计算复杂性是操纵的障碍吗？
Is Computational Complexity a Barrier to Manipulation?
摘要：
当代理人一起行动时，他们可能需要一个简单的机制来决定联合行动。一种可能性是让智能体以投票的形式表达他们的偏好，并使用投票规则来决定获胜的动作。不幸的是，代理人可能试图通过误报他们的偏好来操纵这样的选举。幸运的是，已经证明了如何操纵许多不同的投票规则是NP难的。然而，NP-困难仅限制了最坏情况的复杂性。最近的理论结果表明，操纵在实践中往往是容易的。为了解决这个问题，我建议从经验上研究计算复杂性在实践中是否是操纵的障碍。在我的调查中使用的基本工具是计算“相变”的识别。这种方法在识别命题可满足性和其他NP难问题的困难实例方面卓有成效。我表明，相变行为使洞察操纵投票规则的硬度，越来越多的关注，计算复杂性确实是任何形式的障碍。最后，我看看其他相关问题的计算操作问题，如稳定婚姻和锦标赛问题。
英文摘要：
When agents are acting together, they may need a simple mechanism to decide on joint actions. One possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action(s). Unfortunately, agents may try to manipulate such an election by misreporting their preferences. Fortunately, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. To address this issue, I suggest studying empirically if computational complexity is in practice a barrier to manipulation. The basic tool used in my investigations is the identification of computational "phase transitions". Such an approach has been fruitful in identifying hard instances of propositional satisfiability and other NP-hard problems. I show that phase transition behaviour gives insight into the hardness of manipulating voting rules, increasing concern that computational complexity is indeed any sort of barrier. Finally, I look at the problem of computing manipulation of other, related problems like stable marriage and tournament problems.
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