LLM經濟學家：大型人口模型與多智能體生成模擬中的機制設計

摘要

我們提出了LLM經濟學家，這是一個新穎的框架，利用基於代理的建模來設計和評估在具有層次決策的戰略環境中的經濟政策。在較低層次，有限理性的工人代理——實例化為從美國人口普查校準的收入和人口統計數據中抽樣的角色條件提示——選擇勞動供給以最大化在上下文中學習的基於文本的效用函數。在較高層次，一個規劃者代理採用上下文強化學習來提出與當前美國聯邦稅率級距掛鉤的分段線性邊際稅率表。這種構造賦予經濟模擬三種進行可信財政實驗所必需的能力：(i) 異質效用的優化，(ii) 原則性地生成大規模、人口統計上真實的代理群體，以及 (iii) 完全以自然語言表達的機制設計——最終的助推問題。在最多一百個互動代理的群體中進行的實驗表明，規劃者收斂於相對於Saez解決方案改善總體社會福利的斯塔克爾伯格均衡，而週期性的角色層次投票程序在分散治理下進一步提升了這些收益。這些結果表明，基於大型語言模型的代理能夠共同建模、模擬和治理複雜的經濟系統，為社會規模的政策評估提供了一個易於處理的測試平台，以幫助建設更好的文明。

English

We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. At the lower level, bounded rational worker agents -- instantiated as persona-conditioned prompts sampled from U.S. Census-calibrated income and demographic statistics -- choose labor supply to maximize text-based utility functions learned in-context. At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets. This construction endows economic simulacra with three capabilities requisite for credible fiscal experimentation: (i) optimization of heterogeneous utilities, (ii) principled generation of large, demographically realistic agent populations, and (iii) mechanism design -- the ultimate nudging problem -- expressed entirely in natural language. Experiments with populations of up to one hundred interacting agents show that the planner converges near Stackelberg equilibria that improve aggregate social welfare relative to Saez solutions, while a periodic, persona-level voting procedure furthers these gains under decentralized governance. These results demonstrate that large language model-based agents can jointly model, simulate, and govern complex economic systems, providing a tractable test bed for policy evaluation at the societal scale to help build better civilizations.

LLM經濟學家：大型人口模型與多智能體生成模擬中的機制設計

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra

摘要

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