LLM-based Multi-Agent System for Simulating Strategic and Goal-Oriented Data Marketplaces

TL;DR

This paper introduces a novel LLM-based multi-agent system to simulate data marketplaces, enabling more realistic and adaptive modeling of participant behaviors, market dynamics, and trends through natural language reasoning.

Contribution

The paper presents a new LLM-powered multi-agent framework for simulating data marketplaces, allowing autonomous, strategic, and adaptive agent behaviors beyond rule-based models.

Findings

LLM-MAS reproduces real marketplace trading patterns.

The framework captures emergence and evolution of market trends.

Simulation results outperform traditional models in realism.

Abstract

Data marketplaces, which mediate the purchase and exchange of data from third parties, have attracted growing attention for reducing the cost and effort of data collection while enabling the trading of diverse datasets. However, a systematic understanding of the interactions between market participants, data, and regulations remains limited. To address this gap, we propose a Large Language Model-based Multi-Agent System (LLM-MAS) for data marketplaces. In our framework, buyer and seller agents powered by LLMs operate with explicit objectives and autonomously perform strategic actions, such as planning, searching, purchasing, pricing, and updating data. These agents can reason about market dynamics, forecast future demand, and adjust strategies accordingly. Unlike conventional model-based simulations, which are typically constrained to predefined rules, LLM-MAS supports broader and more adaptive behavior selection through natural language reasoning. We evaluated the framework via simulation experiments using three distribution-based metrics: (1) the number of purchases per dataset, (2) the number of purchases per buyer, and (3) the number of repeated purchases of the same dataset. The results demonstrate that LLM-MAS more faithfully reproduces trading patterns observed in real data marketplaces compared to traditional approaches, and further captures the emergence and evolution of market trends.