PAWS：政策驱动的智能体世界模拟数据集
原标题：PAWS: Policy-driven Agentic World Simulation
AI 摘要
新加坡国立大学、清华大学和香港城市大学的研究者发布 PAWS，一个政策驱动的多智能体世界模拟数据集，覆盖 36 个美国金融经济政策事件、12,727 条政策相关新闻和 65,291 条利益相关者行动。每条行动都关联支撑新闻，并用多层事件框架表示，实体归一化后与每日市场收益对齐，支持政策-智能体模拟回放。在 2,522 条分层样本上，AI 与人类评审对交互模式的初始一致率为 89.4%，回放研究还发现高准确率可能掩盖对稀有利益相关者行动的漏检。
正文节选
Abstract Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet datasets for financial multi-agent simulation rarely connect these processes to temporally aligned historical evidence. We introduce PAWS, a Policy-driven Agentic World Simulation dataset covering 36 verified U.S. financial and economic policy episodes, 12,727 policy-linked news records, and 65,291 source-grounded stakeholder actions. Each action is linked to its supporti