Author ORCID Identifier
Date of Award
Summer 8-8-2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematics and Statistics
First Advisor
Igor Belykh
Second Advisor
Michael Stewart
Third Advisor
Yaroslav Molkov
Fourth Advisor
Vladimir Bondarenko
Abstract
This thesis presents several novel results on the nonlinear and emergent collective dynamics of crowds and populations in complex systems. Though, historically, the list of suspension bridges destabilized by pedestrian collective motion is long, the phenomenon still needs to be fully understood, especially regarding the effect of human-to-human interactions on the structure, and often incorrectly explained using synchronization theory. We present a simple general formula that quantifies the effect of pedestrian effective damping of a suspension bridge and illustrate it by simulating three mathematical models, including one with a strong propensity for synchronization. Despite the subtle effects of gait strategies in determining precise instability thresholds, our results show that average negative damping is always the trigger of pedestrian-induced high-amplitude lateral vibration of suspension bridges. Furthermore, we show that human-to-human interactions of heterogeneous pedestrians can trigger the instability of a bridge more effectively than crowds of identical pedestrians. We will also discuss the role of crowd heterogeneity in possible phase pulling between pedestrians and bridge motion. We also develop a model for the evolution of toxic memes on 4chan and report a significant influence on Twitter’s anti-vaccine conspiracy discourse over a nine-year period. We show that 4chan topics evolve according to an emergent process mathematically similar to classic reinforcement learning methods, tending to maximize the expected toxicity of future discourse. We demonstrate that these topics can invade Twitter and persist in an endemic state corresponding to the associated spreading rate and initial distribution of post rates and coexisting with a higher-traffic regime of dynamics. We discuss the implication of this result for preventing large-scale disinformation campaigns.
DOI
https://doi.org/10.57709/35862724
Recommended Citation
Daley, Kevin, "Emergent Collective Dynamics with Applications in Bridge Engineering and Social Networks." Dissertation, Georgia State University, 2023.
doi: https://doi.org/10.57709/35862724
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