Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) increasingly interact with one another in multi-agent systems, from simulations of human discourse to influence operations and fully LLM-driven social platforms.These interactions give rise to new regimes of opinion propagation that are not yet well understood.We investigate whether classical opinion dynamics models, which have long been used to explain how interactions shape collective beliefs in human societies, can capture the behavior of LLM networks.Specifically, we quantify the relative contributions of classical opinion-dynamics mechanisms, including neighbor influence, stubbornness, homophily, and bias, to the evolution of LLM opinions.Among the mechanisms considered, a bias term capturing regression toward a model-and topicdependent opinion emerges as a significant explanatory component, with its inclusion reducing cumulative estimated mean opinion error by up to 88%.We additionally observe similar trends across the evaluated model families, discussion topics, and network structures.
Institution: The University of Texas at Austin
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7165188367