From the archive, originally posted by: [ spectre ]

http://www7.nationalgeographic.com/ngm/0707/feature5/gallery1.html

“During my visit with biologists Thomas Seeley and Kirk Visscher on
Appledore Island off the coast of New Hampshire, I got to watch
honeybees perform waggle dances. It was the first time I had seen this
amazing behavior, and it reminded me a little of break dancing, with
individual bees doing their moves in front of a crowd. The crowd, in
this case, was a swarm of some 4,000 bees in need of a new home, and
the dancers were scouts that had found potential nest sites. Each
dancer was telling the other bees how to find the site they had
visited through a kind of code. And they were very determined. In
fact, they were insistent, as if trying to persuade the crowd. At
times, I saw several bees dancing, competing for attention. The swarm
was so preoccupied with its own situation that I was able to get very
close-less than a foot (0.3 meter) away-to observe the bees without
being noticed.” —Peter Miller

http://www.springboardenterprises.org/press/default.asp?pid=633
http://www.foresight.org/Nanomedicine/Swarm.html
http://www.icosystem.com/releases/DARPA_210403.htm

http://mae.pennnet.com/articles/article_display.cfm?Section=ARCHI&C=Feat&ARTICLE_ID=258264&KEYWORDS=UGV&p=32
http://www.dodsbir.net/selections/abs052/darpaabs052.htm
http://www.aiaa.org/content.cfm?pageid=406&gTable=Paper&gID=21416
http://www.elsevier.com/wps/find/products_in_subject_and_group.cws_home/677922

“Air traffic, on the other hand, is already highly regulated by a top-
down system of airport controllers. But researchers are running tests
of agent-based systems to see if planes could self-organize safely and
efficiently. With the proliferation of flights these days, especially
at the busiest hub airports where planes take off or land minutes
apart, it might be safer if-instead of each plane being monitored on
an individual basis from a central control point-airplanes could flock
like birds (though farther apart). They would adjust their own
movements to stay in proximity to other airplanes, allowing for
quicker landings and departures. At the same time, ant-routing
algorithms could enable planes to find quick, safe routes around bad
weather or into crowded airports.”  -Elizabeth Snodgrass

SWARM BEHAVIOR
http://www7.nationalgeographic.com/ngm/0707/feature5/index.html

By Peter Miller

A single ant or bee isn’t smart, but their colonies are. The study of
swarm intelligence is providing insights that can help humans manage
complex systems, from truck routing to military robots.

I used to think ants knew what they were doing. The ones marching
across my kitchen counter looked so confident, I just figured they had
a plan, knew where they were going and what needed to be done. How
else could ants organize highways, build elaborate nests, stage epic
raids, and do all the other things ants do?

Turns out I was wrong. Ants aren’t clever little engineers,
architects, or warriors after all-at least not as individuals. When it
comes to deciding what to do next, most ants don’t have a clue. “If
you watch an ant try to accomplish something, you’ll be impressed by
how inept it is,” says Deborah M. Gordon, a biologist at Stanford
University.

How do we explain, then, the success of Earth’s 12,000 or so known ant
species? They must have learned something in 140 million years.

“Ants aren’t smart,” Gordon says. “Ant colonies are.” A colony can
solve problems unthinkable for individual ants, such as finding the
shortest path to the best food source, allocating workers to different
tasks, or defending a territory from neighbors. As individuals, ants
might be tiny dummies, but as colonies they respond quickly and
effectively to their environment. They do it with something called
swarm intelligence.

Where this intelligence comes from raises a fundamental question in
nature: How do the simple actions of individuals add up to the complex
behavior of a group? How do hundreds of honeybees make a critical
decision about their hive if many of them disagree? What enables a
school of herring to coordinate its movements so precisely it can
change direction in a flash, like a single, silvery organism? The
collective abilities of such animals-none of which grasps the big
picture, but each of which contributes to the group’s success-seem
miraculous even to the biologists who know them best. Yet during the
past few decades, researchers have come up with intriguing insights.

