Anatomy of leadership in collective behaviour

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, and 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 imply 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 principal 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 01, 2018
Source ID
10.1063/1.5024395

Entities

People

  • Andrew M. Berdahl
  • Erik M. Bollt
  • Jie Sun
  • Joshua Garland

Organizations

  • Army Research Office
  • Clarkson University
  • John Templeton Foundation
  • Office of Naval Research
  • Santa Fe Institute
  • Simons Foundation
  • University of Washington

Tags

Readers

  • Educational Psychology
  • Mathematical Modeling and Probability Theory.
  • Organizational Psychology.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control