Data valuation has become a cornerstone of the modern data economy, where datasets function as tradable intellectual assets that drive decision-making, model training, and market transactions. Despite substantial progress, existing valuation methods remain limited by high computational cost, weak fairness guarantees, and poor interpretability, which hinder their deployment in large-scale, high-stakes applications. This paper introduces Neural Dynamic Data Valuation (NDDV), a new framework that formulates data valuation as a stochastic optimal control problem to capture the dynamic evolution of data utility over time. Unlike static combinatorial approaches, NDDV models data interactions through continuous trajectories that reflect both individual and collective learning dynamics.

翻译：数据估值已成为现代数据经济的基石，数据集作为可交易的知识资产，驱动着决策制定、模型训练和市场交易。尽管已取得显著进展，现有估值方法仍受限于高计算成本、弱公平性保证和较差的可解释性，这阻碍了其在大规模高风险应用中的部署。本文提出神经动态数据估值（NDDV）这一新框架，将数据估值表述为随机最优控制问题，以捕捉数据效用随时间动态演化的特性。与静态组合方法不同，NDDV通过连续轨迹对数据交互进行建模，这些轨迹同时反映了个体与集体的学习动态。