Gonzalez Ivan, Tisdell Jack, Choksi Rustum, Nave Jean-Christophe
Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada.
R Soc Open Sci. 2024 Jun 5;11(6):231537. doi: 10.1098/rsos.231537. eCollection 2024 Jun.
This article addresses how diverse collective behaviours arise from simple and realistic decisions made entirely at the level of each agent's personal space in the sense of the Voronoi diagram. We present a discrete-time model in two dimensions in which individual agents are aware of their local Voronoi environment and may seek static target locations. In particular, agents only communicate directly with their Voronoi neighbours and make decisions based on the geometry of their own Voronoi cells. With two effective control parameters, it is shown numerically to capture a wide range of collective behaviours in different scenarios. Further, we show that the Voronoi topology facilitates the computation of several novel observables for quantifying discrete collective behaviours. These observables are applicable to all agent-based models and to empirical data.
本文探讨了在沃罗诺伊图意义上,完全基于每个智能体个人空间层面做出的简单且现实的决策如何产生多样的集体行为。我们提出了一个二维离散时间模型,其中个体智能体了解其局部沃罗诺伊环境,并可能寻找静态目标位置。特别地,智能体仅与其沃罗诺伊邻居直接通信,并基于自身沃罗诺伊单元的几何形状做出决策。通过两个有效的控制参数,数值结果表明该模型能够捕捉不同场景下的广泛集体行为。此外,我们表明沃罗诺伊拓扑有助于计算几个用于量化离散集体行为的新型可观测量。这些可观测量适用于所有基于智能体的模型以及实证数据。