Designing Reputation Systems for Manufacturing Data Trading Markets: A Multi-Agent Evaluation with Q-Learning and IRL-Estimated Utilities

2025-11-25

AI Summary

To address buyer distrust arising from the inability to pre-verify data quality in data trading markets, this paper develops a multi-agent data market simulator tailored for manufacturing, integrating Q-learning and inverse reinforcement learning (IRL) to model participant behavior and estimate utility functions. The key contribution is a novel hybrid reputation system that synergistically combines Time-decay, Bayesian-beta, PageRank, PowerTrust, and PeerTrust mechanisms. Experimental results demonstrate that while PeerTrust alone achieves optimal alignment between price and data quality, the proposed hybrid system further enhances market efficiency and fairness—significantly improving price-quality consistency, mitigating monopolistic tendencies, increasing systemic stability, and strengthening the reliability of the data ecosystem.

Technology Category

• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation
• Security and Privacy: Data transparency and provenance

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.

Problem

• Developing reputation systems to address information asymmetry in data trading markets
• Evaluating trust mechanisms using multi-agent simulation with reinforcement learning
• Designing hybrid reputation systems to improve price-quality alignment and market stability

Innovation

• Multi-agent simulator models participant behavior
• Reinforcement learning enables adaptive agent actions
• Hybrid reputation system improves price-quality alignment