When pull turns to shove: A continuous-time model for opinion dynamics

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 physics-based methods 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.We are able to extrapolate these nonlinear microscopic dynamics to macroscopic population distributions by using probabilistic functions representing biased environments.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.