Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting
Read the original on arXiv AI →The paper explores how large language models (LLMs) can be grounded in dynamic stochastic general equilibrium (DSGE) simulators to generate and evaluate economic policy actions. By embedding an instruction‑tuned LLM within six Snowdrop‑backed DSGE models, the authors create a long‑horizon credit‑assignment problem where the model observes economic states, selects bounded policy actions, and receives simulated outcomes and rewards. They compare reinforcement learning methods—PPO, which uses a learned value function to propagate delayed rewards, and GRPO, a critic‑free baseline—to assess how well the LLM’s policy decisions align with simulated economic dynamics, testing factors such as semantic signals, reward horizons, and cross‑simulator transfer. "whyItMatters":"The study demonstrates a method for evaluating LLM‑generated policy proposals based on their actual simulated economic consequences rather than merely their linguistic plausibility."
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