Here are a couple of definitions of swarming:
1 a: a great number of honeybees emigrating together from a hive in company with a queen to start a new colony elsewhere b: a colony of honeybees settled in a hive
2 a: a large number of animate or inanimate things massed together and usually in motion : throng <swarms of sightseers> <a swarm of locusts> <a swarm of meteors> b: a number of similar geological features or phenomena close together in space or time <a swarm of dikes> <an earthquake swarm>
From the definition it is apparent that the idea of a swarm is perhaps most commonly used to describe animal behavior. In this particular case, swarming refers specifically to the behavior of insects, namely ants, bees or locusts. However if we look at the broader expanse of animal behavior we can find similar behaviors in many different species. For example birds exhibit flocking behavior and fish are described as traveling in schools or shoals.
flock: a group of animals (as birds or sheep) assembled or herded together
school: a large number of fish or aquatic animals of one kind swimming together
These behaviors, swarming, flocking, schooling are much more than just simple groupings of animals. When observing these groups, there emerge new complex behaviors that serve to benefit the survival of the group as a whole. These groups or throngs form highly complex systems of interaction that are very difficult, if not impossible to predict. For example, from observation of the simple behaviors of any one individual in the group it is impossible to predict the actions of the group as a whole. Therefore, from a micro perspective, the action of the group can appear very chaotic. However, from a larger perspective, the macro view, a cohesive pattern of behavior may emerge.
For example, in the case of bees, when the hive reaches a certain density, the bees will form a swarm and seek a new home. They use a simple decision making strategy to select the optimal location:
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. (Peter Miller, National Geographic)
So for the bees, a complex decision making strategy has evolved from the simple behaviors of members of the swarm. Observation of any single bee will not predict the outcome of the decision. It is only by observation of the group as a whole that the outcome can be foreseen. As a swarm, the bees are exhibiting a form of intelligence that is not present in any individual member of the group. This group is exhibiting complex adaptive behavior. The whole is greater than the sum of its parts.
As it turns out, there is an entire sub discipline of mathematics dedicated to describing just this sort of complex system. Chaos theory and complexity theory study the properties of simple systems that demonstrate emergent properties from apparent chaos. In combination with population biology, it is possible to create mathematical models that imitate the behavior of animal swarms in nature. One of the first people to do this was Craig Reynolds. He created a program he called “Boids” that modeled the flocking behavior of birds within a virtual 3D environment. With his program, you can apply very simple rules that control the behavior of a flock. The three rules are:
- Separation: steer to avoid crowding local flockmates.
- Alignment: steer towards the average heading of local flockmates.
- Cohesion: steer to move toward the average position of local flockmates
From these three rules, we see very interesting behaviors in flocks emerge such as a flock that dynamically splits and re-joins to avoid obstacles. There are many of these sorts of models for flocks, swarms, and schools – all with different sets of simple rules that can be applied to the individuals in the group. It should be noted that in swarming theory there is the absence of central control. There is no manager bird that tells other birds how to fit in formation. These groups are self-organizing – obeying simple rules without any higher organizing authority present.
So, if we can use the mathematics of complex adaptive systems and chaos to model the behavior of animals, can we create similar models for the group behavior of crowds of people? Here we enter the field of economics and game theory. We are attempting to create a predictive model for the behavior of groups of people (organizations or markets). In “The Wisdom of Crowds” Author James Surowiecki does just that: he applies simple rules of behavior to individuals in a group to help explain the complex behavior that evolves in crowds.
Swarming theory has also found use in additional domains such as artificial intelligence and military applications. In AI, swarming theory is being used to manage swarms of tiny robots that collaborate to perform tasks (nanotechnology). In the military, swarming has relevance to both military tactics as well as for controlling the behavior of drones that are playing a larger and larger role on the modern battlefield.
So we find the notion of swarming, which originally came from the animal behavior, is actually applicable to a much wider array of domains. In fact, swarming actually has a mathematical models to back it up. So it appears that Swarming has a solid foundation in theory and a broad range of applicability. The question is, how can we apply the principles of Swarming Theory to Agile teams?
One great example of swarming is the Open Space meeting (an activity that is becoming increasingly popular at agile conferences). An open space is organized with the following four principles:
1. Whoever comes are the right people.
2. Whatever happens is the only thing that could have.
3. Whenever it starts is the right time.
4. When it’s over, it’s over.
These four principles are the only rules needed for an Open Space meeting. If you have an idea, you share it. If people are interested, they listen. If people are not interested, they move on. It enables a group to focus on talking about the most relevant issues for them, rather than being forced to sit through lectures from others that they may not be interested in. Using only these rules allows for unpredictable discussions to emerge. We don’t know in advance who will be talking. We don’t know in advance what topics will be the most interesting. All we do in an open space is create the conditions that allow those topics to emerge.
The key to making swarming work is applying the right rules.