People
Police protect Nick Altrockfrom an adoring crowd during baseball's 1906 World Series
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See also: Crowd and Crowd simulation
A collection of people can also exhibit swarm behavior, such as pedestrians or soldiers swarming the parapets. In Cologne, Germany, two biologists from the University of Leeds demonstrated flock like behavior in humans. The group of people exhibited similar behavioral pattern to a flock, where if five percent of the flock changed the direction the others would follow. If one person was designated as a predator and everyone else was to avoid him, the flock behaved very much like a school of fish. Understanding how humans interact in crowds is important if crowd management is to effectively avoid casualties at football grounds, music concerts, and subway stations.
The mathematical modeling of flocking behavior is a common technology and has found uses in animation. Flocking simulations have been used in many films to generate crowds which move realistically. Tim Burton's Batman Returns was the first movie to make use of swarm technology for rendering, realistically depicting the movements of a group of bats using the boids system. The Lord of the Rings film trilogy made use of similar technology, known as massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.
An ant-based computer simulation using only six interaction rules has also been used to evaluate aircraft boarding behavior. Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. An airline system developed by Douglas A. Lawson uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. "The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline," Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive and depart quickly. The program can even alert a pilot of plane back-ups before they happen. "We can anticipate that it's going to happen, so we'll have a gate available," says Lawson.
Swarm behavior occurs also in traffic flow dynamics, such as the traffic wave. Bidirectional traffic can be observed in ant trails. In recent years this behavior has been researched for insight into pedestrian and traffic models. Simulations based on pedestrian models have also been applied to crowds which stampede because of panic.
Herd behavior in marketing has been used to explain the dependencies of customers' mutual behavior. The Economist reported a recent conference in Rome on the subject of the simulation of adaptive human behavior. It shared mechanisms to increase impulse buying and get people "to buy more by playing on the herd instinct." The basic idea is that people will buy more of products that are seen to be popular, and several feedback mechanisms to get product popularity information to consumers are mentioned, including smart card technology and the use of Radio Frequency Identification Tag technology. A "swarm-moves" model was introduced by a Florida Institute of Technology researcher, which is appealing to supermarkets because it can "increase sales without the need to give people discounts."
Helbing D, Keltsch J, Molnar P (1997). "Modelling the evolution of human trail systems". Nature. 388 (6637): 47–50. arXiv:cond-mat/9805158. Bibcode:1997Natur.388...47H. doi:10.1038/40353. PMID 9214501.
Helbing D, Farkas I, Vicsek T (2000). "Simulating dynamical features of escape panic". Nature. 407 (6803): 487–490. arXiv:cond-mat/0009448. Bibcode:2000Natur.407..487H. doi:10.1038/35035023. PMID 11028994.
Helbing D, Farkas IJ, Vicsek T (2000). "Freezing by heating in a driven mesoscopic system". Physical Review Letters. 84 (6): 1240–1243. arXiv:cond-mat/9904326. Bibcode:2000PhRvL..84.1240H. doi:10.1103/PhysRevLett.84.1240. PMID 11017488.
Robotics
Main article: Swarm robotics
See also: Ant robotics and Robotic materials
Kilobot thousand robot swarm developed by Radhika Nagpal and Michael Rubenstein at Harvard University.
The application of swarm principles to robots is called swarm robotics, while swarm intelligence refers to the more general set of algorithms.
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Partially inspired by colonies of insects such as ants and bees, researchers are modeling the behavior of swarms of thousands of tiny robots which together perform a useful task, such as finding something hidden, cleaning, or spying. Each robot is quite simple, but the emergent behavior of the swarm is more complex. The whole set of robots can be considered as one single distributed system, in the same way, an ant colony can be considered a superorganism, exhibiting swarm intelligence. The largest swarms so far created is the 1024 robot Kilobot swarm. Other large swarms include the iRobot swarm, the SRI International/ActivMedia Robotics Centibots project, and the Open-source Micro-robotic Project swarm, which are being used to research collective behaviors. Swarms are also more resistant to failure. Whereas one large robot may fail and ruin a mission, a swarm can continue even if several robots fail. This could make them attractive for space exploration missions, where failure is normally extremely costly. In addition to ground vehicles, swarm robotics includes also the research of swarms of aerial robots and heterogeneous teams of ground and aerial vehicles.
