Collective behavior coordination with predictive mechanisms

In natural flocks/swarms, it is very appealing that low-level individual intelligence and communication can yield advanced coordinated collective behaviors such as congregation, synchronization and migration. In the past few years, the discovery of collective flocking behaviors has stimulated much interest in the study of the underlying organizing principles of abundant natural groups, which has led to dramatic advances in this emerging and active research field. Inspired by previous investigations on the predictive intelligence of animals, insects and microorganisms, we seek in this article to understand the role of predictive mechanisms in the forming and evolving of flocks/swarms by using both numerical simulations and mathematical analyses. This article reviews some basic concepts, important progress, and significant results in the current studies of collective predictive mechanisms, with emphasis on their virtues concerning consensus improvement and communication cost reduction. Due to these advantages, such predictive mechanisms have great potential to find their way into industrial applications.

I. Introduction

Collective behavior coordination in a network of dynamic agents has attracted a lot of attention in recent years, in particular, from physicists, biologists, mathematicians and social scientists. Investigation of the fundamental mechanisms yielding collective behaviors is significant not only for bio-group of animals or group activities of humans (e.g., riots, fashion and escaping panic [1], etc.), but also for nanotechnology applications such as spontaneous magnetization [2]. A large volume of literature has reported some research progress in collectively migrating bacteria [3], insects or birds [4], and in phenomena where groups of organisms or non-living objects reach an ordered or synchronized state such as the one corresponding to fireflies flashing in unison [5] or people clapping in phase during rhythmic applause [6].