LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation

TL;DR

The paper introduces S-Researcher, a platform utilizing large language model agents to automate and scale social science research through simulation, analysis, and reporting, enhancing human-AI collaboration.

Contribution

It develops YuLan-OneSim, a scalable, general, and reliable social simulation system, and operationalizes LLM-based social research paradigms into three reasoning modes.

Findings

Successfully simulated cultural dynamics consistent with Axelrod's theory.

Validated hypotheses on teacher attention against survey data.

Identified cooperation mechanisms in public goods games confirmed by human experiments.

Abstract

Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more efficiently and at greater scale by "siliconizing" both the research process and the participant pool. To build S-Researcher, we first develop YuLan-OneSim, a large-scale social simulation system designed around three core requirements: generality via auto-programming from natural language to executable scenarios, scalability via a distributed architecture supporting up to 100,000 concurrent agents, and reliability via feedback-driven LLM fine-tuning. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete human-AI collaborative research loop in which researchers retain oversight and intervention at every stage. We operationalize LLM simulation research paradigms into three canonical reasoning modes (induction, deduction, and abduction) and validate S-Researcher through systematic case studies: inductive reproduction of cultural dynamics consistent with Axelrod's theory, deductive testing of competing hypotheses on teacher attention validated against survey data, and abductive identification of a cooperation mechanism in public goods games confirmed by human experiments. S-Researcher establishes a new human--AI collaborative paradigm for social science, in which computational simulation augments human researchers to accelerate discovery across the full spectrum of social inquiry.