In recent years, the concept of self-organization has been used to understand collective behaviour of animals. The central tenet of self-organization is that simple repeated interactions between individuals can produce complex adaptive patterns at the level of the group. Inspiration comes from patterns seen in physical systems, such as spiralling chemical waves, which arise without complexity at the level of the individual units of which the system is composed. The suggestion is that biological structures such as termite mounds, ant trail networks and even human crowds can be explained in terms of repeated interactions between the animals and their environment, without invoking individual complexity. Here, I review cases in which the self-organization approach has been successful in explaining collective behaviour of animal groups and societies. Ant pheromone trail networks, aggregation of cockroaches, the applause of opera audiences and the migration of fish schools have all been accurately described in terms of individuals following simple sets of rules. Unlike the simple units composing physical systems, however, animals are themselves complex entities, and other examples of collective behaviour, such as honey bee foraging with its myriad of dance signals and behavioural cues, cannot be fully understood in terms of simple individuals alone. I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems. Ultimately, this research could inform not only our understanding of animal societies, but also the principles by which we organize our own society.

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The principles of collective animal behaviour.

Publisher Summary The chapter discusses an emerging area of study: that of applying self-organization theory to mobile vertebrate groups composed of many interacting individuals such as bird flocks, ungulate herds, fish schools, and human crowds in an attempt to improve our understanding of underlying organizational principles. Mathematical modeling is becoming increasingly recognized as an important research tool when studying collective behavior. The chapter presents the interaction dynamics among individuals result in the formation, internal structuring, and collective behaviors of vertebrate groups. The chapter explores the distribution of grouping individuals over larger spatial and temporal scales, and discusses how individual behaviors lead to population-level dynamics. Behavioral differences among individuals within a group may have an important internal structuring influence. By using simulation models, it can be shown how individuals can modify their positions relative to other group members without necessitating information about their current position within the group. In considering self-organization within vertebrate groups it is evident that the organization at one level, for example, that of the group relates to that at higher levels. For example, self-sorting processes that lead to internal structuring within groups also result in population-level patterns when such groups fragment, thus affecting the probability that an individual will be in a group of a given size and composition at any moment in time. These population properties then feed back to the individual interactions by changing the probability of encounters among different members of a population. The chapter concludes that to understand collective behaviors fully, these properties cannot necessarily be considered in isolation.

Self-Organization and Collective Behavior in Vertebrates

Stigmergy is a class of mechanisms that mediate animal-animal interactions. Its introduction in 1959 by Pierre-Paul Grasse 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.

A brief history of stigmergy

The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.

The biological principles of swarm intelligence

https://webdocs.cs.ualberta.ca/~kube/papers/KubeBonabeauRAS2000.pdf

Cooperative transport by ants and robots

Self-organization was introduced originally in the context of physics and chemistry to describe how microscopic processes give rise to macroscopic stuctures in out-of-equilibrium systems, Recent research that extends this concept to ethology suggests that it provides a concise description of a wide range of collective phenomena in animals, especially in social insects. This description does not rely on individual complexity to account for complex spatiotemporal features that emerge at the colony level, but rather assumes that intractions among simple individuals can produce highly structured collective behaviours.

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Jean-Louls Deneubourg

Papers

Self-organization in social insects.