Basak Udoy S, Sattari Sulimon, Hossain Motaleb, Horikawa Kazuki, Komatsuzaki Tamiki
Graduate School of Life Science, Transdisciplinary Life Science Course, Hokkaido University, Sapporo, Hokkaido 060-0812, Japan.
Pabna University of Science and Technology, Pabna 6600, Bangladesh.
Biophys Physicobiol. 2021 May 15;18:131-144. doi: 10.2142/biophysico.bppb-v18.015. eCollection 2021.
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.
（单细胞和多细胞）系统的同步运动几乎随处可见。理解生物体如何被调节以产生同步行为是集体运动领域中具有挑战性的问题之一。据推测，群体中的一个或几个主体调节整个集体的动态，这些主体被称为领导者。识别领导者（有影响力的）主体非常关键。本文回顾了代表不同类型领导力的不同数学模型。我们专注于改进领导者 - 跟随者分类问题。通过模拟模型发现，使用交互域信息显著提高了使用线性方案和信息论方案来量化影响力的领导者 - 跟随者分类能力。本文还回顾了可用于利用主体运动数据识别交互域的不同方案。
