多智能体辩论式LLM中鹰派-鸽派隐含信念建模用于货币政策决策分类
Modeling Hawkish-Dovish Latent Beliefs in Multi-Agent Debate-Based LLMs for Monetary Policy Decision Classification
AI总结：
本文提出多智能体辩论式LLM框架，通过建模鹰派与鸽派隐含信念提升货币政策预测准确性，优于传统LLM基线。
摘要：
准确预测央行政策决策，特别是美联储公开市场委员会（FOMC）的决策，在经济不确定性加剧的背景下变得尤为重要。尽管先前研究利用货币政策文本预测利率变化，但大多数方法依赖静态分类模型，忽略了政策制定的审议性质。本文提出了一种新颖的框架，通过建模多个大型语言模型（LLMs）作为交互智能体，结构上模仿FOMC的集体决策过程。每个智能体从不同的初始信念开始，并基于定性政策文本和定量宏观经济指标生成预测。通过迭代轮次，智能体通过观察其他智能体的输出修订预测，模拟审议和共识形成。为提高可解释性，我们引入一个表示每个智能体隐含信念（例如鹰派或鸽派）的隐变量，并理论证明该信念如何调解输入信息的感知和交互动态。实证结果表明，这种辩论式方法在预测准确性上显著优于标准LLM基线。此外，显式建模信念提供了关于个体视角和社会影响如何塑造集体政策预测的见解。
英文摘要：
Accurately forecasting central bank policy decisions, particularly those of the Federal Open Market Committee(FOMC) has become increasingly important amid heightened economic uncertainty. While prior studies have used monetary policy texts to predict rate changes, most rely on static classification models that overlook the deliberative nature of policymaking. This study proposes a novel framework that structurally imitates the FOMC's collective decision-making process by modeling multiple large language models(LLMs) as interacting agents. Each agent begins with a distinct initial belief and produces a prediction based on both qualitative policy texts and quantitative macroeconomic indicators. Through iterative rounds, agents revise their predictions by observing the outputs of others, simulating deliberation and consensus formation. To enhance interpretability, we introduce a latent variable representing each agent's underlying belief(e.g., hawkish or dovish), and we theoretically demonstrate how this belief mediates the perception of input information and interaction dynamics. Empirical results show that this debate-based approach significantly outperforms standard LLMs-based baselines in prediction accuracy. Furthermore, the explicit modeling of beliefs provides insights into how individual perspectives and social influence shape collective policy forecasts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
