Army ant colonies display complex foraging raid patterns involving thousands of individuals communicating through chemical trails. In this article we explore, by means of a simple search algorithm, the properties of these trails in order to test the hypothesis that their structure reflects an optimized mechanism for exploring and exploiting food resources. The raid patterns of three army ant species, Eciton hamatum , Eciton burchelli , and Eciton rapax , are analyzed. The respective diets of these species involve large but rare, small but common, and a combination of large but rare and small but common food sources. Using a model proposed by Deneubourg et al. [4], we simulate the formation of raid patterns in response to different food distributions. Our results indicate that the empirically observed raid patterns maximize return on investment, that is, the amount of food brought back to the nest per unit of energy expended, for each of the diets. Moreover, the values of the parameters that characterize the three optimal pattern-generating mechanisms are strikingly similar. Therefore the same behavioral rules at the individual level can produce optimal colony-level patterns. The evolutionary implications of these findings are discussed.

Stigmergy is a class of mechanisms that mediate animal-animal interactions. Its introduction in 1959 by Pierre-Paul Grassé made it possible to explain what had been until then considered paradoxical observations: In an insect society individuals work as if they were alone while their collective activities appear to be coordinated. In this article we describe the history of stigmergy in the context of social insects and discuss the general properties of two distinct stigmergic mechanisms: quantitative stigmergy and qualitative stigmergy.

An extremely large body of theoretical work exists on pattern formation, but very few experimental results have confirmed the relevance of theoretical models. It is argued in this article that the notion of agent-based pattern formation, which is introduced and exemplified, can serve as a basis to study pattern formation in nature, especially because pattern-forming systems based on agents are (relatively) more easily amenable to experimental observations. Moreover, understanding agent-based pattern formation is a necessary step if one wishes to design distributed artificial pattern-forming systems. But, to achieve this goal, a theory of agent-based pattern formation is needed. This article suggests that it can certainly be derived from existing theories of pattern formation.