Complexity and Swarming Systems

Abstract

Complexity science has shown that collective behaviors in animal groups, that is swarms, emerge from repeated local interactions between neighboring individuals. It has also revealed that a set of simple local interaction rules applied to very simple artificial agents gives rise to complex patterns possessing long-range and long-lasting dynamic order. These swarming systems, be them natural or artificial, are all characterized by somehow similar features: (i) lack of central controller or leader overseeing the collective dynamics, instead the latter emerges through self-organization, (ii) local perception of the environment leading to a certain level of global knowledge by means of effective distributed information sharing, and (iii) a high degree of adaptation to rapidly changing circumstances. These features afford swarms unique distributed problem solving capabilities—a.k.a. swarm intelligence—such that collectively they perform tasks far exceeding each individual agent’s ability.