One key to an ant colony, for example, is that no one’s in charge. No
generals command ant warriors. No managers boss ant workers. The queen
plays no role except to lay eggs. Even with half a million ants, a
colony functions just fine with no management at all-at least none
that we would recognize. It relies instead upon countless interactions
between individual ants, each of which is following simple rules of
thumb. Scientists describe such a system as self-organizing.

Consider the problem of job allocation. 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. But how does a colony make such adjustments if no one’s in
charge? Gordon has a theory.

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.”

To see how this works, Gordon and her collaborator Michael Greene of
the University of Colorado at Denver captured patroller ants as they
left a nest one morning. After waiting half an hour, they simulated
the ants’ return by dropping glass beads into the nest entrance at
regular intervals-some coated with patroller scent, some with
maintenance worker scent, some with no scent. Only the beads coated
with patroller scent stimulated foragers to leave the nest. Their
conclusion: Foragers use the rate of their encounters with patrollers
to tell if it’s safe to go out. (If you bump into patrollers at the
right rate, it’s time to go foraging. If not, better wait. It might be
too windy, or there might be a hungry lizard waiting out there.) Once
the ants start foraging and bringing back food, other ants join the
effort, depending on the rate at which they encounter returning
foragers.

“A forager won’t come back until it finds something,” Gordon says.
“The less food there is, the longer it takes the forager to find it
and get back. The more food there is, the faster it comes back. So
nobody’s deciding whether it’s a good day to forage. The collective
is, but no particular ant is.”

That’s how swarm intelligence works: simple creatures following simple
rules, each one acting on local information. No ant sees the big
picture. No ant tells any other ant what to do. Some ant species may
go about this with more sophistication than others. (Temnothorax
albipennis, for example, can rate the quality of a potential nest site
using multiple criteria.) But the bottom line, says Iain Couzin, a
biologist at Oxford and Princeton Universities, is that no leadership
is required. “Even complex behavior may be coordinated by relatively
simple interactions,” he says.

Inspired by the elegance of this idea, Marco Dorigo, a computer
scientist at the Université Libre in Brussels, used his knowledge of
ant behavior in 1991 to create mathematical procedures for solving
particularly complex human problems, such as routing trucks,
scheduling airlines, or guiding military robots.

In Houston, for example, a company named American Air Liquide has been
using an ant-based strategy to manage a complex business problem. The
company produces industrial and medical gases, mostly nitrogen,
oxygen, and hydrogen, at about a hundred locations in the United
States and delivers them to 6,000 sites, using pipelines, railcars,
and 400 trucks. Deregulated power markets in some regions (the price
of electricity changes every 15 minutes in parts of Texas) add yet
another layer of complexity.

“Right now in Houston, the price is $44 a megawatt for an industrial
customer,” says Charles N. Harper, who oversees the supply system at
Air Liquide. “Last night the price went up to $64, and Monday when the
cold front came through, it went up to $210.” The company needed a way
to pull it all together.

Working with the Bios Group (now NuTech Solutions), a firm that
specialized in artificial intelligence, Air Liquide developed a
computer model based on algorithms inspired by the foraging behavior
of Argentine ants (Linepithema humile), a species that deposits
chemical substances called pheromones.

“When these ants bring food back to the nest, they lay a pheromone
trail that tells other ants to go get more food,” Harper explains.
“The pheromone trail gets reinforced every time an ant goes out and
comes back, kind of like when you wear a trail in the forest to
collect wood. So we developed a program that sends out billions of
software ants to find out where the pheromone trails are strongest for
our truck routes.”

Ants had evolved an efficient method to find the best routes in their
neighborhoods. Why not follow their example? So Air Liquide combined
the ant approach with other artificial intelligence techniques to
consider every permutation of plant scheduling, weather, and truck
routing-millions of possible decisions and outcomes a day. Every
night, forecasts of customer demand and manufacturing costs are fed
into the model.

