  Accurately simulating human opinion dynamics is crucial for understanding a
variety of societal phenomena, including polarization and the spread of
misinformation. However, the agent-based models (ABMs) commonly used for such
simulations often over-simplify human behavior. We propose a new approach to
simulating opinion dynamics based on populations of Large Language Models
(LLMs). Our findings reveal a strong inherent bias in LLM agents towards
producing accurate information, leading simulated agents to consensus in line
with scientific reality. This bias limits their utility for understanding
resistance to consensus views on issues like climate change. After inducing
confirmation bias through prompt engineering, however, we observed opinion
fragmentation in line with existing agent-based modeling and opinion dynamics
research. These insights highlight the promise and limitations of LLM agents in
this domain and suggest a path forward: refining LLMs with real-world discourse
to better simulate the evolution of human beliefs.
