让数字孪生学会人类偏好的公平性，提升决策可信度
Inverse Optimality for Fair Digital Twins: A Preference-based approach
- 从人类对决策的成对偏好中学习公平目标
- 用双胞胎神经网络生成可计算的公平代价函数
- 在疫情医院资源分配中验证了有效性
数字孪生（DTs）正被广泛应用于复杂人机系统中的自主决策。然而，其数学最优决策常与人类预期不符，反映出算法理性与人类有限理性的持续偏差。本文提出一种框架，将公平性作为可学习的目标引入基于优化的数字孪生系统。通过设计一种基于偏好的学习流程，直接从人类对可行决策的成对偏好中推断潜在的公平目标。开发专用的双胞胎神经网络，生成依赖上下文信息的凸二次代价函数。该代理目标驱动优化过程，使结果更符合人类感知的公平性，同时保持计算效率。在新冠疫情医院资源分配场景中验证了该方法的有效性。本工作为将以人为本的公平性融入自主决策系统设计提供了实用方案。
原文摘要 · Abstract (English)
Digital Twins (DTs) are increasingly used as autonomous decision-makers in complex socio-technical systems. However, their mathematically optimal decisions often diverge from human expectations, revealing a persistent mismatch between algorithmic and bounded human rationality. This work addresses this challenge by proposing a framework that introduces fairness as a learnable objective within optimization-based Digital Twins. In this respect, a preference-driven learning workflow that infers latent fairness objectives directly from human pairwise preferences over feasible decisions is introduced. A dedicated Siamese neural network is developed to generate convex quadratic cost functions conditioned on contextual information. The resulting surrogate objectives drive the optimization procedure toward solutions that better reflect human-perceived fairness while maintaining computational efficiency. The effectiveness of the approach is demonstrated on a COVID-19 hospital resource allocation scenario. Overall, this work offers a practical solution to integrate human-centered fairness into the design of autonomous decision-making systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。