Warren William H
Department of Cognitive, Linguistic, and Psychological Sciences, Brown University.
Curr Dir Psychol Sci. 2018 Aug;27(4):232-240. doi: 10.1177/0963721417746743. Epub 2018 Jul 11.
The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals, in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the 'rules' governing these interactions. Based on such results, we build a dynamical model of how a pedestrian aligns their motion with that of a neighbor, and how these binary interactions are combined within a neighborhood in a crowd. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach yields the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.
鸟群、鱼群和人群的芭蕾舞般的运动被认为是在自组织过程中由个体之间的局部相互作用产生的。因此,解释这种集体行为的关键在于理解这些局部相互作用。经过数十年的理论建模,使用虚拟人群的实验以及对真实人群数据的分析,使我们能够解读支配这些相互作用的“规则”。基于这些结果,我们构建了一个动力学模型,该模型描述了行人如何使其运动与邻居的运动保持一致,以及这些二元相互作用如何在人群中的一个邻域内组合。该模型的计算机模拟在全局层面产生连贯运动,并在局部层面重现个体轨迹。这种方法产生了第一个由实验驱动的、自下而上的集体运动模型,为理解日常和紧急情况下更复杂的人群行为模式提供了基础。