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Automated Mechanism Design for a Self-Interested Designer

机译:自动机制设计,为自身兴趣设计师

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Often, an outcome must be chosen on the basis of the preferences reported by a group of agents. The key difficulty is that the agents may report their preferences insincerely to make the chosen outcome more favorable to themselves. Mechanism design is the art of designing the rules of the game so that the agents are motivated to report their preferences truthfully, and a desirable outcome is chosen. We recently proposed an approach―called automated mechanism design―where a mechanism is computed for the preference aggregation setting at hand. This has several advantages, but the downside is that the mechanism design optimization problem needs to be solved anew each time. Unlike the earlier work on automated mechanism design that studied a benevolent designer, in this paper we study automated mechanism design problems where the designer is self-interested. In this case, the center cares only about which outcome is chosen and what payments are made to it. The reason that the agents' preferences are relevant is that the center is constrained to making each agent at least as well off as the agent would have been had it not participated in the mechanism. In this setting, we show that designing optimal deterministic mechanisms is NP-complete in two important special cases: when the center is interested only in the payments made to it, and when payments are not possible and the center is interested only in the outcome chosen. We then show how allowing for randomization in the mechanism makes problems in this setting computationally easy.
机译:通常,必须根据一组药剂报告的偏好选择结果。关键难度是,代理商可以报告他们的偏好,不确定,使所选择的结果更加有利于自己。机制设计是设计游戏规则的艺术,使得代理是为了真实地报告它们的偏好,并且选择了理想的结果。我们最近提出了一种被称为自动化机制设计的方法 - 用于手头的偏好聚合设置来计算机制。这有几个优点,但下行是机制设计优化问题每次都需要重新解决。与早期的自动化机制设计的工作不同,研究了仁慈设计师,在本文中,我们研究了设计师自私的自动机制设计问题。在这种情况下,中心仅关心选择哪些结果以及对其进行的付款。代理商的偏好是相关的原因是该中心被限制为使每个特工至少也是由于代理商未参与该机制。在这个设置中,我们表明,设计最佳确定性机制是在两个重要特殊情况下的NP完成:当中心仅涉及到它的付款时,并且当不可能的付款时,该中心仅​​在所选的结果中感兴趣。然后,我们展示了如何允许在机制中随机化的如何在这种情况下计算出来的问题。

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