Identifying Emergent Behaviors in Complex Systems

Forager ants in the Arizona desert have a problem: after leaving the nest, they don’t return until they’ve found food. On the hottest and driest days, this means many ants will die before finding food, let alone before bringing it back to the nest. Honeybees also have a problem: even small deviations from 35ºC in the brood nest can lead to brood death, malformed wings, susceptibility to pesticides, and suboptimal divisions of labor within the hive. All ants in the colony coordinate to minimize the number of forager ants lost while maximizing the amount of food foraged, and all bees in the hive coordinate to keep the brood nest temperature constant in changing environmental temperatures.

The solutions realized by each system are necessarily decentralized and abstract: no single ant or bee coordinates the others, and the solutions must withstand the loss of individual ants and bees and extend to new ants and bees. They focus on simple yet essential features and capabilities of each ant and bee, and use them to great effect. In this sense, they are incredibly elegant.

In this talk, we’ll examine a handful of natural and computer systems to illustrate how to cast system-wide problems into solutions at the individual component level, yielding incredibly simple algorithms for incredibly complex collective behaviors.