[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2806":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":30,"doi":31,"paper":32,"created_at":45},2806,"Evidence-Weighted Multi-Criteria Decision Support for Subjective Quality Assessment Under Sparse and Unbalanced Information","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16189222","Decision making for complex products often depends on subjective user experience, while competing alternatives may be supported by strongly unequal numbers of observations. This study develops an evidence-weighted multi-criteria decision-making methodology for such sparse and unbalanced information. A seven-category rating scale is aggregated at the respondent level and transformed affinely to [0, 1]. Domain-specific variance components are estimated by restricted maximum likelihood and used for empirical Bayes partial pooling, so that sparse estimates are moderated without excluding valid alternatives. AHP preference weights are kept conceptually separate from evidence weights, inherent product quality is separated from non-inherent ownership attributes, and uncertainty is propagated through Monte Carlo simulation. The empirical demonstration comprised 140 unique questionnaire records for 21 agricultural tractor brands with sample sizes from 1 to 50. Compared with direct averaging, the empirical Bayes ranking was highly preserved (Spearman ρ=0.992) while unsupported extremes were reduced. A controlled simulation showed lower RMSE for empirical Bayes than for the arithmetic mean, median, and fixed shrinkage under both Gaussian and bounded non-Gaussian data generation, with the largest benefit at n=1. Sensitivity analyses showed high ranking stability to the upper-level quality weight, perturbations of AHP weights, and bounded score transformations. The framework therefore provides reproducible uncertainty-aware decision support without treating weak evidence as either absent or equally strong as data-rich evidence.","复杂产品的决策往往依赖主观用户体验，而相互竞争的备选方案可能由数量极不均衡的观测值所支持。本研究针对此类稀疏且不均衡的信息，开发了一种证据加权多准则决策方法。将七类评分量表在受访者层面进行聚合，并通过仿射变换映射至[0, 1]区间。通过限制最大似然估计领域特定的方差分量，并将其用于经验贝叶斯部分池化，从而在不排除有效备选方案的前提下对稀疏估计进行适度调整。AHP偏好权重在概念上与证据权重保持分离，固有产品质量与非固有所有权属性相区分，不确定性通过蒙特卡洛模拟进行传播。实证演示包含21个农用拖拉机品牌的140份独立问卷记录，样本量从1到50不等。与直接平均法相比，经验贝叶斯排序结果高度保持（Spearman ρ=0.992），同时缺乏支持的极端值有所减少。一项受控模拟表明，在高斯和有界非高斯数据生成条件下，经验贝叶斯的RMSE均低于算术平均、中位数和固定收缩法，且在n=1时获益最大。敏感性分析表明，排序对上层质量权重、AHP权重的扰动以及有界得分变换均具有高度稳定性。因此，该框架提供了可重复的、考虑不确定性的决策支持，而不会将弱证据视为不存在或与数据丰富的证据同等有力。",null,"Applied Sciences","2026-09-17T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},8,20,17,13,1,"方法新颖、数据扎实的农机主观质量评价决策支持论文，对农业装备质量评估有参考价值，但属方法学研究，产业影响面有限。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农机装备","决策支持","质量评价",0,"10.3390\u002Fapp16189222",{"doi":31,"openalex_id":33,"authors":34,"venue":10,"cited_by_count":30,"oa_url":6,"card":38,"direction":42,"ingested_from":44},"W7213447409",[35],{"name":36,"orcid":37},"K. Durczak","https:\u002F\u002Forcid.org\u002F0000-0003-4811-005X",{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"提出证据加权多准则决策方法，解决稀疏不平衡主观评价下的产品排序问题。","经验贝叶斯部分池化、AHP权重分离、蒙特卡洛模拟，基于21个拖拉机品牌140份问","经验贝叶斯排序与直接平均高度一致（ρ=0.992），但能降低无支持极端值，n=1时RMSE改善最大。","农业人工智能与决策模型","可将该证据加权框架迁移到农机用户体验、智能装备评价等小样本主观决策场景，结合在线数据动态更新。","openalex","2026-09-17T23:31:04.245524Z"]