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MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment

Yusen Wu, Yiran Liu, Xiaotie Deng

Abstract

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.

MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment

Abstract

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.
Paper Structure (18 sections, 4 figures, 1 table)

This paper contains 18 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries: LLM-Empowered Economic Agent Simulation
  4. Methodology
  5. Framework Overview
  6. Problem Formulation and Notation
  7. Retail and Wholesale Customer Simulation with Economic Alignment and Multi-Agent Discussion
  8. Stabilization and Calibration Mechanisms
  9. Framework Implementation
  10. Theoretical Guarantees
  11. Experiments
  12. Dataset Construction
  13. Dataset Construction
  14. Experimental Motivation and Design
  15. Simulation and Evaluation
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Dataset building pipeline from real trade data.
  • Figure 2: Major simulation pipeline of introduced sandbox.
  • Figure 3: Performance comparison of different simulation algorithms. The best one in each metric is bolded. Time Cost values are normalized to the [0,1] range with original values on the bars. The base_sandbox and meanfield_sandbox collectively correspond to the MALLES approach with and without mean-field enhancement, respectively.
  • Figure 4: Ablation study on multiple objectives