User Simulation

User simulation studies how to represent people’s goals, preferences, knowledge, and behavior in interactions with AI agents, from tutoring and shopping to healthcare and web use. Common approaches include persona prompting, models grounded in real interaction logs, explicit cognitive or behavioral profiles, and learned simulators trained with supervised or reinforcement learning. Researchers evaluate these simulators against human behavior using measures of conversational and decision fidelity, behavioral diversity, long-term trajectories, and effects on agent performance. A central challenge is closing the sim-to-real gap: fluent or population-level outputs can still misrepresent individuals, omit difficult behaviors, or bias evaluations through overly cooperative simulated users.