An axiomatic distance methodology for aggregating multimodal evaluations
An axiomatic distance methodology for aggregating multimodal evaluations
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DOI：
10.1016/j.ins.2021.12.124
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发表时间：
2022-02-01
影响因子：
8.1
通讯作者：
Yasmin, Romena
中科院分区：
文献类型：
--
作者：
Escobedo, Adolfo R.;Moreno-Centeno, Erick;Yasmin, Romena
这项工作介绍了一种多模式的数据聚合方法，其特点是优化模型和算法，用于联合聚合异构的序数和基数评价输入到一个共识评价。具体而言，这项工作推导出数学建模组件，以执行集体序数和基数评估之间的三种类型的逻辑耦合：评级和排名偏好，数值和序数估计，评级和批准偏好。所提出的方法是基于公理距离植根于社会选择理论。此外，它充分处理了高度不完整的评估，捆绑的价值观，以及群体决策环境的其他复杂方面。我们说明了所提出的方法的实用性，在一个案例研究，涉及学术学生论文比赛。该方法的优点和计算方面的进一步探讨通过合成的实例从分布参数化地面真理和不同的噪声水平。这些结果表明，多模态聚合有效地提取了一个集体的真理，从嘈杂的信息源，并成功地捕捉到独特的评价质量的评级和排名偏好数据。(C)2022爱思唯尔公司All rights reserved.
This work introduces a multimodal data aggregation methodology featuring optimization models and algorithms for jointly aggregating heterogeneous ordinal and cardinal evaluation inputs into a consensus evaluation. Specifically, this work derives mathematical modeling components to enforce three types of logical couplings between the collective ordinal and cardinal evaluations: Rating and ranking preferences, numerical and ordinal estimates, and rating and approval preferences. The proposed methodology is based on axiomatic distances rooted in social choice theory. Moreover, it adequately deals with highly incomplete evaluations, tied values, and other complicating aspects of group decision-making contexts. We illustrate the practicality of the proposed methodology in a case study involving an academic student paper competition. The methodology's advantages and computational aspects are further explored via synthetic instances sampled from distributions parametrized by ground truths and varying noise levels. These results show that multimodal aggregation effectively extracts a collective truth from noisy information sources and successfully captures the distinctive evaluation qualities of rating and ranking preference data. (C) 2022 Elsevier Inc. All rights reserved.