When Pull Turns To Shove: A Mathematical Model For Opinion Dynamics
Year: 2020
Type: preprint
Abstract: Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use differential equations to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. The framework we put forward can reproduce real-world political distributions and experimentally observed dynamics, and is amenable to further refinement as more data becomes available.
Source: arXiv (Cornell University)
Authors: David Sabin-Miller, Daniel M. Abrams
Institution: Northwestern University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3025273162