For social insects, teamwork is predominantly self-organized. Coordinated primarily through the interactions of individual colony members, the insects can solve complex problems even though each interaction may be very simple.
To understand the power of self-organization, consider how ants find the shortest distance to a food source simply by laying and following chemical trails. For example, two ants leave their nest in search of food and venture off on separate paths. While walking, they release a trail of pheromones (chemicals) that the other ants in the colony can detect. The ant that takes the shorter route doubles back to the nest more quickly, adding more pheromones on top of those already left on the first passage (away from the nest). This reinforcement of pheromones leaves a higher concentration for other ants to sense, and as a result, other members of the colony detect and follow this more distinct trail of pheromones, as well.
In the same way, termites, with no supervision, collectively build mounds with ambient internal temperatures and comfortable levels of oxygen and carbon dioxide. Although science has yet to fully explain the exact mechanisms through which these architecturally efficient mounds are created, several models indicate that termites are more likely to deposit soil particles where other individuals have just placed particles, due to the presence of short-lived pheromones.
Self-organization enables a swarm of social insects to carry out complex tasks through the collective duties of individuals within the colony without being centrally controlled. The collective effort of the whole group is the only means by which the group is able to sustain itself and grow as a community.
This summary was contributed by Stephen Nelson.
Social Insect Colonies as Complex Adaptive SystemsEcosystemsJuly 25, 2002
“In essence, we believe that social insects have been so successful–they are almost everywhere in the ecosphere–because of three characteristics:
- flexibility (the colony can adapt to a changing environment);
- robustness (even when one or more individuals fail, the group can still perform its tasks); and
- self-organization (activities are neither centrally controlled nor locally supervised).
Business executives relate readily to the first two attributes, but they often balk at the third, which is perhaps the most intriguing. Through self-organization, the behavior of the group emerges from the collective interactions of all the individuals. In fact, a major recurring theme in swarm intelligence (and of complexity science in general) is that even if individuals follow simple rules, the resulting group behavior can be surprisingly complex–and remarkably effective. And, to a large extent, fiexibility and robustness result from self-organization.” (Bonabeau and Meyer 2001:108)
Swarm intelligence. A whole new way to think about businessHarvard Business ReviewMay 1, 2001
“Each insect in a colony seems to have its own agenda, and yet the group as a whole appears to be highly organized. Apparently the seamless integration of all individual activities does not require any supervision. In fact, scientist who study the behavior of social insects have found that cooperation at the colony level is largely self-organized: in numerous situations the coordination arises from interactions among individuals. Although these interactions might be simple (one ant merely following the trail left by another), together they can solve difficult problems (finding the shortest route among countless possible paths to a food source). This collective behavior that emerges from a group of social insects has been dubbed ‘swarm intelligence.'” (Bonebeau and Theraulaz 2000:73-74)
Swarm SmartsSci AmDecember 16, 2009
“…the ants can select the shortest path to the food source because they lay and follow pheromone (chemical) trails…the colony may occasionally get ‘stuck’ on a longer path if by chance the longer path is the first one marked. In using the ‘trail laying-trail following’ metaphor for optimization purposes, computer scientists found it essential to improve the convergence properties of their algorithms by artificially increasing the rate of pheromone evaporation beyond biological plausibility.” (Bonebeau et al. 2000:39)