled in the 4th millennium
BCE. Its culture is very specific to its location but
the degree to which its growth emerged rather than was planned
is little different from Manhattan, Rome, Los Angeles, Mexico
City, Nairobi or Hong Kong.