Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

The article presents a framework for multi-agent LLMs utilizing subjective graphs to simulate uncertainty in social perception within educational settings. The agents employ individualized graphs to manage peer visibility and communication, updating Gaussian belief states through Bayesian fusion. Evaluated across 12 middle-school classrooms, the framework demonstrated a significant increase in collective ranking error, indicating persistent distortions in perceived academic standing, and outperformed traditional DeGroot configurations in maintaining opinion diversity, highlighting its relevance for practitioners interested in modeling social dynamics in AI systems.