摘要翻译：
我们提出了一种简单的方法，将投票规则组合在一起，在每个投票规则的不同获胜者之间进行决选。我们证明了这个组合子具有几个良好的性质。例如，即使只有一个基本投票规则具有像Condorcet一致性这样的理想属性，组合也会继承该属性。此外，我们还证明了以这种方式将投票规则组合在一起会使寻找操纵更加困难。最后，我们研究了这种组合器对找到接近最优操作的近似方法的影响。
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英文标题：
《Combining Voting Rules Together》
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作者：
Nina Narodytska, Toby Walsh and Lirong Xia
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base voting rules has a desirable property like Condorcet consistency, the combination inherits this property. In addition, we prove that combining voting rules together in this way can make finding a manipulation more computationally difficult. Finally, we study the impact of this combinator on approximation methods that find close to optimal manipulations.
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PDF链接：
https://arxiv.org/pdf/1203.3051