Designing Reputation Systems for Manufacturing Data Trading Markets: A Multi-Agent Evaluation with Q-Learning and IRL-Estimated Utilities
Kenta Yamamoto, Teruaki Hayashi
TL;DR
The paper tackles trust and quality challenges in data-trading markets by building a multi-agent simulator that merges RL for adaptive decision-making with IRL for empirically grounded utility estimation in a manufacturing context. It systematically evaluates five reputation systems (Time-Decay, Bayesian-beta, PageRank, PowerTrust, PeerTrust) and introduces a novel hybrid, Beta-PT, to improve price–quality alignment while reducing market concentration. Key findings show that reputation systems generally enhance market efficiency and stability, with Beta-PT achieving the best balance between price–quality consistency and fairness. The work provides a computational framework to study trust-based mechanisms in data ecosystems and offers actionable guidance for designing reliable, scalable data marketplaces.
Abstract
Recent advances in machine learning and big data analytics have intensified the demand for high-quality cross-domain datasets and accelerated the growth of data trading across organizations. As data become increasingly recognized as an economic asset, data marketplaces have emerged as a key infrastructure for data-driven innovation. However, unlike mature product or service markets, data-trading environments remain nascent and suffer from pronounced information asymmetry. Buyers cannot verify the content or quality before purchasing data, making trust and quality assurance central challenges. To address these issues, this study develops a multi-agent data-market simulator that models participant behavior and evaluates the institutional mechanisms for trust formation. Focusing on the manufacturing sector, where initiatives such as GAIA-X and Catena-X are advancing, the simulator integrates reinforcement learning (RL) for adaptive agent behavior and inverse reinforcement learning (IRL) to estimate utility functions from empirical behavioral data. Using the simulator, we examine the market-level effects of five representative reputation systems-Time-decay, Bayesian-beta, PageRank, PowerTrust, and PeerTrust-and found that PeerTrust achieved the strongest alignment between data price and quality, while preventing monopolistic dominance. Building on these results, we develop a hybrid reputation mechanism that integrates the strengths of existing systems to achieve improved price-quality consistency and overall market stability. This study extends simulation-based data-market analysis by incorporating trust and reputation as endogenous mechanisms and offering methodological and institutional insights into the design of reliable and efficient data ecosystems.