代表性社会选择：从学习理论到AI对齐
Representative Social Choice: From Learning Theory to AI Alignment
AI总结：
提出代表性社会选择框架，将大规模集体决策的偏好聚合转化为统计学习问题，证明机制泛化性质与不可能性定理，连接社会选择、学习理论与AI对齐。
摘要：
社会选择理论研究群体中的偏好聚合，既用于人类智能体的机制设计，也用于语言模型的民主对齐。本研究提出代表性社会选择框架，用于建模集体决策中的民主代表机制，适用于议题和个体数量过多而无法直接考虑所有偏好的场景。这些场景在现实决策过程中普遍存在，如陪审团审判、立法、公司治理，以及近期的语言模型对齐。在代表性社会选择中，群体由个体-议题对的有限样本表示，社会选择决策基于这些样本做出。我们证明，代表性社会选择中的许多深层问题可表述为统计学习问题，并利用机器学习理论证明社会选择机制的泛化性质。我们进一步提出代表性社会选择的公理，并运用新的组合分析工具证明类阿罗不可能性定理。该框架为社会选择引入代表性方法，开辟了社会选择、学习理论与AI对齐交叉领域的研究方向。
英文摘要：
Social choice theory is the study of preference aggregation across a population, used both in mechanism design for human agents and in the democratic alignment of language models. In this study, we propose the representative social choice framework for the modeling of democratic representation in collective decisions, where the number of issues and individuals are too large for mechanisms to consider all preferences directly. These scenarios are widespread in real-world decision-making processes, such as jury trials, legislation, corporate governance, and, more recently, language model alignment. In representative social choice, the population is represented by a finite sample of individual-issue pairs based on which social choice decisions are made. We show that many of the deepest questions in representative social choice can be formulated as statistical learning problems, and prove the generalization properties of social choice mechanisms using the theory of machine learning. We further formulate axioms for representative social choice, and prove Arrow-like impossibility theorems with new combinatorial tools of analysis. Our framework introduces the representative approach to social choice, opening up research directions at the intersection of social choice, learning theory, and AI alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