“It takes four hours to run, even with the biggest computers we have,”
Harper says. “But at six o’clock every morning we get a solution that
says how we’re going to manage our day.”

For truck drivers, the new system took some getting used to. Instead
of delivering gas from the plant closest to a customer, as they used
to do, drivers were now asked to pick up shipments from whichever
plant was making gas at the lowest delivered price, even if it was
farther away.

“You want me to drive a hundred miles? To the drivers, it wasn’t
intuitive,” Harper says. But for the company, the savings have been
impressive. “It’s huge. It’s actually huge.”

Other companies also have profited by imitating ants. In Italy and
Switzerland, fleets of trucks carrying milk and dairy products,
heating oil, and groceries all use ant-foraging rules to find the best
routes for deliveries. In England and France, telephone companies have
made calls go through faster on their networks by programming messages
to deposit virtual pheromones at switching stations, just as ants
leave signals for other ants to show them the best trails.

In the U.S., Southwest Airlines has tested an ant-based model to
improve service at Sky Harbor International Airport in Phoenix. With
about 200 aircraft a day taking off and landing on two runways and
using gates at three concourses, the company wanted to make sure that
each plane got in and out as quickly as possible, even if it arrived
early or late.

“People don’t like being only 500 yards away from a gate and having to
sit out there until another aircraft leaves,” says Doug Lawson of
Southwest. So Lawson 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,” he 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.
Southwest was so pleased with the outcome, it may use a similar model
to study the ticket counter area.

WHEN IT COMES TO SWARM intelligence, ants aren’t the only insects with
something useful to teach us. On a small, breezy island off the
southern coast of Maine, Thomas Seeley, a biologist at Cornell
University, has been looking into the uncanny ability of honeybees to
make good decisions. With as many as 50,000 workers in a single hive,
honeybees have evolved ways to work through individual differences of
opinion to do what’s best for the colony. If only people could be as
effective in boardrooms, church committees, and town meetings, Seeley
says, we could avoid problems making decisions in our own lives.

During the past decade, Seeley, Kirk Visscher of the University of
California, Riverside, and others have been studying colonies of
honeybees (Apis mellifera) to see how they choose a new home. In late
spring, when a hive gets too crowded, a colony normally splits, and
the queen, some drones, and about half the workers fly a short
distance to cluster on a tree branch. There the bees bivouac while a
small percentage of them go searching for new real estate. Ideally,
the site will be a cavity in a tree, well off the ground, with a small
entrance hole facing south, and lots of room inside for brood and
honey. Once a colony selects a site, it usually won’t move again, so
it has to make the right choice.

To find out how, Seeley’s team applied paint dots and tiny plastic
tags to identify all 4,000 bees in each of several small swarms that
they ferried to Appledore Island, home of the Shoals Marine
Laboratory. There, in a series of experiments, they released each
swarm to locate nest boxes they’d placed on one side of the half-mile-
long (one kilometer) island, which has plenty of shrubs but almost no
trees or other places for nests.

In one test they put out five nest boxes, four that weren’t quite big
enough and one that was just about perfect. Scout bees soon appeared
at all five. When they returned to the swarm, each performed a waggle
dance urging other scouts to go have a look. (These dances include a
code giving directions to a box’s location.) The strength of each
dance reflected the scout’s enthusiasm for the site. After a while,
dozens of scouts were dancing their little feet off, some for one
site, some for another, and a small cloud of bees was buzzing around
each box.

The decisive moment didn’t take place in the main cluster of bees, but
out at the boxes, where scouts were building up. As soon as the number
of scouts visible near the entrance to a box reached about 15-a
threshold confirmed by other experiments-the bees at that box sensed
that a quorum had been reached, and they returned to the swarm with
the news.

“It was a race,” Seeley says. “Which site was going to build up 15
bees first?”

