Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

• Published in: PloS one Vol. 15; no. 12; p. e0243173
• Main Authors: Koda, Hiroki, Arai, Zin, Matsuda, Ikki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.12.2020; Public Library of Science (PLoS)
• Subjects: Agent based models; Agents (artificial intelligence); Algorithms; Animal behavior; Animal Distribution; Animal social behavior; Animals; Animals, Wild - physiology; Behavior, Animal; Biology and Life Sciences; Computer and Information Sciences; Computer Simulation; Coupling agents; Field investigations; Field tests; Models, Biological; Movement; Network analysis; Physical Sciences; Reconstruction; Research and Analysis Methods; Simulation; Social Behavior; Social conditions; Social network analysis; Social networks; Social organization; Social Sciences; Spatial distribution; Two dimensional models; Wild animals; Wildlife; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0243173

Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data—serialized order movements generated by simple bottom-up rules of collective movements—are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations—a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.