学习选举

摘要：

投票系统有着广泛的应用，包括推荐系统，网络搜索，产品设计和选举。由于缺乏通用分析工具，很难为每个用例手工设计理想的投票规则。因此，自动发现针对每种场景的投票规则很有吸引力。在本文中，我们证明了集输入神经网络架构，如Set Transformers，全连接图网络和DeepSets，在理论和经验上都非常适合学习投票规则。特别是，我们表明，这些网络模型不仅可以模仿一些现有的投票规则，以令人信服的准确性-基于位置（如Plurality和Borda）和基于比较（如Kemeny，Copeland和Maximin）-但也发现接近最优的投票规则，最大限度地提高不同的社会福利功能。此外，学习的投票规则很好地推广到不同的选民效用分布和选举规模在训练过程中看不见。

英文摘要：

Voting systems have a wide range of applications including recommender systems, web search, product design and elections. Limited by the lack of general-purpose analytical tools, it is difficult to hand-engineer desirable voting rules for each use case. For this reason, it is appealing to automatically discover voting rules geared towards each scenario. In this paper, we show that set-input neural network architectures such as Set Transformers, fully-connected graph networks and DeepSets are both theoretically and empirically well-suited for learning voting rules. In particular, we show that these network models can not only mimic a number of existing voting rules to compelling accuracy -- both position-based (such as Plurality and Borda) and comparison-based (such as Kemeny, Copeland and Maximin) -- but also discover near-optimal voting rules that maximize different social welfare functions. Furthermore, the learned voting rules generalize well to different voter utility distributions and election sizes unseen during training.

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。