Scouts from the chosen box then spread through the swarm, signaling
that it was time to move. Once all the bees had warmed up, they lifted
off for their new home, which, to no one’s surprise, turned out to be
the best of the five boxes.

The bees’ rules for decision-making-seek a diversity of options,
encourage a free competition among ideas, and use an effective
mechanism to narrow choices-so impressed Seeley that he now uses them
at Cornell as chairman of his department.

“I’ve applied what I’ve learned from the bees to run faculty
meetings,” he says. To avoid going into a meeting with his mind made
up, hearing only what he wants to hear, and pressuring people to
conform, Seeley asks his group to identify all the possibilities, kick
their ideas around for a while, then vote by secret ballot. “It’s
exactly what the swarm bees do, which gives a group time to let the
best ideas emerge and win. People are usually quite amenable to that.”

In fact, almost any group that follows the bees’ rules will make
itself smarter, says James Surowiecki, author of The Wisdom of Crowds.
“The analogy is really quite powerful. The bees are predicting which
nest site will be best, and humans can do the same thing, even in the
face of exceptionally complex decisions.” Investors in the stock
market, scientists on a research project, even kids at a county fair
guessing the number of beans in a jar can be smart groups, he says, if
their members are diverse, independent minded, and use a mechanism
such as voting, auctioning, or averaging to reach a collective
decision.

Take bettors at a horse race. Why are they so accurate at predicting
the outcome of a race? At the moment the horses leave the starting
gate, the odds posted on the pari-mutuel board, which are calculated
from all bets put down, almost always predict the race’s outcome:
Horses with the lowest odds normally finish first, those with second
lowest odds finish second, and so on. The reason, Surowiecki says, is
that pari-mutuel betting is a nearly perfect machine for tapping into
the wisdom of the crowd.

“If you ever go to the track, you find a really diverse group, experts
who spend all day perusing daily race forms, people who know something
about some kinds of horses, and others who are betting at random, like
the woman who only likes black horses,” he says. Like bees trying to
make a decision, bettors gather all kinds of information, disagree
with one another, and distill their collective judgment when they
place their bets.

That’s why it’s so rare to win on a long shot.

THERE’S A SMALL PARK near the White House in Washington, D.C., where I
like to watch flocks of pigeons swirl over the traffic and trees.
Sooner or later, the birds come to rest on ledges of buildings
surrounding the park. Then something disrupts them, and they’re off
again in synchronized flight.

The birds don’t have a leader. No pigeon is telling the others what to
do. Instead, they’re each paying close attention to the pigeons next
to them, each bird following simple rules as they wheel across the
sky. These rules add up to another kind of swarm intelligence-one that
has less to do with making decisions than with precisely coordinating
movement.

Craig Reynolds, a computer graphics researcher, was curious about what
these rules might be. So in 1986 he created a deceptively simple
steering program called boids. In this simulation, generic birdlike
objects, or boids, were each given three instructions: 1) avoid
crowding nearby boids, 2) fly in the average direction of nearby
boids, and 3) stay close to nearby boids. The result, when set in
motion on a computer screen, was a convincing simulation of flocking,
including lifelike and unpredictable movements.

At the time, Reynolds was looking for ways to depict animals
realistically in TV shows and films. (Batman Returns in 1992 was the
first movie to use his approach, portraying a swarm of bats and an
army of penguins.) Today he works at Sony doing research for games,
such as an algorithm that simulates in real time as many as 15,000
interacting birds, fish, or people.

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.

“In biology, if you look at groups with large numbers, there are very
few examples where you have a central agent,” says Vijay Kumar, a
professor of mechanical engineering at the University of Pennsylvania.
“Everything is very distributed: They don’t all talk to each other.
They act on local information. And they’re all anonymous. I don’t care
who moves the chair, as long as somebody moves the chair. To go from
one robot to multiple robots, you need all three of those ideas.”

