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1. Local interactions underlying collective motion in human crowds

   https://doi.org/10.1098/rspb.2018.0611  

   Rio, Kevin W.; Dachner, Gregory C.; Warren, William H. (May 2018, Proceedings of the Royal Society B: Biological Sciences)

   null (Ed.)

   It is commonly believed that global patterns of motion in flocks, schools and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behaviour thus lies in deciphering these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we observed that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbour. Here we investigate the neighbourhood of interaction in a crowd: which neighbours influence a pedestrian's behaviour, how this depends on neighbour position, and how the influences of multiple neighbours are combined. In three experiments, a participant walked in a virtual crowd whose speed and heading were manipulated. We find that neighbour influence is linearly combined and decreases with distance, but not with lateral position (eccentricity). We model the neighbourhood as (i) a circularly symmetric region with (ii) a weighted average of neighbours, (iii) a uni-directional influence, and (iv) weights that decay exponentially to zero by 5 m. The model reproduces the experimental data and predicts individual trajectories in observational data on a human ‘swarm’. The results yield the first bottom-up model of collective crowd motion. 

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2. The Influence of Explicit and Covert Leaders on Human Crowd Motion

   https://doi.org/10.1051/epjconf/202533404010  

   Yoshida, Kei; Taylor, Hector; Warren, William H (January 2025, EPJ Web of Conferences: Traffic and Granular Flow 2024)

   Nicolas, A; Bain, N; Douin, A; Ramos, O; Furno, A (Ed.)

   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. 

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3. Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization

   https://doi.org/10.1177/17456916231186406  

   Warren, William_H; Falandays, J_Benjamin; Yoshida, Kei; Wirth, Trenton_D; Free, Brian_A (August 2023, Perspectives on Psychological Science)

   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. 

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4. Simulating and quantifying the influence of covert and explicit leader on human crowd motion, 12th International Conference on Pedestrian and Evacuation Dynamics

   Yoshida, K; Feldmann, S; Warren, WH (September 2025, Czech Technical University in Prague; https://www.ped25.cz/program)

   We previously reported an experiment in which covert or explicit leaders (confederates) were placed in a group of walking pedestrians in order to test leader influence on human crowd motion. Here we simulate the participant trajectories with variants of an empirical pedestrian model, treating the covert leaders’ motion as input, and test model agreement with the experimental data. We are currently using reconstructed influence networks [2] to modify the model weights in order to simulate the influence of explicit leaders. The results help us to understand how leader influence propagates via local interactions in real human crowds. 

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5. Is the neighborhood of interaction in human crowds metric, topological, or visual?

   https://doi.org/10.1093/pnasnexus/pgad118  

   Wirth, Trenton D; Dachner, Gregory C; Rio, Kevin W; Warren, William H (May 2023, PNAS Nexus)

   Borge-Holthoefer, Javier (Ed.)

   Abstract Global patterns of collective motion in bird flocks, fish schools, and human crowds are thought to emerge from local interactions within a neighborhood of interaction, the zone in which an individual is influenced by their neighbors. Both metric and topological neighborhoods have been reported in animal groups, but this question has not been addressed for human crowds. The answer has important implications for modeling crowd behavior and predicting crowd disasters such as jams, crushes, and stampedes. In a metric neighborhood, an individual is influenced by all neighbors within a fixed radius, whereas in a topological neighborhood, an individual is influenced by a fixed number of nearest neighbors, regardless of their physical distance. A recently proposed alternative is a visual neighborhood, in which an individual is influenced by the optical motions of all visible neighbors. We test these hypotheses experimentally by asking participants to walk in real and virtual crowds and manipulating the crowd's density. Our results rule out a topological neighborhood, are approximated by a metric neighborhood, but are best explained by a visual neighborhood that has elements of both. We conclude that the neighborhood of interaction in human crowds follows naturally from the laws of optics and suggest that previously observed “topological” and “metric” interactions might be a consequence of the visual neighborhood. 

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