投票中计分规则的溯因与对比解释
Abductive and Contrastive Explanations for Scoring Rules in Voting
AI总结：
将投票规则视为分类器，提出基于形式可解释性的溯因和对比解释方法，识别蕴含获胜者或解释候选人落选的最小偏好子集，设计了计分规则的计算算法，并分析了Borda规则下解释大小的下界及相关性。
摘要：
我们将投票规则视为分类器，将获胜者（类别）分配给选民偏好概况（实例）。我们提议应用形式可解释性技术，最显著的是溯因和对比解释，以识别偏好概况中最小的子集，这些子集要么蕴含当前的获胜者，要么解释为什么不同的候选人未被选中。形式解释与计算社会选择中研究的经典问题（如贿赂、可能和必要的获胜者识别以及偏好学习）有很强的联系。我们设计了用于计算计分规则的溯因和对比解释的算法。对于 Borda 规则，我们找到了最小溯因解释大小的下界，并进行了模拟以识别偏好概况属性与其最小溯因解释大小之间的相关性。
英文摘要：
We view voting rules as classifiers that assign a winner (a class) to a profile of voters' preferences (an instance). We propose to apply techniques from formal explainability, most notably abductive and contrastive explanations, to identify minimal subsets of a preference profile that either imply the current winner or explain why a different candidate was not elected. Formal explanations turn out to have strong connections with classical problems studied in computational social choice such as bribery, possible and necessary winner identification, and preference learning. We design algorithms for computing abductive and contrastive explanations for scoring rules. For the Borda rule, we find a lower bound on the size of the smallest abductive explanations, and we conduct simulations to identify correlations between properties of preference profiles and the size of their smallest abductive explanations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