Within five years Kumar hopes to put a networked team of robotic
vehicles in the field. One purpose might be as first responders.
“Let’s say there’s a 911 call,” he says. “The fire alarm goes off. You
don’t want humans to respond. You want machines to respond, to tell
you what’s happening. Before you send firemen into a burning building,
why not send in a group of robots?”

Taking this idea one step further, Marco Dorigo’s group in Brussels is
leading a European effort to create a “swarmanoid,” a group of
cooperating robots with complementary abilities: “foot-bots” to
transport things on the ground, “hand-bots” to climb walls and
manipulate objects, and “eye-bots” to fly around, providing
information to the other units.

The military is eager to acquire similar capabilities. On January 20,
2004, researchers released a swarm of 66 pint-size robots into an
empty office building at Fort A. P. Hill, a training center near
Fredericksburg, Virginia. The mission: Find targets hidden in the
building.

Zipping down the main hallway, the foot-long (0.3 meter) red robots
pivoted this way and that on their three wheels, resembling nothing so
much as large insects. Eight sonars on each unit helped them avoid
collisions with walls and other robots. As they spread out, entering
one room after another, each robot searched for objects of interest
with a small, Web-style camera. When one robot encountered another, it
used wireless network gear to exchange information. (“Hey, I’ve
already explored that part of the building. Look somewhere else.”)

In the back of one room, a robot spotted something suspicious: a pink
ball in an open closet (the swarm had been trained to look for
anything pink). The robot froze, sending an image to its human
supervisor. Soon several more robots arrived to form a perimeter
around the pink intruder. Within half an hour, all six of the hidden
objects had been found. The research team conducting the experiment
declared the run a success. Then they started a new test.

The demonstration was part of the Centibots project, an investigation
to see if as many as a hundred robots could collaborate on a mission.
If they could, teams of robots might someday be sent into a hostile
village to flush out terrorists or locate prisoners; into an
earthquake-damaged building to find victims; onto chemical-spill sites
to examine hazardous waste; or along borders to watch for intruders.
Military agencies such as DARPA (Defense Advanced Research Projects
Agency) have funded a number of robotics programs using collaborative
flocks of helicopters and fixed-wing aircraft, schools of torpedo-
shaped underwater gliders, and herds of unmanned ground vehicles. But
at the time, this was the largest swarm of robots ever tested.

“When we started Centibots, we were all thinking, this is a crazy
idea, it’s impossible to do,” says Régis Vincent, a researcher at SRI
International in Menlo Park, California. “Now we’re looking to see if
we can do it with a thousand robots.”

IN NATURE, OF COURSE, animals travel in even larger numbers. That’s
because, as members of a big group, whether it’s a flock, school, or
herd, individuals increase their chances of detecting predators,
finding food, locating a mate, or following a migration route. For
these animals, coordinating their movements with one another can be a
matter of life or death.

“It’s much harder for a predator to avoid being spotted by a thousand
fish than it is to avoid being spotted by one,” says Daniel Grünbaum,
a biologist at the University of Washington. “News that a predator is
approaching spreads quickly through a school because fish sense from
their neighbors that something’s going on.”

When a predator strikes a school of fish, the group is capable of
scattering in patterns that make it almost impossible to track any
individual. It might explode in a flash, create a kind of moving
bubble around the predator, or fracture into multiple blobs, before
coming back together and swimming away.

Animals on land do much the same, as Karsten Heuer, a wildlife
biologist, observed in 2003, when he and his wife, Leanne Allison,
followed the vast Porcupine caribou herd (Rangifer tarandus granti)
for five months. Traveling more than a thousand miles (1,600
kilometers) with the animals, they documented the migration from
winter range in Canada’s northern Yukon Territory to calving grounds
in Alaska’s Arctic National Wildlife Refuge.

“It’s difficult to describe in words, but when the herd was on the
move it looked very much like a cloud shadow passing over the
landscape, or a mass of dominoes toppling over at the same time and
changing direction,” Karsten says. “It was as though every animal knew
what its neighbor was going to do, and the neighbor beside that and
beside that. There was no anticipation or reaction. No cause and
effect. It just was.”

