When Can Digital Personas Reliably Approximate Human Survey Findings?
This study tested digital personas, created by Large Language Models (LLMs) using respondent data, as substitutes for human survey answers. The research found these personas are most reliable for stable attributes like family and household values but perform poorly on subjective topics. Retrieval-augmented contexts improve accuracy, suggesting they are best used for approximating population distributions rather than individual predictions.