Self-Organization and Collective Decision-Making in Human Crowds

鸟群、鱼群和人群的集体运动是生物系统中自组织行为的最好例子。 该项目将研究引发集体运动的人群中个体之间的局部互动，以及是否相同的机制产生集体决策，例如跟随哪个“领导者”。 研究结果将有助于建立行人和人群行为的数学模型，目的是解释和预测现实环境中的人群动态。 一个互动的人群模拟器将张贴在网上。 该研究具有许多社会效益，包括疏散规划，安全建筑设计，人群灾难的实时监测和预测以及社交机器人的设计。研究结果也将有助于盲人疏散行为和导航系统的研究。此外，对虚拟环境中人类行为的理解对人类技术前沿的未来工作（NSF的“大创意”之一）以及沉浸式社交媒体的设计都有影响。 该项目还将通过培训本科生和研究生，特别是来自代表性不足群体的学生，进行实验研究以及使用虚拟现实技术和计算建模方法，为教育下一代科学家做出贡献。一般认为，集体行为产生于个体之间的局部互动。 因此，解释集体运动的关键在于理解支配这些相互作用的参与规则以及它们所作用的相互作用邻域。在数学生物学、计算机动画、物理学和机器人学等完全不同的领域，有许多不同的集体运动和集体决策模型。然而，他们中很少有人是基于实验证据的规则，实际上控制当地的相互作用，以及他们如何产生集体运动的全球模式。 这个项目测试了竞争模型是否可以解释（a）集体运动的开始和传播，自组织的关键，以及（B）群体的集体决策，例如向左还是向右，或者跟随哪个小组。 特别是，这项研究将确定领导在集体决策中的作用，以及战略定位的领导者是否可以控制人群的运动。 研究人员将通过以下实验的组合来回答这些问题：1）人类参与者在虚拟人群中行走，以破译当地的招募和决策规则; 2）基于代理的数据模拟，以测试竞争模型; 3）使用网络重建方法分析真实的人群数据，以确定人群中的因果网络;以及4）使用牵制控制来预测领导者（同盟者）在真实的人群实验中的影响，使用大规模运动捕捉技术。 这个项目的结果将是一个基于理论的模型，该模型解释了集体运动的自组织和人类群体中集体决策的出现，这是向集体行为的一般动力学理论迈出的重要一步。综合有机体系统部的行为系统集群参加了联合该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The collective motion of bird flocks, fish schools, and human crowds are prime examples of self-organized behavior in biological systems. This project will investigate the local interactions between individuals in a crowd that trigger collective motion and whether the same mechanisms yield collective decisions, such as which 'leaders' to follow. The results will contribute to a mathematical model of pedestrian and crowd behavior, with the aim of explaining and predicting crowd dynamics in real-world environments. An interactive crowd simulator will be posted on the Web. The research has many societal benefits, including applications to evacuation planning, safe building design, real-time monitoring and forecasting of crowd disasters, and the design of social robots. The results will also contribute to research on evacuation behavior and navigation systems for the blind. Moreover, an understanding of human behavior in virtual environments has implications for the Future of Work at the Human-Technology Frontier, one of NSF's 'Big Ideas,' as well as for the design of immersive social media. The project will also contribute to educating the next generation of scientists by training undergraduate and graduate students, particularly students from underrepresented groups, in experimental research and the use of virtual reality technology and computational modeling methods.It is generally believed that collective behavior emerges from local interactions between individuals. Thus, the key to explaining collective motion lies in understanding the rules of engagement that govern these interactions and the neighborhood of interaction over which they operate. There are many different models of collective motion and collective decision-making in fields as disparate as mathematical biology, computer animation, physics, and robotics. However, few of them are based on experimental evidence about the rules that actually govern local interactions and how they generate global patterns of collective motion. This project tests whether competing models can explain (a) the onset and propagation of collective motion, the crux of self-organization, and (b) collective decision-making by a crowd, such as whether to turn right or left or which subgroup to follow. In particular, the research will determine the role of leadership in collective decisions and whether strategically positioned leaders can control a crowd's motion. Researchers will answer these questions using a combination of experiments on 1) a human participant walking in a virtual crowd to decipher the local recruitment and decision rules; 2) agent-based simulations of the data to test competing models; 3) analysis of real crowd data using methods of network reconstruction to determine the causal networks in a crowd; and 4) use of pinning control to predict the influence of leaders (confederates) in real crowd experiments, using large-scale motion capture techniques. The result of this project will be an empirically-grounded model that accounts for the self-organization of collective motion and the emergence of collective decisions in human crowds, a significant step toward a general kinetic theory of collective behavior.The Behavioral Systems Cluster in the Division of Integrative Organismal Systems participated in co-funding this award.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.