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Previous research has suggested that some positions in human crowds are more influential than others. The present study aims to manipulate the influence networks in real human crowds by specifying the causal relationship among some pedestrians. We strategically placed covert or explicit leaders (confederates) in a group of walking pedestrians, instructed them to change walking direction (heading) on a signal, and tested their influence on collective motion. We reconstructed visual influence networks from video data and analyzed the effect of these leaders on the movements of other pedestrians. Our results suggest that both covert and explicit leaders in influential positions can steer and split a crowd, but explicit leaders change the network topology and are significantly more influential than their covert counterparts. The results have potential applications to directing emergency evacuations. 

Collective motion in human crowds has been understood as a self-organizing phenomenon that is generated from local visual interactions between neighboring pedestrians. To analyze these interactions, we introduce an approach that estimates local influences in observational data on moving human crowds and represents them as spatially-embedded dynamic networks (visual influence networks). We analyzed data from a human “swarm” experiment (N= 10, 16, 20) in which participants were instructed to walk about the tracking area while staying together as a group. We reconstructed the network every 0.5 seconds using Time-Dependent Delayed Correlation (TDDC). Using novel network measures of local and global leadership ('direct influence' and 'branching influence'), we find that both measures strongly depend on an individual’s spatial position within the group, yielding similar but distinctive leadership gradients from the front to the back. There was also a strong linear relationship between individual influence and front-back position in the crowd. The results reveal that influence is concentrated in specific positions in a crowd, a fact that could be exploited by individuals seeking to lead collective crowd motion. 

Conference Abstract 

A ubiquitous type of collective behavior and decision-making is the coordinated motion of bird flocks, fish schools, and human crowds. Collective decisions to move in the same direction, turn right or left, or split into subgroups arise in a self-organized fashion from local interactions between individuals without central plans or designated leaders. Strikingly similar phenomena of consensus (collective motion), clustering (subgroup formation), and bipolarization (splitting into extreme groups) are also observed in opinion formation. As we developed models of crowd dynamics and analyzed crowd networks, we found ourselves going down the same path as models of opinion dynamics in social networks. In this article, we draw out the parallels between human crowds and social networks. We show that models of crowd dynamics and opinion dynamics have a similar mathematical form and generate analogous phenomena in multiagent simulations. We suggest that they can be unified by a common collective dynamics, which may be extended to other psychological collectives. Models of collective dynamics thus offer a means to account for collective behavior and collective decisions without appealing to a priori mental structures.