通过嵌入降维找共识区间，用少量提问高效识别群体同意区域。
Probably Approximately Consensus: On the Learning Theory of Finding Common Ground
- 将用户意见映射到一维观点空间，用区间表示共识。
- 在假设区间内最大化预期同意率，隐含考虑议题重要性。
- 只需选择性提问少量用户，就能高效定位最优共识区。
在线协商平台的核心目标是通过用户表达的偏好，识别出被广泛接受的观点。然而，共识提取应超越用户给出的具体陈述，还需考虑特定议题的重要性。本文通过嵌入与降维技术，将高维数据映射至一维观点空间，将共识建模为一个区间。定义的目标函数旨在最大化假设区间内的期望同意率，其中期望对潜在议题分布取值，隐式纳入了议题的显著性。提出一种高效的经验风险最小化（ERM）算法，并建立了PAC学习保证。初步实验表明该算法表现良好，并探索了更高效的最优共识区域识别方法。结果显示，通过对已有陈述中的用户进行选择性提问，可将所需查询次数降至实际可行水平。
原文摘要 · Abstract (English)
A primary goal of online deliberation platforms is to identify ideas that are broadly agreeable to a community of users through their expressed preferences. Yet, consensus elicitation should ideally extend beyond the specific statements provided by users and should incorporate the relative salience of particular topics. We address this issue by modelling consensus as an interval in a one-dimensional opinion space derived from potentially high-dimensional data via embedding and dimensionality reduction. We define an objective that maximizes expected agreement within a hypothesis interval where the expectation is over an underlying distribution of issues, implicitly taking into account their salience. We propose an efficient Empirical Risk Minimization (ERM) algorithm and establish PAC-learning guarantees. Our initial experiments demonstrate the performance of our algorithm and examine more efficient approaches to identifying optimal consensus regions. We find that through selectively querying users on an existing sample of statements, we can reduce the number of queries needed to a practical number.
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