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Manipulative Elicitation - A New Attack on Elections with Incomplete Preferences

机译:操纵诱因 - 对不完全偏好的选举的新攻击

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Lu and Boutilier (2011) proposed a novel approach based on "minimax regret" to use classical score based voting rules in the setting where preferences can be any partial (instead of complete) orders over the set of alternatives. We show here that such an approach is vulnerable to a new kind of manipulation which was not present in the classical (where preferences are complete orders) world of voting. We call this attack "manipulative elicitation." More specifically, it may be possible to (partially) elicit the preferences of the agents in a way that makes some distinguished alternative win the election who may not be a winner if we elicit every preference completely. More alarmingly, we show that the related computational task is polynomial time solvable for a large class of voting rules which includes all scoring rules, maximin, Copeland" for every α ∈ [0, 1], simplified Bucklin voting rules, etc. We then show that introducing a parameter per pair of alternatives which specifies the minimum number of partial preferences where this pair of alternatives must be comparable makes the related computational task of manipulative elicitation N P-complete for all common voting rules including a class of scoring rules which includes the plurality, k-approval, k-veto, veto, and Borda voting rules, maximin, Copeland~α for every α ∈ [0, 1], and simplified Bucklin voting rules. Hence, in this work, we discover a fundamental vulnerability in using minimax regret based approach in partial preferential setting and propose a novel way to tackle it.
机译:LU和Boutier(2011)提出了一种基于“Minimax遗憾”的新方法,以在偏好可以是所有部分(而不是完整的)订单中的设置中基于基于古典得分的投票规则。我们在这里展示了这种方法容易受到一种新的操作,这些操作在经典(偏好是完整订单)的投票世界中并不存在。我们称之为攻击“操纵诱因”。 More specifically, it may be possible to (partially) elicit the preferences of the agents in a way that makes some distinguished alternative win the election who may not be a winner if we elicit every preference completely.更令人惊讶地表明,相关的计算任务是多项式时间可解决大类投票规则,包括所有评分规则,Maximin,Copeland“为每一个α∈[0,1],简化的Bucklin投票规则等显示每对替代方案引入参数,该备选方案指定这对替代方案必须可比的最小数量的部分偏好,使得操纵诱导的相关计算任务对于所有常见的投票规则,包括一类包括的评分规则多元,K批准,K-veto,veto和Borda投票规则,Maximin,Copeland〜α为每个α∈[0,1]和简化的Bucklin投票规则。因此,在这项工作中,我们发现了一个基本漏洞在使用基于Minimax的后悔的方法中,在部分优先设置中提出一种解决方案的新方法。

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