Inference of Causal Information Flow in Collective Animal Behavior

TL;DR

This paper applies an information theoretic approach to infer causal information flow networks in collective animal behavior, specifically in insect swarms, revealing long-range communication channels beyond local interactions.

Contribution

It introduces the use of optimal causation entropy (oCSE) to identify direct causal links in animal groups, demonstrating its effectiveness in analyzing 3D flight data.

Findings

Long-range information channels are more common than expected.

The method successfully reconstructs communication networks in insect swarms.

Tools are broadly applicable to natural intercommunication studies.

Abstract

Understanding and even defining what constitutes animal interactions remains a challenging problem. Correlational tools may be inappropriate for detecting communication between a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of an information theoretic channel of direct causal information flow. In this work, we consider time series data obtained by an experimental protocol of optical tracking of the insect species Chironomus riparius. The data constitute reconstructed 3-D spatial trajectories of the insects' flight trajectories and kinematics. We present an application of the optimal causation entropy (oCSE) principle to identify direct causal relationships or information channels among the insects. The collection of channels inferred by oCSE describes a network of information flow within the swarm. We find that information channels with a long spatial range are more common than expected under the assumption that causal information flows should be spatially localized. The tools developed herein are general and applicable to the inference and study of intercommunication networks in a wide variety of natural settings.