发表机构
MIT; Harvard（麻省理工学院; 哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究内部多元主义下成对比较假设的局限性，提供决策规则多元偏好形式模型，指出局部比较无法捕捉全局优先性及会引发内部冲突，允许表达不确定性可减少查询，模型指向直接引出优先性的偏好学习方法。
AI 中文摘要
局部成对比较是了解人们希望决策规则如何工作的标准工具，但有两个强假设。我们研究内部多元主义下这些假设如何被破坏，提供正式模型，识别出强迫局部成对比较数据的两个不同失败情况，还探讨了允许报告犹豫不决的替代方案，最后描述了模型指向的偏好学习方法。
英文摘要
Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision rules according to multiple authoritative priorities about how the rule should behave. We provide a formal model of such pluralistic preferences over decision rules, which then lets us identify two distinct failures of forced local pairwise comparison data. First, priorities such as proportionality, egalitarianism, and equal treatment are inherently global: what they imply in one case can depend on what happens elsewhere, so local comparisons may fail to capture them. Second, even when priorities are representable locally, tension between strongly-held priorities can generate internal conflict, producing potentially costly behavioral distortions when comparisons are forced. We then use our model to investigate the alternative -- allowing people to report indecision -- and our findings suggest that doing so can considerably reduce the number of queries needed to learn preferences accurately. We conclude by describing how our model points toward preference-learning methods that elicit these priorities directly, yielding more faithful and interpretable accounts of what people value.
