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Extending Group Dynamics Models: Incorporating Heterogeneous Networks to Analyze Cooperative Behaviors and Collective Intelligence in Biological Groups
Fu, Y.; Zhu, J.; Li, X.; Han, X.; Tan, W.; Huangpeng, Q.; Duan, X. Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders. Mathematics2024, 12, 1160.
Fu, Y.; Zhu, J.; Li, X.; Han, X.; Tan, W.; Huangpeng, Q.; Duan, X. Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders. Mathematics 2024, 12, 1160.
Fu, Y.; Zhu, J.; Li, X.; Han, X.; Tan, W.; Huangpeng, Q.; Duan, X. Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders. Mathematics2024, 12, 1160.
Fu, Y.; Zhu, J.; Li, X.; Han, X.; Tan, W.; Huangpeng, Q.; Duan, X. Research on Group Behavior Modeling and Individual Interaction Modes with Informed Leaders. Mathematics 2024, 12, 1160.
Abstract
{This study investigates the coordinated behaviors and the underlying collective intelligence in biological groups, particularly those led by informed leaders. By establishing new convergence criteria based on real biological group experiments, this research introduces the concept of a volitional term and heterogeneous networks, constructing a coupled force Cucker-Smale model with informed leaders. The inclusion of the informed leader aspect distinctively characterizes the dynamics of leaders and followers, enabling a more accurate representation of biological group behavior. The paper then proposes the Flock Leadership Hierarchy Network (FLH), a model reflecting real biological interactions. By employing time slicing and rolling time windows, the study methodically analyses group behavior stages, using volatility and convergence time as metrics to examine the relationship between group consistency and interactions. Comparative experiments show the FLH network's superior performance. The Kolmogorov-Smirnov test indicates that the FLH network follows a power-law distribution, highlighting the evolutionary intelligence of biological groups. This research offers new perspectives on the evolution of biological groups, contributing to our understanding of both natural and artificial systems, such as animal migration and autonomous drone operations.
Computer Science and Mathematics, Applied Mathematics
Copyright:
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