基于VCG的机构逼近的放大硬度
Amplified Hardness of Approximation for VCG-Based Mechanisms
摘要：
如果两个玩家的社会福利最大化问题不承认PTAS，我们证明，任何最大范围内的真实机制，运行在多项式时间不能达到一个近似因子优于1/2。此外，对于同一问题的k人版本，在相同的两人难度假设下，近似的难度提高到1/k。(We注意，1/k可通过平凡的确定性范围内最大值机制来实现。这个硬度的结果不仅包括确定性的最大范围内的机制，但也普遍真实的随机最大范围内的算法，以及一类严格的更强大的真实的期望随机机制最近介绍了Dobzinski和Dughmi。我们的结果适用于任何一类的赋值函数，满足一些极小闭包性质。这些性质都满足所有研究得很好的APX-硬社会福利最大化问题的估值函数，如覆盖，次模，次加估值。我们还证明了普遍真实的最大范围内的机制更强的结果。也就是说，即使对于允许FPTAS的预算加法估值类，也没有这样的机制可以在多项式时间内实现优于1/k的近似因子。
英文摘要：
If a two-player social welfare maximization problem does not admit a PTAS, we prove that any maximal-in-range truthful mechanism that runs in polynomial time cannot achieve an approximation factor better than 1/2. Moreover, for the k-player version of the same problem, the hardness of approximation improves to 1/k under the same two-player hardness assumption. (We note that 1/k is achievable by a trivial deterministic maximal-in-range mechanism.) This hardness result encompasses not only deterministic maximal-in-range mechanisms, but also all universally-truthful randomized maximal in range algorithms, as well as a class of strictly more powerful truthful-in-expectation randomized mechanisms recently introduced by Dobzinski and Dughmi. Our result applies to any class of valuation functions that satisfies some minimal closure properties. These properties are satisfied by the valuation functions in all well-studied APX-hard social welfare maximization problems, such as coverage, submodular, and subadditive valuations. We also prove a stronger result for universally-truthful maximal-in-range mechanisms. Namely, even for the class of budgeted additive valuations, which admits an FPTAS, no such mechanism can achieve an approximation factor better than 1/k in polynomial time.
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