AI 中文总结
针对社会科学研究对象的独特性及现有系统缺陷，AgentSociety 2集成研究环境结合大语言模型代理的两个角色，支持端到端工作流程，通过多领域研究展示其能力，为计算社会科学提供可控基础设施。
AI 中文摘要
人工智能科学家系统开始使部分科学研究自动化，但社会科学面临独特挑战，其研究对象是涉及参与者、互动情境、干预和结果的综合社会过程。现有系统存在缺陷，研究工作流程与模拟社会脱节。本文介绍了AgentSociety 2，一个用于可执行社会科学的集成研究环境。它在同一运行时结合了大语言模型代理的两个角色：人工智能社会科学家和硅参与者。这种双角色设计将假设转化为可审计的代理行为等，支持端到端工作流程。通过七个说明性研究展示了其能力，为下一代计算社会科学提供了人在回路且可控的基础设施。
英文摘要
AI scientist systems are beginning to automate parts of scientific research, but social science poses a distinct challenge: its objects of inquiry are not merely datasets or laboratory protocols, but integrated social processes involving situated participants, interaction contexts, interventions, and outcomes. Yet a critical link is missing: existing systems either assist isolated research tasks or simulate agents as experimental subjects, leaving the research workflow and simulated society decoupled. Here we introduce AgentSociety 2, an Integrated Research Environment for executable social science. It couples two roles of LLM agents in the same runtime: AI social scientists that coordinate literature grounding, hypothesis generation, experiment design, simulation execution, result interpretation, and manuscript drafting; and silicon participants that generate behavioral responses within configurable social environments. This dual-role design turns hypotheses into auditable agent behaviors, environment rules, interventions, and measurements, thereby supporting an end-to-end workflow. Across seven illustrative studies spanning micro-level social-science laboratory experiments, meso-level dynamics in social media, and macro-level urban scenarios, we demonstrate its capacity to support diverse disciplinary questions, reproduce major qualitative patterns from prior studies, identify informative deviations, and enable large-scale simulations through optimized agent-environment interactions. By preserving human researchers' high-level agency while delegating procedural orchestration to agentic systems, it provides a human-in-the-loop and controllable infrastructure for next-generation computational social science, with broader applications in scalable computational social experimentation and AI-enabled social governance platforms.
