Agent-based models of “flocking” and “schooling” have shown that a weighted average of neighbor velocities, with weights that decay gradually with distance, yields emergent collective motion. Weighted averaging thus offers a potential mechanism of self-organization that recruits an increasing, but self-limiting, number of individuals into collective motion. Previously, we identified and modeled such a ‘soft metric’ neighborhood of interaction in human crowds that decays exponentially to zero at a distance of 4–5 m. Here we investigate the limits of weighted averaging in humans and find that it is surprisingly robust: pedestrians align with the mean heading direction in their neighborhood, despite high levels of noise and diverging motions in the crowd, as predicted by the model. In three Virtual Reality experiments, participants were immersed in a crowd of virtual humans in a mobile head-mounted display and were instructed to walk with the crowd. By perturbing the heading (walking direction) of virtual neighbors and measuring the participant’s trajectory, we probed the limits of weighted averaging. 1) In the “Noisy Neighbors” experiment, the neighbor headings were randomized (range 0–90°) about the crowd’s mean direction (±10° or ±20°, left or right); 2) in the “Splitting Crowd” experiment, the crowd split into two groups (heading difference = 10–40°) and the proportion of the crowd in one group was varied (50–84%); 3) in the “Coherent Subgroup” experiment, a perturbed subgroup varied in its coherence (heading SD = 0–20°) about a mean direction (±10° or ±20°) within a noisy crowd (heading range = 180°), and the proportion of the crowd in the subgroup was varied. In each scenario, the results were predicted by the weighted averaging model, and attraction strength (turning rate) increased with the participant’s deviation from the mean heading direction, not with group coherence. However, the results indicate that humans ignore highly discrepant headings (45–90°). These findings reveal that weighted averaging in humans is highly robust and generates a common heading direction that acts as a positive feedback to recruit more individuals into collective motion, in a self-reinforcing cascade. Therefore, this “soft” metric neighborhood serves as a mechanism of self-organization in human crowds.
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Human Crowds as Social Networks: Collective Dynamics of Consensus and Polarization
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.
more » « less- Award ID(s):
- 1849446
- NSF-PAR ID:
- 10437567
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Perspectives on Psychological Science
- Volume:
- 19
- Issue:
- 2
- ISSN:
- 1745-6916
- Format(s):
- Medium: X Size: p. 522-537
- Size(s):
- p. 522-537
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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