{"ID":2862695,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26080","arxiv_id":"2509.26080","title":"Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research","abstract":"Large Language Models (LLMs) are being increasingly used as synthetic agents in social science, in applications ranging from augmenting survey responses to powering multi-agent simulations. This paper outlines cautions that should be taken when interpreting LLM outputs and proposes a pragmatic reframing for the social sciences in which LLMs are used as high-capacity pattern matchers for quasi-predictive interpolation under explicit scope conditions and not as substitutes for probabilistic inference. Practical guardrails such as independent draws, preregistered human baselines, reliability-aware validation, and subgroup calibration, are introduced so that researchers may engage in useful prototyping and forecasting while avoiding category errors.","short_abstract":"Large Language Models (LLMs) are being increasingly used as synthetic agents in social science, in applications ranging from augmenting survey responses to powering multi-agent simulations. This paper outlines cautions that should be taken when interpreting LLM outputs and proposes a pragmatic reframing for the social...","url_abs":"https://arxiv.org/abs/2509.26080","url_pdf":"https://arxiv.org/pdf/2509.26080v2","authors":"[\"Emma Rose Madden\"]","published":"2025-09-30T10:53:54Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"stat.AP\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}