Why do LLMs fail when simulating agents with private information?

Most LLM social simulations use a single model to generate all participants — an omniscient perspective fundamentally at odds with how real social interaction works. When evaluated against non-omniscient settings that preserve information asymmetry, LLMs struggle.

The "Is this the real life?" evaluation framework (2024) demonstrates this by comparing omniscient simulation (one LLM controls all parties) against non-omniscient simulation (separate LLM instances with private information). The performance gap is systematic: models that appear socially competent in omniscient mode fail when they must reason under genuine uncertainty about what the other party knows, wants, or intends.

This matters because real social interaction is defined by information asymmetry. In SOTOPIA's scenarios, agents have shared context but private goals — "Your goal is to buy the chair for $80" is visible only to the buyer. The Secret dimension (what agents must hide) directly requires information management that omniscient models bypass entirely.

The implication for persona simulation research is direct. Since Can AI agents learn people better from interviews than surveys?, simulation fidelity appears high. But if that fidelity was measured under omniscient conditions, it overstates real-world applicability. Since Do language models actually build shared understanding in conversation?, the failure under information asymmetry is predictable: models that skip grounding work will fail precisely when grounding is most needed — when parties have genuinely different information states.

Since Why do language models skip the calibration step?, non-omniscient simulation demands the dynamic grounding that LLMs systematically lack.

Source: Social Theory Society