发表机构
National University of Singapore; Tsinghua University; City University of Hong Kong（新加坡国立大学; 清华大学; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PAWS构建了覆盖36个政策事件、12,727条新闻和65,291个行动的历史金融模拟数据集，通过多层事件框架支持政策智能体回放，并揭示高精度可能掩盖罕见行动检测失败的核心挑战。
AI 中文摘要
政策干预通过公共传播、机构决策和利益相关者反应进行传导，然而用于金融多智能体模拟的数据集很少将这些过程与时间对齐的历史证据联系起来。我们提出了PAWS，一个政策驱动的智能体世界模拟数据集，涵盖36个经过验证的美国金融和经济政策事件、12,727条与政策相关的新闻记录以及65,291个基于来源的利益相关者行动。每个行动都与其支持的新闻相关联，并由一个多层事件框架表示，该框架捕获其交互模式、金融行动族和子类型、语义属性以及对外部分类法的条件映射。实体被解析为规范化的组织，行动与每日市场回报背景对齐，以支持政策智能体模拟回放。在2,522个分层行动样本上，独立的人工智能和人类评审员在交互模式上达到了89.4%的初始一致性，分歧随后被裁定。对2008年卖空禁令和2001年十进制化的案例研究，在密集和稀疏新闻设置下均恢复了记录在案的政策时间线及相关市场模式。一项回放研究进一步表明，高准确性可能掩盖对罕见利益相关者行动的检测失败，从而将行动时机和校准确定为核心挑战。PAWS为评估智能体影响力、政策响应级联以及行动结果一致性，在历史依据的金融模拟中提供了可审计的基础。
英文摘要
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 supporting news and represented by a multi-layer event frame capturing its interaction mode, financial-action family and subtype, semantic attributes, and conditional mappings to external taxonomies. Entities are resolved to normalized organizations, and actions are aligned with daily market-return context to support policy-agent simulation replay. On 2,522 stratified action samples, independent AI and human reviewers achieved 89.4% initial agreement on interaction mode, with disagreements subsequently adjudicated. Case studies of the 2008 short-selling ban and 2001 decimalization recover documented policy timelines and associated market patterns across both dense and sparse news settings. A replay study further shows that high accuracy can mask failure to detect rare stakeholder actions, identifying action timing and calibration as central challenges. PAWS provides an auditable substrate for evaluating agent influence, policy-response cascades, and action-outcome alignment in historically grounded financial simulations.
