Hoffman Marion, Thrash Tyler, Hölscher Christoph, Kapadia Mubbasir, Schinazi Victor R
Institute for Advanced Study in Toulouse, Toulouse School of Economics, University Toulouse Capitole, Toulouse, France.
Department of Humanities, Social and Political Sciences, ETH Zürich, Zurich, Switzerland.
Sci Rep. 2025 May 30;15(1):19086. doi: 10.1038/s41598-025-02460-7.
Understanding crowd behavior is critical for designing buildings and public spaces with efficient circulation. However, the interplay of social and spatial contexts makes this endeavor challenging. This paper examines scenarios in which crowds perform a search task with time constraints, akin to individuals shopping or officers searching a crime area. We formulate and test two sets of hypotheses defined at the crowd and individual levels using desktop VR experiments. We conducted four experimental sessions that employed different social incentives (collaborative versus competitive) with a total of 140 participants, using a mixed factorial design where each individual participated in 12 trials. We found that competitive incentives produced higher levels of crowd aggregation than collaborative incentives. In addition, individuals were more likely to be influenced by others' behaviors in the collaborative compared to the competitive condition. Notably, these social signals were conveyed among participants without any verbal communication. We also developed a novel graph theoretic measure, "search attractiveness," that accurately predicts space occupation during a search task. This paper highlights the roles of social and spatial contexts in understanding occupation and aggregation.
理解人群行为对于设计具有高效流通性的建筑和公共空间至关重要。然而，社会和空间背景的相互作用使得这项工作具有挑战性。本文研究了人群在有时间限制的情况下执行搜索任务的场景，类似于个人购物或警察搜索犯罪区域。我们使用桌面虚拟现实实验，制定并测试了两组在人群和个体层面定义的假设。我们进行了四个实验环节，采用不同的社会激励方式（合作与竞争），共有140名参与者，采用混合因子设计，每个个体参与12次试验。我们发现，与合作激励相比，竞争激励产生了更高水平的人群聚集。此外，与竞争条件相比，个体在合作条件下更容易受到他人行为的影响。值得注意的是，这些社会信号在参与者之间无需任何言语交流即可传达。我们还开发了一种新颖的图论测量方法“搜索吸引力”，它可以准确预测搜索任务期间的空间占用情况。本文强调了社会和空间背景在理解占用和聚集方面的作用。
