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.
