MALLES：基于多智能体LLM的经济沙盒与消费者偏好对齐
MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment
AI总结：
本文提出MALLES，通过大规模模型的泛化能力构建统一模拟框架，利用偏好学习对齐LLM，解决高维环境下的数据稀疏问题，提升经济模拟的准确性和稳定性。
摘要：
在现实经济中，现代决策面临高维、多模态环境的挑战，且受智能体异质性和组合数据稀疏性影响更甚。本文引入基于多智能体大语言模型的经济沙盒（MALLES），利用大规模模型的内在泛化能力，建立适用于跨领域和跨类别的统一模拟框架。核心方法是偏好学习范式，通过在广泛、异质的交易记录上进行后训练，使LLM在经济上对齐，从而内化并转移潜在的消费者偏好模式，缓解个体类别中的数据稀疏问题。为增强模拟稳定性，我们实现了一个均场机制，用于建模产品环境与客户群体之间的动态交互，有效稳定高维决策空间中的采样过程。此外，我们提出一个多智能体讨论框架，其中专门化的智能体协作处理大量产品信息。该架构将认知负载分散以缓解单智能体注意力瓶颈，并通过结构化对话捕捉关键决策因素。实验表明，与现有经济和金融LLM模拟基线相比，我们的框架在产品选择准确率、购买量预测和模拟稳定性方面实现了显著改进。我们的结果证实了大规模语言模型作为高保真、可扩展决策模拟和后续分析的基础支柱的潜力，基于基础数据库的现实经济。
英文摘要：
In the real economy, modern decision-making is fundamentally challenged by high-dimensional, multimodal environments, which are further complicated by agent heterogeneity and combinatorial data sparsity. This paper introduces a Multi-Agent Large Language Model-based Economic Sandbox (MALLES), leveraging the inherent generalization capabilities of large-sacle models to establish a unified simulation framework applicable to cross-domain and cross-category scenarios. Central to our approach is a preference learning paradigm in which LLMs are economically aligned via post-training on extensive, heterogeneous transaction records across diverse product categories. This methodology enables the models to internalize and transfer latent consumer preference patterns, thereby mitigating the data sparsity issues prevalent in individual categories. To enhance simulation stability, we implement a mean-field mechanism designed to model the dynamic interactions between the product environment and customer populations, effectively stabilizing sampling processes within high-dimensional decision spaces. Furthermore, we propose a multi-agent discussion framework wherein specialized agents collaboratively process extensive product information. This architecture distributes cognitive load to alleviate single-agent attention bottlenecks and captures critical decision factors through structured dialogue. Experiments demonstrate that our framework achieves significant improvements in product selection accuracy, purchase quantity prediction, and simulation stability compared to existing economic and financial LLM simulation baselines. Our results substantiate the potential of large language models as a foundational pillar for high-fidelity, scalable decision simulation and latter analysis in the real economy based on foundational database.
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。
