Complex adaptive systems

Complex adaptive systems (CASs) are systems of relatively similar interconnected or interacting components or micro-structures which mutate and self-organize corresponding to the influences of their environment. Their constitution hence allows them adapt to changing environments, in particular to environments that change in reaction to their own adaptations.

Examples of complex adaptive systems include: social insect and ant colonies; the biosphere and the ecosystem; the brain and the immune system; the cell and the developing embryo; the stock market; the global macroeconomic network; the internet ...

The ideas and models of CAS are essentially evolutionary, grounded in modern chemistry, biology, exaptation and evolution and simulation models in economics and social systems.

Also central to the concept are the principles of emergence and self-organization.

Example 1: Ant search

When ants are looking for food they use a very simple but highly efficient communication mechanism which enormously increases the efficiency of their random search-movements. An observer may interpret the effect of this mechanism as social learning. To explain this one could assume that ants search food according to the Monte Carlo method. The more ants search for food in a given territory, the higher is the probability to find it within an acceptable time. The law of high numbers and the Matthew effect play a role in this context. As soon as an ant finds a food source and carries the food to the nest, it leaves behind an odor trail consisting of pheromones. This odor trail attracts other ants close by and leads them to the food source. Joining in to food transportation, the other ants use the same communication method and intensify the odor trail, thus raising the probability further that even more ants join in on the food trail.

However, the pheromones are quite volatile and diffuse rather quickly if not permanently renewed. Therefore, if the food source is consumed, the ants loose their means of guidance and start roaming randomly again. This raises the probability to find new food source - to adapt to an environment of changing subsistence conditions.

ant search

Click on the image to access a Netlogo/Tortoise implementation of the model.

Today, this mechanism is successfully used to let swarms of robot coordinate via self-organization.

Example 2: The beehive

The beehive is an example for a natural air conditioning system driven by negative feedback that turns out to be more efficient than common air conditioners. In order to ensure optimal parental care, the temperature inside a beehive has to stay within a small range. Therefore, if temperatures are too low the bees start to accumulate around the breed and warm it by way of a special humming technique. If on the other extreme, temperature is too high, they collectively use their wings to fan cooler air to the breed. However, investigations showed that bees differ genetically regarding their temperature sensations. While for some bees the temperature might still be comfortable, for others it might be too hot or too cold. Some even start fanning while others are still humming. This economically inefficient behavior results in an interesting effect on the range in which the temperature fluctuates: the differentiation of individual temperature sensations causes a significantly weaker oscillation of the temperature inside the beehive. Heterogeneity within societies hence, can have advantageous effects.

bee hive

Click on the image to access a Netlogo/Tortoise implementation of the model.

Example 3: Fire flies

The synchronization of oscillating entities has long been thought to be dependent on some sort of conductor. When early travellers to the tropics reported on synchronously flashing fireflies for example, European biologists dismissed these accounts as fantasies. Spontaneous synchronization was inconceivable.

Studies on interconnected systems, however, revealed not only the possibility of self-organizing synchronization, but proved the phenomenon to be one of the premises for the emergence of complex structures. Today, examples for spontanous synchronization range from synchronically waving fiddler crabs to cardiac pacemaker cells, female menstrual synchrony, traffic patterns, quantum choruses and the unison firing of neurons.

Simulation emerges through self-organizing synchronization of moving oscillators which tune their periodicity with the help of impulses from other oscillators nearby. The example is that of fireflies. The phenomenon was first observed in the 17th century, but the explanation was not found until computer simulations were used in the 1960s. In order to model this interaction of oscillators, software agents representing fire flies count from 1 to 10 similar to a clockwork, switch on their light for one count, and then start counting again from 1 to 10. Agents start counting randomly at different times, but when another agent near by flashes its light, they reset their clockwork to 1. As the agents are moving and shuffling like the fireflies do, this simple mechanism is enough to synchronize the agents by time and let them blink synchronously.

fire flies

Strogatz, Steven (2003): Sync. How Order emerges from Chaos in the Universe, Nature and Daily Life. New York: Hyperion.

Miller, John H. / Page, Scott E. (2007): Complex Adaptive Systems. An Introduction to Computational Models of Social Life. Princeton (Princeton UP).

Gordon, D.M. (1999): Ants at Work: how an insect society is organized. W. W. Norton.

Resnick, Mitchell (1994): Turtles, Termites, and Traffic Jams. Cambridge, MA: MIT Press.