The honey bee waggledance, social wasp nest-building, and termite mound construction have been variously studied as instances of emergent behaviour, collective intelligence, and swarm intelligence. Now we add collaborative intelligence to this list. Although it could be debated as to whether these creatures represent solely collective intelligence or both collective and collaborative intelligence in that they do not all play the same roles and can shift roles as needed by the community, their effective systems of communicating and making individual decisions that serve the interests of the community as a whole are instructive to study.
The ideas of “collective intelligence” have been broadly explored in a range of ways, one of which can be loosely termed "ant algorithms." The purpose of this page is not exhaustively to report on this vast domain of research, both in biology and in computer science, but to provide initial pointers to some significant references relevant to principles of collaborative intelligence.
Pierre Lévy in his book Collective Intelligence defines the term to exclude ants, or any group behaving such that the individuals are indistinguishable from each other.
Entomologist W. M. Wheeler was an early proponent of the idea that ants act like the cells of a single “superorganism.” He noted that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism (1911). Many biologists have been fascinated by these instances of multi-agent intelligent systems, which have been termed super-organisms.
Biologist Deborah Gordon, in her 2007 article in Nature, encapsulated why Ants have attracted such interest from the community of researchers working on how multi-agent systems can be programmed to be more intelligent: "Because most of the dynamic systems that we design, from machines to governments, are based on hierarchical control, it is difficult to imagine a system in which the parts use only local information and the whole thing directs itself. To explain how biological systems operate without central control — embryos, brains and social insect colonies are familiar examples — we often fall back on metaphors from our own products, such as blueprints and programmes. But these metaphors don’t correspond to the way a living system works, with parts linked in regulatory networks that respond to environment and context. . . . A better route to understanding the dynamics of apparently self-organizing systems is to focus on the details of specific systems." Gordon's Lab at Stanford has spent the last twenty-five years studying colonies of Harvester ants in southwestern United States.
In fact, there are differences across species. Individuals within the same species do not perform the same roles, a the same individual can perform different roles as needed by the colony as a whole. Less well known are these instances of ant behavior manifesting collaborative intelligence with ants shifting roles as needed and performing different roles in the larger, effectively performing system of the ant colony.
The collective intelligence analog has generally been adopted in computer science and operations research where the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems that can be reduced to finding good paths through graphs. This algorithm is a member of ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants.
The A-PR Hypothesis (Autonomy and Pattern Recognition) can be observed at the level of each individual ant, responding to signals and adapting its performance for the good of the colony.
Bonabeau, E., M. Dorigo, G. Theraulaz. 1999. Swarm intelligence: from natural to artificial systems. Oxford University Press.
Camazine, S., J.L. Deneubourg, N.R. Franks, J. Sneyd, G. Theraulaz and E. Bonabeau. 1999. Self-Organization in Biological Systems. Princeton University Press.
Dorigo, M., T. Stützle. 2004. Ant Colony Optimization. MIT Press.
Franks, N.R. 1980. "Army Ants: A Collective Intelligence." American Scientist. 77, 139-45.
Franks, N.R., et al. 1991. "The Blind Leading the Blind in Army Ant Raid Patterns: Testing a Model of Self-organization (Hymenoptera: Formicidae)." Journal of Insect Behavior. 4(5), 583-607.
Gordon, D. M. 1999. Ants at Work: how an insect society is organized. Free Press, Simon and Schuster. 2000 paperback, W. W. Norton.
_______. 2007. Control without hierarchy. Nature 4468:143.
_______. 2010. Ant Encounters: Interaction Networks and Colony Behavior. Princeton University Press.
_______. 2010. The fusion of ecology and behavioral ecology. Behavioral Ecology, in press.
Gordon, D. M., S. Holmes and S. Nacu. 2008. The short-term regulation of foraging in harvester ants. Behavioral Ecology 19:217-222. Gordon, D.M., A. Guetz, M.J. Greene, and S. Holmes. 2011. Colony variation in the collective regulation of foraging by harvester ants. Behavioral Ecology, in press.
Lévy, Pierre. 1997. Trans. Robert Bononno. Collective Intelligence: Mankind’s Emerging World in Cyberspace. NY: Plenum Press. 51.
MacKay, Charles. 1852. Memoirs of Extraordinary Popular Delusions and the Madness of Crowds. London: Richard Bentley. Reissued by New Harmony Books, 1980.
Turner, J. Scott. 2002. The Extended Organism: The Physiology of Animal-Built Structures. Harvard University Press.
Wilson, E.O. 1998. Attini: transition from hunter-gatherer to agriculture. Behavioral Ecology and Sociology. 7:143 –156.
Wheeler, W. M. 1911. The ant-colony as an organism. Journal of Morphology, 22: 307–325.
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