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摘要翻译：
我们提供了确定性的，多项式时间的可计算投票规则，它近似道奇森和（“最小化版本”）杨的评分规则在一个对数因子内。我们对道奇森规则的近似紧到一个常数因子，因为道奇森规则是$\np$-很难在某个对数因子内近似。杨氏法则的“最大化版本”众所周知是$\np$-很难用任何常数来近似。这两种近似值都是简单的，而且作为它们本身的规则是自然的：给定一个我们希望得分的候选人，我们可以将其道奇森或杨得分视为给定的选民偏好集与其中要得分的候选人是孔多塞获胜者之间的编辑距离。（这两种评分规则的区别在于允许的编辑类型。）我们认为一系列编辑的边际成本是编辑次数除以编辑减少的次数（候选人在与对手的两两竞争中对对手的赤字）。在一系列轮中，我们的评分规则贪婪地选择一个编辑序列，该序列恰好修改一个投票者的偏好，其边际成本不超过任何其他此类单票修改序列。
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英文标题：
《A $O(\log m)$, deterministic, polynomial-time computable approximation
of Lewis Carroll's scoring rule》
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作者：
Jason Covey and Christopher Homan
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Science and Game Theory 计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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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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一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
We provide deterministic, polynomial-time computable voting rules that approximate Dodgson's and (the ``minimization version'' of) Young's scoring rules to within a logarithmic factor. Our approximation of Dodgson's rule is tight up to a constant factor, as Dodgson's rule is $\NP$-hard to approximate to within some logarithmic factor. The ``maximization version'' of Young's rule is known to be $\NP$-hard to approximate by any constant factor. Both approximations are simple, and natural as rules in their own right: Given a candidate we wish to score, we can regard either its Dodgson or Young score as the edit distance between a given set of voter preferences and one in which the candidate to be scored is the Condorcet winner. (The difference between the two scoring rules is the type of edits allowed.) We regard the marginal cost of a sequence of edits to be the number of edits divided by the number of reductions (in the candidate's deficit against any of its opponents in the pairwise race against that opponent) that the edits yield. Over a series of rounds, our scoring rules greedily choose a sequence of edits that modify exactly one voter's preferences and whose marginal cost is no greater than any other such single-vote-modifying sequence.
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PDF链接：
https://arxiv.org/pdf/0804.1421
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