将投票机制设计为可学习的微分模型，实现智能系统中的公平决策。
Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment
- 把投票规则建模为可训练的微分函数，从数据中自动优化。
- 揭示了经典社会选择理论在机器学习中的新体现与权衡关系。
- 适合研究机制设计、对齐与公平性的学者参考。
社会选择已成为现代机器学习系统的核心组成部分。从拍卖和资源分配到大模型对齐，机器学习流程越来越多地将异质偏好和激励聚合为集体决策。实际上，许多当代机器学习系统已隐式实现社会选择机制，但缺乏明确的规范性审视。本文综述了可微社会选择：一种新兴范式，将投票规则、机制和聚合过程表述为可学习、可微模型，并基于数据进行优化。我们整合了拍卖、决策聚合和偏好学习领域的研究，揭示经典公理与不可能性结果如何以目标、约束和优化权衡的形式再现。最后，我们提出18个开放问题，定义了机器学习与社会选择理论交叉领域的新研究议程。
原文摘要 · Abstract (English)
Social choice has become a foundational component of modern machine learning systems. From auctions and resource allocation to the alignment of large generative models, machine learning pipelines increasingly aggregate heterogeneous preferences and incentives into collective decisions. In effect, many contemporary machine learning systems already implement social choice mechanisms, often implicitly and without explicit normative scrutiny. This Review surveys differentiable social choice: an emerging paradigm that formulates voting rules, mechanisms, and aggregation procedures as learnable, differentiable models optimized from data. We synthesize work across auctions, decision aggregation, and preference learning, showing how classical axioms and impossibility results reappear as objectives, constraints, and optimization trade-offs. We conclude by identifying 18 open problems defining a new research agenda at the intersection of machine learning and social choice theory.
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