Plants
Scientists have attributed swarm behavior to plants for hundreds of years. In his 1800 book, Phytologia: or, The philosophy of agriculture and gardening, Erasmus Darwin wrote that plant growth resembled swarms observed elsewhere in nature. While he was referring to more broad observations of plant morphology and was focused on both root and shoot behavior, recent research has supported this claim.
Roots, in particular, display observable swarm behavior, growing in patterns that exceed the statistical threshold for random probability, and indicate the presence of communication between individual root apexes. The primary function of plant roots is the uptake of soil nutrients, and it is this purpose which drives swarm behavior. Plants growing in close proximity have adapted their growth to assure optimal nutrient availability. This is accomplished by growing in a direction that optimizes the distance between nearby roots, thereby increasing their chance of exploiting untapped nutrient reserves. The action of this behavior takes two forms: maximization of distance from, and repulsion by, neighboring root apexes. The transition zone of a root tip is largely responsible for monitoring for the presence of soil-borne hormones, signaling responsive growth patterns as appropriate. Plant responses are often complex, integrating multiple inputs to inform an autonomous response. Additional inputs that inform swarm growth includes light and gravity, both of which are also monitored in the transition zone of a root's apex. These forces act to inform any number of growing "main" roots, which exhibit their own independent releases of inhibitory chemicals to establish appropriate spacing, thereby contributing to a swarm behavior pattern. Horizontal growth of roots, whether in response to high mineral content in soil or due to stolon growth, produces branched growth that establish to also form their own, independent root swarms.
Other organisms
Bacteria
See also: Swarming motility and Microbial intelligence
Swarming is also used to describe groupings of some kinds of bacteria such as myxobacteria. Myxobacteria swarm together in "wolf packs", actively moving using a process known as bacterial gliding and keeping together with the help of intercellular molecular signals.
Quadrupeds
Sheep dogs (here a Border Collie) control the flocking behaviour of sheep
See also: Herd, Herd behaviour, and Animal migration
Parrish JK, Edelstein-Keshet L (1999). "Complexity, pattern and evolutionary trade-offs in animal aggregation" (PDF). Science. 284(5411): 99–101. Bibcode:1999Sci...284...99P. doi:10.1126/science.284.5411.99. PMID 10102827. Archived from the original(PDF) on 2011-07-20.
A swarm of migrating herrings
Sources
Blum C and Merkle D (2008) Swarm intelligence: introduction and applicationsSpringer. ISBN 978-3-540-74088-9.
Camazine S, Deneubourg JL, Franks NR, Sneyd J, Theraulaz G and Bonabeau E (2003) Self-Organization in Biological Systems Princeton University Press. ISBN 978-0-691-11624-2.
Fisher L (2009) The perfect swarm: the science of complexity in everyday lifeBasic Books. ISBN 978-0-465-01884-0.
Kennedy JF, Kennedy J, Eberhart RC and Shi Y (2001) Swarm intelligenceMorgan Kaufmann. ISBN 978-1-55860-595-4.
Krause, J (2005) Living in Groups Oxford University Press. ISBN 978-0-19-850818-2
Lim CP, Jain LC and Dehuri S (2009) Innovations in Swarm IntelligenceSpringer. ISBN 978-3-642-04224-9.
Miller, Peter (2010) The Smart Swarm: How understanding flocks, schools, and colonies can make us better at communicating, decision making, and getting things done Penguin, ISBN 978-1-58333-390-7
Nedjah N and Mourelle LdM (2006) Swarm intelligent systems Springer. ISBN 978-3-540-33868-0.
Sumpter, David JT (2010) Collective Animal Behavior Princeton University Press. ISBN 978-0-691-14843-4.
Vicsek A, Zafeiris A (2012). "Collective motion". Physics Reports. 517 (3–4): 71–140. arXiv:1010.5017. Bibcode:2012PhR...517...71V. doi:10.1016/j.physrep.2012.03.004.
External links
New York Times article on investigations into swarming
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