One day, as the herd funneled through a gully at the tree line,
Karsten and Leanne spotted a wolf creeping up. The herd responded with
a classic swarm defense.

“As soon as the wolf got within a certain distance of the caribou, the
herd’s alertness just skyrocketed,” Karsten says. “Now there was no
movement. Every animal just stopped, completely vigilant and
watching.” A hundred yards (90 meters) closer, and the wolf crossed
another threshold. “The nearest caribou turned and ran, and that
response moved like a wave through the entire herd until they were all
running. Reaction times shifted into another realm. Animals closest to
the wolf at the back end of the herd looked like a blanket unraveling
and tattering, which, from the wolf’s perspective, must have been
extremely confusing.” The wolf chased one caribou after another,
losing ground with each change of target. In the end, the herd escaped
over the ridge, and the wolf was left panting and gulping snow.

For each caribou, the stakes couldn’t have been higher, yet the herd’s
evasive maneuvers displayed not panic but precision. (Imagine the
chaos if a hungry wolf were released into a crowd of people.) Every
caribou knew when it was time to run and in which direction to go,
even if it didn’t know exactly why. No leader was responsible for
coordinating the rest of the herd. Instead each animal was following
simple rules evolved over thousands of years of wolf attacks.

That’s the wonderful appeal of swarm intelligence. Whether we’re
talking about ants, bees, pigeons, or caribou, the ingredients of
smart group behavior-decentralized control, response to local cues,
simple rules of thumb-add up to a shrewd strategy to cope with
complexity.

“We don’t even know yet what else we can do with this,” says Eric
Bonabeau, a complexity theorist and the chief scientist at Icosystem
Corporation in Cambridge, Massachusetts. “We’re not used to solving
decentralized problems in a decentralized way. We can’t control an
emergent phenomenon like traffic by putting stop signs and lights
everywhere. But the idea of shaping traffic as a self-organizing
system, that’s very exciting.”

Social and political groups have already adopted crude swarm tactics.
During mass protests eight years ago in Seattle, anti-globalization
activists used mobile communications devices to spread news quickly
about police movements, turning an otherwise unruly crowd into a
“smart mob” that was able to disperse and re-form like a school of
fish.

The biggest changes may be on the Internet. Consider the way Google
uses group smarts to find what you’re looking for. When you type in a
search query, Google surveys billions of Web pages on its index
servers to identify the most relevant ones. It then ranks them by the
number of pages that link to them, counting links as votes (the most
popular sites get weighted votes, since they’re more likely to be
reliable). The pages that receive the most votes are listed first in
the search results. In this way, Google says, it “uses the collective
intelligence of the Web to determine a page’s importance.”

Wikipedia, a free collaborative encyclopedia, has also proved to be a
big success, with millions of articles in more than 200 languages
about everything under the sun, each of which can be contributed by
anyone or edited by anyone. “It’s now possible for huge numbers of
people to think together in ways we never imagined a few decades ago,”
says Thomas Malone of MIT’s new Center for Collective Intelligence.
“No single person knows everything that’s needed to deal with problems
we face as a society, such as health care or climate change, but
collectively we know far more than we’ve been able to tap so far.”

Such thoughts underline an important truth about collective
intelligence: Crowds tend to be wise only if individual members act
responsibly and make their own decisions. A group won’t be smart if
its members imitate one another, slavishly follow fads, or wait for
someone to tell them what to do. When a group is being intelligent,
whether it’s made up of ants or attorneys, it relies on its members to
do their own part. For those of us who sometimes wonder if it’s really
worth recycling that extra bottle to lighten our impact on the planet,
the bottom line is that our actions matter, even if we don’t see how.

