| 引用本文: | 何佳妮,黄科满,刘金飞,卢卫,范举,杜小勇.机器学习模型市场的收益分配策略综述.软件学报,2026,37(8):3309-3336 |
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| 机器学习模型市场的收益分配策略综述 |
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何佳妮1,2, 黄科满1,2, 刘金飞3, 卢卫1,2, 范举1,2, 杜小勇1,2
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1.数据工程与知识工程教育部重点实验室(中国人民大学), 北京 100872;2.中国人民大学 信息学院, 北京 100872;3.浙江大学 计算机科学与技术学院, 浙江 杭州 310058
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| 摘要: |
| 数据收益的合理分配是构建可持续数据市场的核心命题之一. 相较于传统生产要素, 数据的价值后验性、信息不对称性、零成本复制、外部性等特性, 给其收益分配策略的设计带来了多维度的挑战. 聚焦数据市场的重要分支——机器学习模型市场(以下简称模型市场), 系统梳理该领域收益分配策略的研究进展, 揭示其“从同质化走向差异化、从短期走向长期”的发展趋势. 具体而言, 首先形式化定义模型市场的收益分配问题, 厘清收益分配的主体、模式与目标. 在此基础上整理“同质化分配-差异化补偿”的分配依据: 在同质化贡献度量上, 归纳基于Shapley值等指标的数据贡献度评估体系; 在差异化补偿指标上, 解析数据成本、数据多样性等差异化指标的度量方法, 进而揭示综合两个维度的混合策略. 进一步地, 针对模型市场在长期尺度下的动态特征, 分析不同主体的策略性行为对收益分配的影响及其应对措施. 最后总结当前研究面临的主要挑战, 明晰从差异化补偿以及长期动态的视角探讨收益分配策略优化的未来研究方向. |
| 关键词: 模型市场 收益分配策略 数据贡献度量 差异化补偿 主体策略性行为 |
| DOI：10.13328/j.cnki.jos.007658 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62441230, 62172425); SMP-IDATA 晨星青年基金(SMP2023-iData-002) |
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| Survey on Revenue Allocation Strategies for Machine Learning Model Markets |
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HE Jia-Ni1,2, HUANG Ke-Man1,2, LIU Jin-Fei3, LU Wei1,2, FAN Ju1,2, DU Xiao-Yong1,2
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1.Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing 100872, China;2.School of Information, Renmin University of China, Beijing 100872, China;3.College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
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| Abstract: |
| The fair allocation of data revenue is one of the core issues in building sustainable data markets. Compared with traditional production factors, data exhibit several characteristics, such as ex-post value, information asymmetry, costless replication, and externalities, which pose multidimensional challenges for designing revenue allocation strategies. This study focuses on the machine learning model market, which is an important branch of the data market. It systematically reviews the research progress of revenue allocation strategies in this domain, revealing a development trend from homogeneity to differentiation and from short-term to long-term. Specifically, the revenue allocation problem in the machine learning model market is first formalized, and the participants, allocation modes, and objectives are clarified. On this basis, the allocation basis of “homogeneous allocation-differentiated compensation” is organized. In terms of homogeneous contribution measurement, data contribution evaluation methods based on indicators, such as the Shapley value, are summarized. In terms of differentiated compensation, the measurement methods of differentiated indicators such as data cost and data diversity are analyzed, and a hybrid strategy integrating both dimensions is revealed. Furthermore, regarding the dynamic characteristics of the model market over the long term, the impact of strategic behaviors of different participants on revenue allocation and the corresponding response measures are analyzed. Finally, the main challenges in current research are summarized, and future research directions for optimizing revenue allocation strategies are clarified from the perspectives of differentiated compensation and long-term dynamics. |
| Key words: model market revenue allocation strategy data contribution measurement differentiated compensation strategic behavior of stakeholders |
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