Anatomy of Leadership in Collective Behaviour
arXiv:1802.01194 · doi:10.1063/1.5024395
Abstract
Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership", e.g., position, causality, influence, information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group implies that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper we develop an anatomy of leadership, identify several principle components and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.
13 pages, 3 figures
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Cited by in corpus (6)
- Modes of Information Flow in Collective Cohesion
- The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
- Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts
- Transfer entropy dependent on distance among agents in quantifying leader-follower relationships
- Leadership Inference for Multi-Agent Interactions
- Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming