English(EN) Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

将大型语言模型置于经济模拟器中以生成政策

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了一种将大型语言模型 (LLM) 置于动态随机一般均衡 (DSGE) 模拟器中的方法，以生成和预测经济政策。该方法通过将一个经过指令调整的语言模型置于六个 DSGE 模拟器中来测试 LLM 生成的政策是否与经济动态一致。该模型观察经济状态和讨论，选择政策行动，并接收奖励，从而产生一个长期信用分配问题，Proximal Policy Optimization (PPO) 通过学习到的价值函数来解决。 AI

影响这项研究可能导致人工智能系统提供更可靠、更符合经济学原理的政策建议。

排序理由学术论文，详细介绍了将 LLM 置于经济模拟器中的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Aditya Dubey, Namah Gupta, Vinti Agarwal · 2026-10-02 04:00

在 DSGE 模拟器中对大型语言模型进行接地以生成政策和进行预测

arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economic dynamics. We test this by placing an instruction-tuned language model inside s…

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在 DSGE 模拟器中对大型语言模型进行接地以生成政策和进行预测

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