论文标题
票代表
Representation with Incomplete Votes
论文作者
论文摘要
在线公民参与的平台在很大程度上取决于参与者是否同意或不同意他们的方法，将数千条评论凝结成相关的方法。这些方法应保证参与者的公平代表，因为他们的结果可能会影响对话的健康并为有影响力的下游决策提供依据。为此，我们借鉴了基于批准的委员会选举的文献。我们的环境是新颖的，因为批准投票是不完整的，因为参与者通常不会对所有评论进行投票。我们证明，这种并发症使非自适应算法在必须收集的信息数量方面不切实际。因此，我们开发了一种自适应算法，该算法通过向传入的参与者展示基于以前参与者的投票似乎有希望的陈述来更有效地使用信息。我们证明，即使参与者只对一小部分评论进行投票，这种方法也满足了公平代表的常用概念。最后，使用实际数据的经验评估表明，所提出的算法在实践中提供了代表性的结果。
Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of the participants, as their outcomes may affect the health of the conversation and inform impactful downstream decisions. To that end, we draw on the literature on approval-based committee elections. Our setting is novel in that the approval votes are incomplete since participants will typically not vote on all comments. We prove that this complication renders non-adaptive algorithms impractical in terms of the amount of information they must gather. Therefore, we develop an adaptive algorithm that uses information more efficiently by presenting incoming participants with statements that appear promising based on votes by previous participants. We prove that this method satisfies commonly used notions of fair representation, even when participants only vote on a small fraction of comments. Finally, an empirical evaluation using real data shows that the proposed algorithm provides representative outcomes in practice.
