Dynamical modeling aims to capture the essential mechanics at work in real-world systems while remaining tractable enough to yield mathematical insights for predictions and interventions. The work presented here first takes this approach to the system of political ideology and influence, establishing a model for the continuous-time evolution of individual and population-level ideological distributions. This model strikes a middle ground of complexity, eschewing the normal network- or agent-based approach while maintaining significantly more nonlinear realism than statistical-physics models. Second, we explore an under-studied and formerly ill-defined technical roadblock of that model and establish a technique for properly dealing with other such stochastic systems. Finally we examine a nonlinear potential-coupled particle system with unique self-organizing behavior.

Creator

• Sabin-Miller, David Webster

DOI

• https://doi.org/10.21985/n2-yfyk-kf76

Subject

• Applied mathematics

Language

• en

Alternate Identifier

• http://dissertations.umi.com/northwestern:16340
• etdadmin_upload_944503

Keyword

• Sociophysics
• Opinion Dynamics
• Dynamical Systems
• Stochastics
• Modeling
• Coupled Particles

Date created

• 2022-01-01

Resource type

• Dissertation

Rights statement

• In Copyright