用强化学习模拟制造数据市场，优化信誉系统提升价格与质量匹配度。
Designing Reputation Systems for Manufacturing Data Trading Markets: A Multi-Agent Evaluation with Q-Learning and IRL-Estimated Utilities
- 构建多智能体仿真模型，结合RL与逆强化学习模拟市场行为。
- PeerTrust系统使数据价格与质量最匹配，且避免垄断现象。
- 提出融合多种机制的混合信誉方案，增强市场稳定性与可靠性。
机器学习与大数据分析的进步加剧了跨领域高质量数据集的需求，推动组织间数据交易快速发展。随着数据被视为关键经济资产，数据交易平台成为数据驱动创新的核心基础设施。然而，与成熟的产品或服务市场相比，数据交易环境仍处于初级阶段，存在显著的信息不对称问题：买家无法在购买前验证数据内容或质量，导致信任与质量保障成为核心挑战。为此，本研究开发了一个多智能体数据市场仿真器，用于建模参与者行为并评估信任形成机制。聚焦制造领域（如GAIA-X和Catena-X倡议），仿真器融合强化学习（RL）以实现智能体自适应行为，并采用逆强化学习（IRL）从实证行为数据中估计效用函数。通过仿真，评估了五种代表性信誉系统——时间衰减、贝叶斯-贝塔、PageRank、PowerTrust与PeerTrust——发现PeerTrust在数据价格与质量一致性方面表现最优，同时抑制了垄断趋势。基于此，我们设计了一种融合各系统优势的混合信誉机制，在提升价格-质量一致性的同时增强整体市场稳定性。本研究将信任与信誉作为内生机制纳入仿真分析，为构建可靠高效的数据生态系统提供了方法论与制度性洞见。
原文摘要 · Abstract (English)
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.
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