关税威胁、宏观经济预期与政策沟通策略：基于多智能体系统的实验
Tariff Threats, Macroeconomic Expectations, and Policy Communication Strategies: Experiments Based on a Multi-Agent System
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中文总结 AI 辅助
本研究构建多智能体系统，模拟分析关税威胁等因素对宏观经济预期的影响，发现央行解释可协调信念，为政策沟通探索提供支撑。
中文摘要 AI 辅助
关税威胁可在政策实施前改变家庭信念，但其快速变化的语言难以用传统调查研究。我们构建了一个多智能体系统，将密歇根消费者调查中的300户家庭转化为持久的大语言模型智能体，使其在数个模拟月中接触社交媒体信息。经校准的智能体重现了“解放日”关税公告后人类调查数据中的部分分布与人口统计模式。模拟实验表明，即时性、利率显著性、语义演进、信息复杂度、叙事性及发送者身份共同塑造了通胀与失业预期及其离散程度。开放式响应将这些影响归因于注意力、模糊性、可信度及因果叙事。第二项实验发现，央行解释可协调信念，但其对平均预期的影响取决于信息内容。该框架支持对政策沟通进行严谨探索，需经人类验证而非作为其替代方案。
英文摘要
Tariff threats can move household beliefs before policy is enacted, yet their rapidly changing language is difficult to study with conventional surveys. We build a multi-agent system that turns 300 households from the Michigan Surveys of Consumers into persistent large-language-model agents exposed to social-media information over several simulated months. Calibrated agents reproduce some distributional and demographic patterns in human survey data collected after the announcement of Liberation Day tariffs. Simulated experiments indicate that immediacy, rate salience, semantic progression, message complexity, narrative, and sender identity jointly shape inflation and unemployment expectations and their dispersion. Open-ended responses trace these effects to attention, ambiguity, credibility, and causal narratives. A second experiment finds that central-bank explanations can coordinate beliefs, although their effects on average expectations depend on message content. The framework supports disciplined exploration of policy communication, subject to human validation rather than as a substitute for it.
