Transfer entropy dependent on distance among agents in quantifying leader-follower relationships

TL;DR

This paper reviews mathematical models and methods for identifying leader-follower relationships in synchronized collective motion, emphasizing the role of transfer entropy and interaction domain information in improving classification accuracy.

Contribution

It introduces the use of transfer entropy dependent on distance among agents and reviews schemes for identifying interaction domains to enhance leader detection.

Findings

Interaction domain information improves leader-follower classification.

Transfer entropy's effectiveness depends on agent distance.

Simulation results support the use of information-theoretic measures.

Abstract

Synchronized movement of (both unicellular and multicellular) systems can be observed almost everywhere. Understanding of how organisms are regulated to synchronized behavior is one of the challenging issues in the field of collective motion. It is hypothesized that one or a few agents in a group regulate(s) the dynamics of the whole collective, known as leader(s). The identification of the leader (influential) agent(s) is very crucial. This article reviews different mathematical models that represent different types of leadership. We focus on the improvement of the leader-follower classification problem. It was found using a simulation model that the use of interaction domain information significantly improves the leader-follower classification ability using both linear schemes and information-theoretic schemes for quantifying influence. This article also reviews different schemes that can be used to identify the interaction domain using the motion data of agents.