Influence in Motion: Scaling From Individual Interactions to Crowd DynamicsdissertationsYoshida, KeicreatorWarren, William H.AdvisorFeldmanHall, OrielReaderdi Bernardo, MarioReaderBrown University. Department of Cognitive, Linguistic, and Psychological Sciencessponsor202616, 164 p.born digitalThesis (Ph. D.)--Brown University, 2026thesesCollective motion in animal groups and human crowds is widely understood as a self-organizing phenomenon emerging from local interactions, but the mechanisms by which individual behavior scales up to coordinated group motion remain unclear. This dissertation investigates pedestrian interaction mechanisms underlying collective motion in human crowds, focusing on intermediate scales that link microscopic and macroscopic descriptions of behavior, including pairwise leader-follower relations, information integration across neighbors, and group-level influence structures. This work combines theory-driven and data-driven approaches. First, I designed and conducted two crowd experiments in which pedestrian interactions were manipulated through confederates. When participants were unaware of the confederates (covert), they could steer but not split the crowd, but when participants were instructed to follow confederates holding flags (explicit), both steering and splitting were observed. Second, I developed an integrated framework for the weighting mechanism underlying pedestrian following, building on two existing agent-based models, in order to systematically test hypotheses about the local interaction rules. The findings provide evidence for a weighted-averaging account of local interactions, while also showing that pedestrian following cannot be fully explained by fixed rules alone, and that attention is necessary to explain collective behavior in the presence of explicit leaders. Third, I implemented and evaluated three causal inference methods for reconstructing influence networks from empirical data, with the goal of identifying the strength of direct influences. Time-dependent delayed directional correlation was found to be the most effective method in capturing the expected influence structure. Although the current implementation of causation entropy (CSE) did not yield meaningful results, it highlighted the complexity of pedestrian interactions and the need for further methodological refinement. Taken together, these findings contribute to a multi-scale understanding of human collective motion by linking individual interactions to emergent crowd dynamics. They also make methodological contributions by providing a unified framework for evaluating hypotheses about weighting mechanisms, and by extending methods for causal inference and network reconstruction in human crowd research, including the first application of CSE in this context.agent-based modelsCollective behaviorNetwork sciencePedestrian dynamicsEntropy (Information theory)EnglishRPB20260516