Problem
Research questions and friction points this paper is trying to address.
active elicitation
voting rules
competitive ratio
next-best query model
The Art of Calling the Winner by Asking Just Enough Questions: Competitive Preference Elicitation with Next-Best Queries
🤖 AI Summary
研究通过次优查询模型主动获取代理人偏好，以使用投票规则集体选择方案。对多种位置评分规则实现了次线性竞争比，并为波达计数开发了两种技术。
📝 Abstract
We study active elicitation of agent preferences for collectively choosing among $m$ alternatives using prominent voting rules. We focus on the next-best query model, in which an agent responds to a query by revealing their next favorite alternative, and measure the competitive ratio, which is the worst-case ratio between the number of queries made by the active elicitation algorithm and the minimum number of queries needed to reveal the winning alternative(s) in hindsight. We show that sublinear competitive ratios are achievable for many positional scoring rules, whereas every Condorcet-consistent rule has competitive ratio linear in $m$. For Borda count, we develop two complementary techniques: level-wise pruning, whose analysis extends to general concave scoring rules, and multi-scale score thresholding, which gives an $O(\sqrt m)$ worst-case guarantee for Borda. We also demonstrate strong empirical performance of level-wise pruning on real data.
Innovation
Methods, ideas, or system contributions that make the work stand out.
next-best query model
competitive ratio
level-wise pruning
multi-scale score thresholding
Borda count
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