Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents

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 $\kappa$ 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 $\kappa$ 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.

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)

arXiv:2609.21997 [cs.MA]

(or arXiv:2609.21997v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2609.21997 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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