Self-Organization and Collective Decision-Making in Human Crowds

鸟群、鱼群和人类群体的集体运动是生物系统中自组织行为的主要例子。这个项目将调查人群中个体之间触发集体运动的局部互动，以及同样的机制是否会产生集体决策，比如跟随哪个“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，目的是解释和预测真实环境中的人群动态。一个互动的人群模拟器将发布在网络上。这项研究具有许多社会效益，包括应用于疏散规划、安全建筑设计、实时监测和预测人群灾害，以及设计社交机器人。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，对于NSF的“大创意”之一--人类技术前沿的未来工作，以及身临其境的社交媒体的设计，都具有重要意义。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，在实验研究方面以及虚拟现实技术和计算建模方法的使用方面，为培养下一代科学家做出贡献。人们普遍认为，集体行为来自个人之间的局部互动。因此，解释集体运动的关键在于理解支配这些互动的接触规则以及它们所操作的互动所在的社区。在数学生物学、计算机动画、物理学和机器人等截然不同的领域，有许多不同的集体运动和集体决策模型。然而，其中很少有基于关于实际管理局部相互作用的规则以及它们如何产生集体运动的全球模式的实验证据。这个项目测试竞争模型是否可以解释(A)集体运动的开始和传播，自组织的症结，以及(B)群体的集体决策，例如向右转还是向左转，或者跟随哪个子群。特别是，这项研究将确定领导力在集体决策中的作用，以及具有战略地位的领导者是否能够控制人群的运动。研究人员将使用以下实验的组合来回答这些问题：1)人类参与者行走在虚拟人群中，以破译本地招募和决策规则；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人群数据，以确定人群中的因果网络；以及4)使用钉住控制，使用大规模运动捕捉技术，预测真实人群实验中领导者(邦联)的影响。该项目的结果将是一个基于经验的模型，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，朝着集体行为的一般动力学理论迈出了重要的一步。整合组织系统部门的行为系统集群参与了该奖项的共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.