# Opinion dynamics and mutual influence with LLM agents through dialog simulation

arXiv: https://arxiv.org/abs/2602.12583 · HTML: https://mlsanity.com/paper/2602.12583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2026.

## Built on by (most cited first, 4 of 4)

- [Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks](https://mlsanity.com/paper/2603.15809/) (2026)
- [Hidden Anchors in Multi-Agent LLM Deliberation](https://mlsanity.com/paper/2606.19494/) (2026)
- [Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?](https://mlsanity.com/paper/2605.25929/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)

## Abstract (arXiv)

A fundamental challenge in opinion dynamics research is the scarcity of real-world longitudinal opinion data, which complicates the validation of theoretical models. To address this, we propose a novel simulation framework using large language model (LLM) agents in structured multi-round dialogs. Each agent's dialog history is iteratively updated with its own previously stated opinions and those of others analogous to the classical DeGroot model. Furthermore, by retaining each agent's initial opinion throughout the dialog, we simulate anchoring effects consistent with the Friedkin-Johnsen model of opinion dynamics. Our framework thus bridges classical opinion dynamics models and modern multi-agent LLM systems, providing a scalable tool for simulating and analyzing opinion formation when real-world data is limited or inaccessible.