Simulating Opinion Dynamics with Networks of LLM -based Agents (2024)

Abstract: 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.

## introduction understanding how individuals change their opinions as a function of social influences is critical across multiple domains, from public health campaigns, conflict mediation, to misinformation mitigation @xcite @xcite @xcite . accurate models of these dynamics would allow us to forecast future trends, such as potential opinion polarization, but also to devise targeted interventions to alleviate negative impacts. agent-based models (abms) are a cornerstone approach to opinion dynamics (gilbert and terna, 2000; smaldino, 2023; lorenz et al., 2021; chuang 1 code and data are publicly available at https://gith ub.com/yunshiuan/llm-agent-opinion-dynam ics there is no snow this winter here in canada. ## results agents converge towards the inherent bias in the llm. confirmation bias leads to opinion fragmentation. introducing confirmation bias in the prompt leads to less ultimate consensus (i.e., greater diversity d) across llm agents. as shown in ## conclusion this study has explored the use large language models (llms) for understanding opinion dynamics in groups of simulated agents communicating via natural language. in contrast to more traditional abms, llms can interpret and produce natural language, can role-play differing personas, and can simulate human-like linguistic communication. we therefore considered whether groups of interacting llm agents could provide a basis for simulating opinion dynamics comparable to those studied with classical abms. our findings confirm the potential of llms in opinion dynamics simulations but also reveal limitations, particularly their tendency to align with factual information regardless of their personas, which restricts their role-play effectiveness for individuals with fact-resistant beliefs like climate change denial. significant efforts have been made to prevent llms from exhibiting harmful biases. however, for simulating critical undesired social phenomena (e.g., misinformation, polarization), it is crucial to have simulated agents accurately reflect the breadth of human behavior and belief, even those that are maladaptive. our study suggests that prompting alone may be insufficient for llm agents to fully replicate the diverse viewpoints. this leads us to a potential future direction: fine-tuning llm agents with actual human discourse data. such an approach could lead to more accurate models of human belief dynamics.