// The Song Remains The Same

(This article is an entry on my continuing series on Self Organization. The first article was Emergence, and the article that preceeded this one was based on stigmergy. )

What is it that governs here? What is it that issues orders, forsees the future, elaborates plans, and preserves equilibrium?

- Maeterlinck [1]

Social Insects are an incredible biological machine. They operate at the colony level as a single body but retain semi-independence at the local level (their level of independence is tied to their genetic programming, which in itself is the root of a philosophical topic I can’t begin to broach here). These social insect colonies are able to adapt to the environment and robustly thrive in uncertain conditions because as we discussed before — Social insects are able to operate with no central leader and exhibit the emergent collective intelligence of groups of simple agents.

Upon first sight, a critic may argue that these complex behaviors are too sophisticated for such simple agents to achieve even collectively. Give that our point of view is always relative and subjective, people want to believe that it only occurs in places where more sophisticated organisms are involved. The really interesting thing is that it occurs at all levels of nature, with insect societies being one of the more interesting examples — as they simply do not have the mental capacities anywhere near humans or fish, yet create some very complex nests and structures.

Even though an insect doesn’t have higher order mental capacities, it still is a complex creature. However, an insect is still not complex enough on its own to explain the complexities of social insect colonies. Insect colonies are perfect examples of synergy or systems that are much greater than the sum of their parts. In order to operate in such a coordinated manner with no hierarchical control, social insects must employ highly decentralized operations to achieve scale while still communicating state to other operating units. What are some examples of this, and how do they do it? Self organization is the key to all of it.

There are a lot of interesting places in the insect world where self organization drives the basic mechanics of life for these insects. Some examples are

• leaf cutter ants - forage for leaves to grow fungi, where foraging for leaves is a form of dynamic discovery which employs pheromone trails and stigmergy
• army ants - hundreds of thousands of ants which swarm an area in a giant raid party, again achieving discovery of new food sources through pheromone trails and stigmergy
• honey bees nests - construction of honey comb, brood sorting, temperature regulation using stigmergy and positive/negative feedback
• wasp nest construction - complex geometric patterns emerge from decentralized construction using stigmergy and localized rule bases.
• termite chamber construction - decentralized clustering though the use of stigmergy, pheromone, and positive/negative feedback

In most social insect colonies a worker usually does not perform all tasks, but rather specializes in a set of tasks according to its morphology, age, or chance. Some types of insect societies have multiple body types, such as majors and minors, which are built for different subsets of tasks, but are also flexible enough to perform one another’s tasks if need be [2] (large loss of population, etc). Age can contribute to how the insect switches task or deploys themselves into the work force, but one of the strongest factors (especially in ant colonies) is chance and the role of task switching based on response thresholds.

Response thresholds and task-stimulus-associations are concepts introduced by Bonabeau (et al) [3] that help explain Wilson’s observations. For different types of stimulus an ant has an associated threshold and as it perceives locally said stimulus, it raises its set point a little, or how much it has been stimulated by that stimulus. Based on how much stimulus an ant has perceived for its various sets of associations, the ant will probabilistically switch to a new task every so often. For ant colonies examples of these tasks are midden work, nest defense, and foraging for food in raiding parties (discovery).

The probability of which task the ant switches to is governed by the levels of simulation for each association (ie, if task-assoc for A is much higher relatively than task-assoc-B and task-assoc-C, then it is far more likely to switch to that task — think of it as probabilistic process switching in an operating system).  The associations govern how the ant switches tasks as it encounters stimulus, with the aggregate effect of all ants operating in this manner gives the colony a way to do decentralized load balancing. This has been shown to be incredibly robust and effective as a load balancing technique in ant colonies. Nature, it seems, is pretty good at this stuff.

Clustering and decentralized construction is another phenomenon associated with social insects. Termites build complex underground chambers by moving pebbles about with multiple termites touching each pebble as the pebble moves probabilistically towards a resting spot [4]. As termites move about an area they lay a chemical called “pheromone” which indicates a path taken to other insects. The pheromone gradient attracts termites to areas of high densities of pebbles as they are probabilistically attracted towards those areas at each step. Large amounts of pebbles tend to make termites want to drop their pebbles, which when coupled with the pheromone effect, creates a positive feedback look.

To counter the downside or premature convergence on a suboptimal chamber design, termites need a measure of negative feedback. Evaporation of pheromone in termite colonies is a source of negative feedback, as it weakens the pheromone over time resulting in less gradient. As the pheromone gradient weakens, it will attract less termites and therefore less pebbles will be deposited in the area. We need a measure of negative feedback to keep our system from converging on sub-optimal “solutions” too quickly, just like the termites employ negative feedback to keep from clustering their pebbles in many small piles too quickly.

So as we’ve seen, social insect colonies can do some pretty amazing things with relatively simple local interactions and no hierarchical control. We’ve seen ants find food with stigmergy and positive / negative feedback and we’ve seen termites build a nest with the same principles only applied in a different context.

As any engineer (or just curious person) begins to think about insect societies, its easy to see obstacles that ants overcome as the same obstacles we are faced with in our work and in the technology around us. Much research around self organization (and swarm intelligence) is being applied to areas in technology such as

• MANETs (ad hoc networks)
• Robotics
• Telecommunications
• Data mining
• Load balancing
• Search / discovery

and many more. As our world becomes more complex with respect to the amount of data and technology that we must address, hierarchical methods begin to break down.

Something I want to look at in coming articles is discovery with respect to self organization, and how that applies to linked data and the internet.

• Where can we go with that?
• Does it relate, and how so? What are the implications?
• What happens with scale and centralization, and how do we map self organizational techniques onto other problem domains that face these same challenges?

To bridge these ideas and concepts my next article will focus on discovery and social insects a little more in-depth, and begin to talk about my research work from graduate school in self organization and ad hoc networks.

References

[1] Maeterlinck, M. The Life of the White Ant. London: George Allen and Unwin, 1927

[2] Wilson, E. O. “The Relation Between Caste Ratios and Division of Labour in the Ant Genus Pheidole (Hymenoptera: Formicidae).” Behav. Ecol. Sociobiol. 16 (1984): 89-98

[3] E. Bonabeau, G. Theraulaz, and J.-L. Deneubourg. “Quantitative Study of the Fixed Threshold Model for the Regulation of Division of Labour in Insect Societies.” Proceedings Roy. Soc. London B 263 (1996): 1565-1569

[4] M Roth, S Wicker, Termite: A Swarm Intelligent Routing Algorithm for Mobile Wireless Ad-Hoc Networks