Purpose: Multiwinner voting rules typically require full knowledge of voter preferences, which becomes impractical in large-scale or attention-limited settings. This paper investigates how accurately a winning committee can be approximated when voter preferences are elicited using a limited budget of structured queries. Methods: We introduce a query-based framework for multiwinner elections in which voter preferences are elicited through refinement queries over subsets of candidates under a limited budget. We analyse several cost functions that model the cognitive effort needed to answer such queries, propose axiomatic properties for evaluating them, and experimentally evaluate simple query-based committee selection rules across multiple election models. Results: Experimental results show that strategies based on recursively splitting candidate sets provide the best trade-off between elicitation cost and committee accuracy. Across several statistical models, these strategies approximate the outcome of k-Borda elections significantly more efficiently than alternative query types. Conclusion: The results demonstrate that well-designed query strategies can substantially reduce the amount of preference information required while still producing high-quality committee outcomes, suggesting that query-based elicitation is a promising approach for scalable multiwinner decision-making.

翻译：目的：多数投票规则通常需要完全了解选民的偏好，这在规模庞大或注意力受限的场景下变得不切实际。本文研究在利用有限结构化查询预算获取选民偏好时，获胜委员会能被近似到何种精确程度。方法：我们提出一种基于查询的多赢家选举框架，在此框架中，选民偏好在有限预算下通过针对候选子集的精炼查询来获取。我们分析了建模回答此类查询所需认知努力的若干代价函数，提出评估这些函数的公理化属性，并通过实验在多种选举模型下评估简单的基于查询的委员会选择规则。结果：实验结果表明，基于递归分割候选集的策略在查询代价与委员会准确性之间提供了最佳权衡。在多种统计模型中，这些策略近似k-Borda选举结果的效率显著高于其他查询类型。结论：研究结果证明，精心设计的查询策略能在大幅减少偏好信息需求的同时，仍产生高质量的委员会结果，这表明基于查询的偏好获取是实现可扩展的多赢家决策的一种有前景的方法。