Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents

2026-09-18

AI Summary

该研究通过引入贝叶斯编年代理（BCA）来解决LLM代理在社会模拟中无法控制和验证其观点开放性的问题，使用贝叶斯方法更新观点概率，实现可控的观点动态。

Abstract

LLM agents in social simulation revise their opinions implicitly, in context: how open an agent is to persuasion can neither be specified nor verified, and collective outcomes inherit the model's training prior. We introduce Bayesian Chronicle Agents (BCA), a minimal belief layer separating \emph{what} an agent believes from \emph{how} it speaks. Each stance is a probability, updated by one Bayesian step per utterance heard. A single prior-strength parameter $κ$ encodes stubbornness, modeled after its role in Friedkin--Johnsen (FJ) opinion dynamics. We then sweep this parameter to yield three canonical regimes of opinion dynamics on demand (consensus, persistent disagreement, committed-minority influence), with persistent disagreement matching the FJ closed-form fixed points at $R^2\!=\!0.93$--$0.99$. We further show that prescribed $κ$ remains recoverable after the language round-trip, with perfect rank-order recovery across all four models. Explicit belief also makes simulation auditable: the layer surfaces systematic per-model stance biases that end-to-end simulation would silently absorb.

Problem

• Bayesian
• opinion dynamics
• LLM agents
• social simulation
• persuasion

Innovation

• Bayesian Chronicle Agents
• opinion dynamics
• stubbornness parameter
• auditable simulation
• Friedkin--Johnsen model