[提交于 2026 年 10 月 20 日]

标题：数据差异化评估框架（DDF）：在 Agent 评测流水线（SDSAE）中利用信息论与博弈论指标量化数据边际价值

查看 Sukesh Kumar 及另外 1 位作者题为《The Data Differentiator Framework (DDF): Quantifying Marginal Data Value with Information-Theoretic and Game-Theoretic Measures in an Agentic Evaluation Pipeline (SDSAE)》的论文 PDF

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摘要：在数据资源丰富的企业中，核心难点并不在于候选数据集本身质量是否优良，而在于它是否能在组织已有数据资产的基础之上提供增量信息，以及这种增量是否与其价格相匹配。我们提出了数据差异化评估框架（Data Differentiator Framework, DDF），这是一种针对候选数据资产的综合评分方法，它结合了：(i) 针对现有资产组合的基于互信息的冗余惩罚项；(ii) 源自消融实验的成本归一化且受显著性门控的边际效用评分；以及 (iii) 企业设定的显式治理输入（如可信度、实体解析质量、战略权重）。该框架由分阶段的流水线——战略数据采购 Agent 生态系统（Strategic Data Sourcing Agent Ecosystem, SDSAE）执行，该流水线能够为未通过治理或冗余门控的资产快速短路阻断，从而省去昂贵的模型重训步骤。在理念上，DDF 将 mRMR 特征选择中的“最小冗余最大相关”（relevance-minus-redundancy）原则从特征层级拓展到了资产层级，并补充了成本归一化、统计显著性门控以及单样本估值方法（如 Data Shapley）所未涵盖的治理考量。我们在包含 284,807 笔交易的公开信用卡欺诈基准上，构建了 5 个覆盖不同冗余区间的候选资产并对该框架进行了验证。实验表明，仅有真正具备差异化的资产通过了显著性门控（$\Delta$AUC = +0.0029，DeLong $p = 1.4\times 10^{-9}$，Holm 校正后 $p = 7.2\times 10^{-9}$）并获得了采购推荐（DDS = +0.663）；而完全冗余的资产得分最低（DDS = -0.135）。该推荐结果在 22 种合规战略权重配置中的 19 种以及 [0.15, 0.66] 区间内的所有决策阈值下均表现稳定。我们在案例研究中明确指出了哪些指标是根据实际数据计算得出、哪些属于模拟的治理输入，并讨论了单一数据集评估的设计局限。

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来自：Sukesh Kumar [

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[v1] 2026 年 10 月 20 日 星期二 21:10:19 UTC (644 KB)

[Submitted on 20 Oct 2026]

Title:The Data Differentiator Framework (DDF): Quantifying Marginal Data Value with Information-Theoretic and Game-Theoretic Measures in an Agentic Evaluation Pipeline (SDSAE)

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Abstract:In data-rich enterprises the hard question is not whether a candidate dataset is good in isolation, but whether it adds information beyond what the organization already holds, and whether that increment is worth its price. We present the Data Differentiator Framework (DDF), a composite scoring method for candidate data assets that combines (i) a mutual-information-based redundancy penalty against the existing portfolio, (ii) a cost-normalized, significance-gated marginal utility score derived from an ablation study, and (iii) explicit governance inputs (trust, identity resolution quality, strategic weights) set by the business. The framework is executed by a staged pipeline, the Strategic Data Sourcing Agent Ecosystem (SDSAE), that short-circuits expensive model-retraining steps for assets failing governance or redundancy gates. Conceptually, the DDF extends the relevance-minus-redundancy principle of mRMR feature selection from the feature level to the asset level, and adds cost normalization, a statistical significance gate, and governance terms that per-sample valuation methods such as Data Shapley do not model. We validate the framework on the public credit-card fraud benchmark (284,807 transactions) with five constructed candidate assets spanning the redundancy spectrum. Only the genuinely differentiated asset passes the significance gate ($\Delta$AUC = +0.0029, DeLong $p = 1.4\times 10^{-9}$, Holm-corrected $p = 7.2\times 10^{-9}$) and receives an acquire recommendation (DDS = +0.663); the fully redundant asset scores lowest (DDS = -0.135). The recommendation is stable across 19 of 22 admissible strategic-weight configurations and across all decision thresholds in [0.15, 0.66]. We state clearly which quantities in the case study are computed from data and which are simulated governance inputs, and we discuss the design limits of a single-dataset evaluation.

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From: Sukesh Kumar [

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Tue, 20 Oct 2026 21:10:19 UTC (644 KB)