Think about a honeybee as she walks around inside the hive. If a cold
wind hits the hive, she’ll shiver to generate heat and, in the
process, help to warm the nearby brood. She has no idea that hundreds
of workers in other parts of the hive are doing the same thing at the
same time to the benefit of the next generation.

“A honeybee never sees the big picture any more than you or I do,”
says Thomas Seeley, the bee expert. “None of us knows what society as
a whole needs, but we look around and say, oh, they need someone to
volunteer at school, or mow the church lawn, or help in a political
campaign.”

If you’re looking for a role model in a world of complexity, you could
do worse than to imitate a bee.

Related Links

Boids
http://www.red3d.com/cwr/boids
Craig Reynolds’s 1986 agent-based model-one that shows autonomous
units, in this case “boids,” interacting as one large, self-organized
group-is a classic example of emergent behavior, where complex group
actions arise from simple local rules. Packed with demonstration clips
and many Web links, this site is a must for anyone interested in swarm
theory.

Centibots: The 100 Robots Project
htp://www.ai.sri.com/centibots
Learn more about the Centibots project from the SRI International
website, which includes sections on the robots’ technical design and
experiments. See the robots at work and get more information about the
trial runs in video clips.

MIT Center for Collective Intelligence
http://cci.mit.edu
In the fall of 2006 MIT opened its Center for Collective Intelligence,
with the goal of answering the question, How can people and computers
be connected so that-collectively-they act more intelligently than any
individuals, groups, or computers have ever done before?

Crowd Dynamics
http://www.crowddynamics.com
Keith Still studies crowd dynamics in order to learn how large groups
of people move together and how to redesign public spaces in order to
avoid or at least minimize crowd disasters such as crushing and
trampling. In his research, he has found fascinating geometric
patterns in crowds-images are included on his site.

Stephen Strogatz: Who Cares About Fireflies?
http://www.edge.org/3rd_culture/strogatz03/strogatz_index.html
This meaty interview with Stephen Strogatz, one of the top experts in
chaos theory and natural synchronization, introduces one man’s
fascination with nature’s cycles and how that led him to his career in
research.

Out of Control: The New Biology of Machines
http://www.kk.org/outofcontrol/contents.php
A thought-provoking book that you can read online by chapter, Out of
Control goes beyond swarm theory to discuss other types of biological
systems being harnessed by humankind.

.

http://www.sff.net/people/moriarty/complexity.html

COMPLEXITY
http://www.santafe.edu/
http://www.casresearch.com/
http://www.chaos.cornell.edu/
http://www.cscs.umich.edu/
http://www.haskins.yale.edu/haskins/MISC/DEST/complexity.html

CHAOS IMAGERY
http://chaos.aip.org/
http://hypertextbook.com/chaos/
http://www.mathjmendl.org/chaos/
http://ccrma-www.stanford.edu/~stilti/images/chaotic_attractors/nav.html
http://mathworld.wolfram.com/StrangeAttractor.html
http://www.andrew.cmu.edu/user/coa/math-attractor.html

BIOCOMPLEXITY
http://www.eeb.princeton.edu/~slevin/cbc/cbc.html
http://ant.stanford.edu/
http://www.nbb.cornell.edu/neurobio/department/faculty/seeley/Seeley.html
http://www.princeton.edu/~spratt/
http://thesaurus.nbii.gov/
http://www.cipec.org/research/biocomplexity/
http://www.biogeo.cornell.edu/
http://www.krl.caltech.edu/~adami/cas.html

COMPUTER SCIENCES AND COMPLEXITY
http://parallel.hpc.unsw.edu.au/rks/docs/parcomplex/
http://dsp.jpl.nasa.gov/members/payman/swarm/
http://www.openp2p.com/pub/a/p2p/2003/02/21/bonabeau.html
http://www.cs.bham.ac.uk/~sra/People/Def/Dorigo/
http://www.swiss.ai.mit.edu/projects/amorphous/
http://plg.uwaterloo.ca/~cstheory/
http://cjtcs.cs.uchicago.edu/