Sattari Sulimon, Basak Udoy S, James Ryan G, Perrin Louis W, Crutchfield James P, Komatsuzaki Tamiki
Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University Kita 20, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0020, Japan.
Pabna University of Science and Technology, Pabna 6600, Bangladesh.
Sci Adv. 2022 Feb 11;8(6):eabj1720. doi: 10.1126/sciadv.abj1720. Epub 2022 Feb 9.
Pairwise interactions are fundamental drivers of collective behavior-responsible for group cohesion. The abiding question is how each individual influences the collective. However, time-delayed mutual information and transfer entropy, commonly used to quantify mutual influence in aggregated individuals, can result in misleading interpretations. Here, we show that these information measures have substantial pitfalls in measuring information flow between agents from their trajectories. We decompose the information measures into three distinct modes of information flow to expose the role of individual and group memory in collective behavior. It is found that decomposed information modes between a single pair of agents reveal the nature of mutual influence involving many-body nonadditive interactions without conditioning on additional agents. The pairwise decomposed modes of information flow facilitate an improved diagnosis of mutual influence in collectives.
成对相互作用是集体行为的基本驱动因素——负责群体凝聚力。一直存在的问题是每个个体如何影响集体。然而,常用于量化聚集个体间相互影响的时间延迟互信息和转移熵可能会导致误导性的解释。在这里,我们表明这些信息度量在从个体轨迹测量主体间的信息流时存在重大缺陷。我们将信息度量分解为三种不同的信息流模式,以揭示个体和群体记忆在集体行为中的作用。研究发现,一对主体之间分解后的信息流模式揭示了涉及多体非加性相互作用的相互影响的本质,而无需以其他主体为条件。成对分解的信息流模式有助于改进对集体中相互影响的诊断。