Non-Bayesian Social Learning for Modeling Interacting Large Language Model Agents

Non-Bayesian social learning (NBSL) is a framework in distributed inference that describes how agents in a network combine local observations and their neighbors’ beliefs to infer a hidden state. While the framework has been extensively analyzed in theory, its role as a model of belief formation in realistic multi-agent systems remains under-explored. In this work, we investigate whether NBSL can capture the belief dynamics of interacting large language model (LLM) agents. We design controlled experiments in which LLM agents revise their beliefs sequentially based on local evidence and exchanges with their neighbors. Then, we compare their belief trajectories to those predicted by NBSL. Our results provide the first empirical evaluations of NBSL on modern AI collectives, and show that LLM networks can exhibit belief evolution patterns that closely follow NBSL dynamics.